Shownotes and transcript

Summary?

Matt and Jasmine debate whether generative AI search represents a branding opportunity or a performance marketing channel for life science companies. The conversation unpacks the mechanics of AI discoverability, examining conversion data (23x better than organic search), volume reality (still <1% of total traffic), and the strategic implications for B2B life science marketers.

Key tensions explored: incumbents vs newcomers, content quality vs volume, brand visibility vs measurable outcomes, and the 3-6 month timeline for GEO results. The discussion reveals how query fan-out, training data dynamics, and platform-specific biases (Grok over-indexes X, ChatGPT favours Reddit) create a probabilistic rather than deterministic visibility model.

Core argument: AI search isn't an extension of SEO - it's a different game rewarding structured, answer-ready content over traditional optimisation tactics. Small players can compete through specificity and recency, but incumbents hold trust signal density from years of indexed content.

Actionable close: Test your visibility quarterly across ChatGPT and Perplexity with category questions and specific use cases. The gap between results maps your competitive position in AI discovery.

Transcript

Jasmine (00:02): Hello, now.

Matt (00:03): Hey Jasmine, how you doing?

Jasmine (00:05): I'm doing really well. What about yourself? Yeah, yeah. Yeah, me too. Getting ready for Boston and the cold weather. So today, we're gonna talk about a story that resonated with both of us. Right now, while you're reading this, an AI is deciding whether your brand exists, not in a philosophical sense, in a practical commercial one. A life science researcher is asking ChatGPT or their favourite LLM for recommendations on flow cytometry systems. Another is using Perplexity to compare qPCR platforms. Your product might be brilliant, your content might be comprehensive, but if AI can't find you, cite you, and recommend you, you're invisible.

Matt (00:07): I'm good, thank you. Looking forward to this one today.

Jasmine (00:34): For the first time since Google's early days, we're watching the rules of discoverability get rewritten in real time. And most life science marketers haven't noticed yet. The article that you put together maps what we know about the current discoverability landscape in detail. It covers the mechanics of query fan out, which is how a single user question spawns dozens of synthetic queries behind the scenes. The economics of AI crawling versus referral, training bots take vastly more than they give back. And you also talk about the tactical shifts required to remain visible. The core argument is that AI search is not an extension of traditional SEO, as one of our friends, Sarah Stahl says, it's a different game. It operates on fundamentally different logic, rewards different content structures, and creates a probabilistic rather than a deterministic visibility model. The piece draws on a range of sources, including a discussion between Rand Fishkin, the founder of SparkToro, and Mike King of iPullRank. So Matt, why should life science companies care? Yeah, please go for it.

Matt (02:32): Well, the commercial stakes are real, but they're really unevenly distributed. So AI search traffic converts at extraordinary rates, about 23 times better than organic search. And that's according to Ahrefs data. But those people landing on your website from AI search currently accounts for less than 1% of the total website traffic in general. So the gap between the quality signal and the volume reality is the central tension for any marketing team deciding where to invest right now. At the moment, Google is still driving the vast majority of traffic to websites, but that doesn't mean you can really ignore what's going on in AI because very often people aren't actually going to websites until much, much later in their buying journey anyway. It means that people are staying within the chatbot interface for longer. They're creating their own buying guides and fundamentally changing the truths that digital marketers have clung onto over the last decade or so.

Jasmine (03:41): Yeah, so I found this really really interesting, especially from a performance marketing perspective. The B2B life science context completely changes the maths perspective. When a single deal is worth six or seven figures, you don't need volume. You need qualified buyers and that's exactly what AI search delivers because they've done their homework through their favourite LLM. They've done the compare and contrast. They may use different models within their LLM like deep research to really go into the details and they ask their LLM if the LLM doesn't even offer it on its own, what it would recommend as a short list of what to buy.

Matt (04:40): Yeah, it's an interesting point and you know, I think stats from Forrester say that 90% of all B2B purchases currently use some form of generative AI to either do deep research or to compare and contrast information along that buying journey. I think it's really, you know, it's a really important place to be visible. But I would argue that it's not a performance channel, but it's more of one for branding. And that's sort of what Mike King says in his interview with Rand Fishkin. You know, and he was the 2025 Search Marketer of the Year, and he's done a lot of client work and a lot of research. I would contend that these platforms are more branding channels than they are performance. Your return on investment is going to be reasonably low at least right now. I wouldn't dismiss AI search as a way for actually being important. In fact, I think it's vitally important that our brands are visible in AI search. I just think that it doesn't fit within the performance marketing bracket.

Jasmine (05:51): You know, it's interesting because I've heard Sandy Carter, who's recently taken over the Marketing Companion podcast from Mark Schaefer, say that there is evidence that the traffic from GPT converts at a much higher rate than traditional. And, you know, even King's own client data contradicts the branding only thesis. Financial services case study in his article shows a 121% increase in signups and almost 53% increase in organic search traffic from AI search optimisation work. Those are performance metrics, real signups, real traffic. The iPullRank team isn't running a PR campaign as part of that. They're engineering visibility in a retrievable system and the business outcomes are measurable.

Matt (07:00): Look, those stats are very, very impressive. The challenge is that the volume reality hasn't yet changed yet. Google still drives 345 times more traffic than all AI platforms combined. You know, the 23 times conversion stat, you know, it's website data. It makes a lot of sense. And I think it goes to show how important it is to be visible. But because we're not able to go out and look at keywords, this works out a very different way of being visible. Really what you're trying to do is make sure that your brand turns up in the same conversation as the search terms, the questions that your customers are going to be asking. So for me, I still think it sits more in the brand visibility bucket. But then I might be the sort of person that will put a trade show as well in both buckets. You know, it's both brand visibility and branding is incredibly important at a trade show, but it's also important as a performance. And so maybe there's that case where it's a brand performance channel rather than purely either one or the other.

Jasmine (08:24): Yeah, it's an interesting way to look at it because, you know, is that grey line between the two very, very muddled, if you will. You know, if you think about what a lot of these LLMs are doing, they're making recommendations. So to me, that's a conversion metric. Once you're jumping into that recommendation bucket and the inflection point from the branding signal to the performance channel is a question maybe of when not whether, and by the time it's obvious, the early mover advantage is gone.

Matt (09:11): Yeah, and I think one of the really important things right now in my mind at least and this is unproven is that being able to be found in AI search, the way the bots work, there is an argument to say that if you're being found in AI search, you're more likely to end up in the training data for the next model. And if you're there and those probabilistic connections are being made between your brand and something else right now, the more that you turn up in that data, the more likely you are to be the brand of choice for any particular query in the future. And I think that it's really about making sure that content is structured well. And I mean, I've spent quite a bit of time thinking about this and actually playing with the tools and trying to understand it. A well-structured, you know, piece of content is likely to perform better. And then I think smaller players can really hold their own against incumbents because it is much less about the amount of content and much more about the quality and the timeliness and whether or not it's answer-ready.

Jasmine (10:41): I really, I really love this argument because I feel like as a marketer, you know, I've always looked for opportunities for the little guy to compete against the big company. And if we can get teams to understand that AI friendliness rewards structure and specificity and they can go out and find these long tail opportunities to speak to very specific use cases or very specific workflows or pain points within their product categories, this can be a great way to level the playing field with just a bit of extra time and a bit of thoughtful content. You know, so, and that means that sort of startups or smaller budgets can actually compete where they maybe couldn't before in sort of, you know, contested and contentious sectors. So for me, content quality seems to matter more than budget in AI discovery and a focused application guide, you know, well orchestrated, well marked up on the website, targeting specific job to be done, specific questions, can really outperform generic overviews from much larger competitors. Often when AI systems can't even get into those, can't make sense of the offer of those companies. And I think it's particularly helpful because AI systems really reward specificity and structured answer ready content over volume. And we know that so many web projects within large organisations really can't move quickly in big orgs.

Jasmine (16:35): Fair enough. At the same time, you know, there is a bit of first mover advantage, a bit of an incumbent advantage. You know, AI systems clearly learn from what already exists on the web. Incumbents to a particular category, you know, for example, liquid handlers have years of forum citations, review mentions, technical comparisons, indexed documentation that has been absorbed by your favourite LLM. This isn't just content volume. These are trust signal densities and AI systems weigh it heavily when assembling recommendations.

Matt (17:27): That is true, but I think it neglects the recency bias. And so we know that AI search does prefer more recently updated content. And we know so many big organisations, they put pages live and then they're forgotten about and they exist until the next big web refresh. And maybe even aren't even rewritten as part of that. They just moved over to the next platform. So the barrier for entry for GEO is lower than for almost any other discovery channel. And you don't need massive ad spend or a PR team. You just need to do a lot of well-structured content that answers the exact question a researcher is prompting. And really being able to make sure that you're answering those questions and all of their permutations in the right way.

Jasmine (18:16): Without a doubt that answering those questions in the right way, in an AI friendly way and getting your persona AIs to weigh in on what those friendly ways would be is super important. At the same time, GEO optimisation, we believe it takes roughly three to six months to show results. So for a niche player entering a crowded category, that runway assumes you have the resources and patience to wait. Most life science teams launching a new tool don't have that luxury more often than not.

Matt (19:04): And that's fair. I think it also potentially changes the way that we think about product launch. Because whilst AIs do love that recency of content, we know that we need to be making sure that we're getting the right signals to them and we've got to build that, you know, make sure that our content is being spread through the networks that they search and they want to crawl. But I think it does maybe mean that we have to start thinking a little bit earlier. How do we start making sure that we're not tipping our hand, but we're answering questions about the topics, even if we're not giving information about the product itself. That for me still feels like it's a, you know, we're in this wild west of AI search discovery at the moment where, you know, the rules are changing all the time. They're not fixed, you know, even though I spent a lot of time trying to understand what was going on in the space, there's still conflicting information out there. And so I think that smaller players that earn AI recommendations can, you know, usually doing so because they've got higher quality visitors and they've got higher quality content and that leads to higher quality visitors.

Jasmine (20:16): Yeah, I think you've got a point here and this is something I mentioned in my blog post that AI friendliness and timing is butting up against product confidentiality in a new product development process. And I think this behooves the heads of product management to really think a little bit outside the box and build new workflows to be able to leak some information earlier on to help with this sort of three to six month adoption time for the GEO. So, you know, maybe you have your product managers on ResearchGate, on Reddit, and they're replying to technical questions of an area that they're not really well known for yet. So it's sort of an easy early teaser for a new product that's about to be launched in a new category. That's just one example of how they may want to get ahead of the curve in order to play to the game a bit.

Matt (21:44): Yeah, and that makes sense, although it can be hard for larger organisations to allow people to go to their social media sites and take those actions, which I think plays into the hands of it being a bit of a leveller. But I think that there's a real point that's definitely worth acknowledging that incumbents do have lots of index content and typically have bigger forum presence. They've got teams that they could and can deploy to work on that stuff. But it's worth knowing that index content and AI recommended content aren't the same thing. And volume of existing content doesn't necessarily translate into quality of recommendation. But of course, the more that you're there, the more likely you are to be part of that question, you know, to have answered a question. So I think that there's probably a safe middle ground here, but it really is difficult to know exactly how things are going to change. And we're still having to experiment to understand these probabilistic machines. And even within that statement, we know already that Grok and ChatGPT and Claude and Gemini and Perplexity, they all answer questions in different ways. Their search algorithms all work off different probabilistic mechanisms. They all have access to different data. So for example, Grok will over, you know, it's trained on an awful lot of information from X and Grokopedia. But also you've got ChatGPT that has historically always overrepresented Reddit data because of the licensing deal that OpenAI did with Reddit to get access to their data. So again, it becomes down to a bit of a training data and access challenge as to where different tools actually have access to and which bots are being blocked on which platforms.

Jasmine (23:51): So I think this sort of lends itself to product managers, marketing managers, rethinking maybe one of the tasks in their workflow. And let's say once every couple of months, spend an hour prompting ChatGPT and Perplexity just to pick two random LLMs with two queries. The first, the category question your product answers, and then the specific use case it targets. And see who shows up in each of these prompts and each of these queries. And that gap between these two results is your competitive map for AI discovery and it costs nothing except a bit of time, but it can help you stay ahead of how you want to tweak your content on your already launched products and how you might want to expand your content as you're getting ready to launch a new product.

Matt (25:02): And I think that's great advice for us to finish on. I think we really have to be considering AI search, not just as another channel, but also treating the bots as new members of the decision-making group, new influences that we have to be marketing to as well. And that's going to be a mindset shift that it's going to take time for a lot of people and especially because those bots are changing all the time.

Jasmine (25:31): The bots are changing all the time and the bots are becoming the ultimate influencer for your customers, even in the sciences. Yeah, absolutely. Thanks, Matt. This, as always, has been a great discussion. Absolutely. Bye for now.

Matt (25:42): And that's definitely something we can agree on.

Matt (25:50): That was a lot of fun. Look forward to the next one. Thanks, bye.

Q&A: AI Search Strategy for Life Science Marketing

Q: What is the core difference between traditional SEO and AI search optimisation?

A: AI search operates on fundamentally different logic. Traditional SEO is deterministic - you optimise for keywords and track rankings. AI search is probabilistic. It rewards structured, answer-ready content over keyword density. A single user question spawns dozens of synthetic queries behind the scenes (query fan-out), and visibility depends on whether your content matches those probabilistic connections, not just keyword placement.

Q: Is AI search a branding channel or a performance marketing channel?

A: It's both, and the debate matters. AI search traffic converts 23 times better than organic search (Ahrefs data), but represents less than 1% of total website traffic. For B2B life science, where single deals are six or seven figures, you don't need volume - you need qualified buyers. The question isn't whether AI search delivers performance, it's when the inflection point from brand signal to performance channel becomes obvious. By then, early mover advantage is gone.

Q: Do incumbents have an unfair advantage in AI discovery?

A: Yes and no. Incumbents have years of forum citations, review mentions, technical comparisons, and indexed documentation absorbed by LLMs. These are trust signal densities that AI systems weigh heavily. However, indexed content and AI-recommended content aren't the same thing. AI search prefers recency, structure, and specificity over volume. Large organisations often let pages go stale between web refreshes. Smaller players can compete through well-structured application guides targeting specific jobs to be done.

Q: How long does GEO take to show results?

A: Roughly three to six months. This timeline creates tension with product confidentiality in new product development. You need content live before launch to benefit from GEO during launch windows. The workaround: product managers engaging on ResearchGate or Reddit, answering technical questions in adjacent areas before product announcement - seeding the category without tipping your hand.

Q: What's the practical testing framework for AI visibility?

A: Quarterly, spend an hour prompting ChatGPT and Perplexity with two queries: (1) the category question your product answers, and (2) the specific use case it targets. See who shows up in each. The gap between these two results maps your competitive position in AI discovery. It costs nothing except time and reveals where you need to strengthen content.

Q: Do different AI platforms favour different content?

A: Absolutely. Grok over-indexes content from X and Grokopedia. ChatGPT historically overrepresents Reddit data due to OpenAI's licensing deal. Claude, Gemini, and Perplexity each have different search algorithms, probabilistic mechanisms, and training data access. Your visibility varies platform by platform. Test across multiple systems, not just one.

Q: What does "treating bots as new members of the decision-making group" mean?

A: It means recognising that AI systems aren't just search tools - they're influencers. Ninety per cent of B2B purchases use generative AI for research or comparison (Forrester). Buyers are staying inside chatbot interfaces longer, creating their own buying guides, and arriving at your website much later in the journey. You're no longer just marketing to humans. You're marketing to the probabilistic systems that shape their shortlists before you ever see them.

Q: Does being found in AI search now affect future model training?

A: Unproven but plausible. If you're being found in current AI search, you're more likely to end up in training data for the next model generation. Those probabilistic connections between your brand and category questions compound over time. The more you appear in training data, the stronger those connections become. This is speculative but suggests early investment in AI visibility has compounding returns beyond immediate traffic.

Shownotes and transcript

Shownotes

Most life science teams show up to conferences with product specs and TAM models built on market size. Both fail for the same reason: they prove something exists without proving you can win.

This episode is for product managers and marketers preparing for their next conference or portfolio review. Matt Wilkinson and Jasmine Gruia-Gray unpack data from the ELRIG Drug Discovery 2025 exhibitor survey and dissect why competitive revenue triangulation without capture strategy kills viable products.

Preparation without presence fails at conferences, and TAM without capture strategy fails in portfolio reviews.

What you will learn:

• Why 86% of exhibitors lack battle cards and how AI collapses the preparation gap from days to minutes
• The authenticity risk when AI prep becomes a script instead of a launchpad for listening
• Why leading with TAM size gets you stumped by "why would anyone switch?" in portfolio reviews
• How pain point differentiation prevents conservative TAM analysis from killing $60 million opportunities
• The three executive objections every product manager must answer: unique pain solved, economic switching benefit, and beachhead proof

Keywords: life science marketing, conference preparation, battle cards, AI-assisted prep, TAM analysis, market sizing, pain point differentiation, beachhead strategy, portfolio review, ELRIG survey, competitive intelligence, customer discovery

Subscribe to Strivenn Thinking for weekly insights on AI-enabled life science marketing. Visit strivenn.com for frameworks, tools, and strategic resources.

Transcript

Matt Wilkinson and Jasmine Gruia-Gray discuss two critical gaps in life science commercialisation: conference preparation and TAM analysis. Drawing from ELRIG Drug Discovery 2025 survey data and real product portfolio examples, they explore how AI accelerates preparation while authenticity requires listening, and why market sizing without capture strategy kills viable products.

Conference Preparation Gaps and AI-Assisted Battle Cards

Jasmine [00:42]

Hey, Matt. Great. How are you?

Matt Wilkinson [00:44]

Hey Jasmine, how you doing?

Matt Wilkinson [00:48]

I'm great, thank you.

Matt Wilkinson [00:49]

Yeah, really excited to be covering these two stories today.

Jasmine [00:53]

Yeah, me as well. So let's kick it off.

Jasmine [00:56]

So on January 20th, we did a thing. That thing is we ran a webinar with Sanj Kumar, the CEO of Elrig, and unpacked what the data from our survey of exhibitors at Elrig Drug Discovery 2025, a festival of life science means and what teams can do about it before their next exhibit at a conference.

Jasmine [01:24]

The survey exposed a preparation gap that runs deeper than missing battle cards. Nearly half of the exhibitors default to the product-focused booth messaging, describing features and specs rather than outcomes and pain points. Meanwhile, the tactics that actually drive post-event action are expert consultations, 44%, and live demos at 30%, both of which require preparation that most teams haven't done. The webinar introduced the STAR or S-T-A-R framework for battle card creation, proposed AI assisted prep as a shortcut to a first draft, which I often do, I know you do as well, and made the case for putting a marketer back in the booth for a real-time intelligence gathering. The commercial stakes are straightforward, conference budgets are significant, and the ROI calculation depends entirely on what happens before and during the event, not on badge scans or footfalls. The preparation gap isn't a skills problem, it's a prioritization one. So Matt, where are you landing on this?

Matt Wilkinson [02:49]

So I, you know, when we look at the levels of AI adoption, and sort of some of these commercial challenges that we see across teams, I really feel that one of the biggest, you know, one of the biggest opportunities that that life science tools and life science, you know, service companies really have is they've got the ability to use AI to overcome that preparation gap and also to help them with the prioritization.

Matt Wilkinson [03:19]

That 86% figure of people not really having battle cards, it's a preparation access problem. It's not a capability problem at all. Most teams know they should have battle cards somewhere. They don't build them because the blank page problem is real. Competitive research is slow. Positioning takes consensus. And the deadline is always the travel day. And often people have got so many competing priorities that they're really only making sure that they turn up and they prep once they're there.

Matt Wilkinson [03:52]

You know, AI collapses the activation barrier for trying to get to a good enough answer from maybe days and hours of meetings down to just minutes. The prompt framework that we actually provide in the article, which can be found in our Striven Thinking section on the website, provides a prompt framework that produces a structurally sound first draft in a single pass. It's not perfect, it never will be, but the more, but the better the information that you provide, the better that it's going to be. But it really does give you a, an architecture that a marketing team wouldn't, you know, normally spend days assembling and you can get done in minutes. So I really feel that that makes the argument, you know, much, much stronger for adopting AI. How about you, Jasmine?

The Authenticity Challenge

Jasmine [04:44]

So I see it slightly differently. I worry that with adopting AI, you run the risk of losing the humane side of things, the authenticity side of things. AI is great at optimization. It's great for polishing. But that sort of real world lived experience, I worry will get diluted out with AI. In the webinar, you gave this great example when you were working on a project with Artel and you did this, the pipetting Olympics. I thought that was brilliant. It was a great competitor countermeasure, if you will. And it was purely human, purely authentic. It was a great opportunity to listen to what people were saying as they were going through the Olympics.

Matt Wilkinson [05:48]

Yeah, and I think that the challenge here, I understand where you're coming from with that concern about sort of diluting the authenticity and the humanness of our messaging. But I think that if we turn it around and say, hey, look, here's a lot of competitor information off of a website. If I'm going go into in and I'm unprepared, then I've got no idea how to, you know, our offers align with against somebody else's. But if I've gone in and I've done that research and I've got AI to help me to, to, to distill it down, then at the very least I know what I'm up against. And I think that then that allows you to have a much sounder foundation upon which you can then build that humaneness and really focus in on paying attention to the human that's in front of you.

Matt Wilkinson [06:45]

And then taking notes to understand how do we actually go to make sure that that that conversation wasn't just a nice conversation, but it was meaningful to both of us. And that meaning lasts beyond just the two or three minutes that we're at the booth together, but that actually allows us to continue the conversation afterwards.

Jasmine [07:05]

Yeah, I guess where I'm I'm concerned is the difference between preparation and using AI as a crutch and that's where you lose your authenticity. People stop listening to what the person in front of them is saying because they've prepared their battle card. It's all in front of them. It's ingrained in them. And they don't really take the time to probe a little deeper with the person in front of them. They just sort of go directly into that AI prepared spiel.

Matt Wilkinson [07:46]

That could be exactly the same as it's saying that going into a an exit X, you know, going into an exhibition and having been given scripts for the booth. This is what I have to say about this. Or what so many people do is to default to talking about the technical specifications that may or may not make their, you know, their product or service different. So I think it's, you know, it's a valid concern, but I think it's, that's, that focuses more on booth training and how to make sure that anybody in a commercial team, when they're dealing with customers is spending twice, at least twice as much time listening to their customers and the people they're speaking to as they are speaking.

Matt Wilkinson [08:29]

You know, after all, there is that famous saying that we were given two ears and one mouth for a good reason. And I think that holds especially true in any conversation that we're having with prospects and customers.

Jasmine [08:44]

Yeah, I think that that's fair that listening skills should be sharpened. I think that using the word clarification is super important to help you get a little bit deeper understanding on the pain points on what the person in front of you likes about the product they're already using or dislikes about the product they're already using to help them then connect the dots between what they heard and what preparation they did with AI. I think that's where it becomes a great hand in glove situation.

Matt Wilkinson [09:28]

And I think that that's something that we can agree on here is that we're using AI to, you know, to optimize to really to be able to get to the point where we've got a really strong foundation so that we can then spend time really focusing on what the customers are saying. And I don't think that we should be looking at AI as a band-aid that fixes every problem. I think we need to be looking at how do we make sure that our customers and our prospects get the most out of those interactions. And a lot of that is by giving them the respect of understanding that they have, you know, they have other options out there and it's our job not just to understand our portfolio, but to understand the context in which that portfolio sits. So that actually we might be able to recommend that a competitor's offer is better for a particular challenge. Now, hopefully our offer is better for across a whole range of things so that we have the obvious choice to go to. But we actually need to be upfront about saying this is actually, that's actually where, you know, competitor A is actually going to beat us. You you don't want to switch because of these things. And I think that's really important because there's nothing, you know, in this age of AI, there is nothing worse than bad reviews online. And so we really don't want to be pushing, you know, trying to sell people on false promises. I think that's one of the things that sort of AI search and the ability to build your own buying guides using AI is really going to bring to the forefront.

Jasmine [11:04]

Yeah, maybe we need to create a new word here, some mashup between authenticity and accelerator and acceleronicity or something. But the word I really like here is Launchpad. I think that if you consider AI for prep work as that launchpad into the conversation, you really can't go wrong. Preparation gap is real and absolutely AI can help to close it. But the authenticity gap opens that moment where the card becomes the script rather than the starting point. So it's that combination of using AI for prep, but then using your listening skills and the clarification language to get beyond that script.

Matt Wilkinson [12:00]

And I think that's a really nice way to to wrap that piece up.

TAM Analysis: Market Size vs Market Opportunity

Matt Wilkinson [12:04]

Next up, we have a an article that you wrote about on a total available market and how most teams are confusing market size with market opportunity. And this is something that's really interesting and probably a mistake I've made many times before realizing it. But, you know, in that article, you say that many companies present competitive revenue proving a, shall we say, a $50 million market exists. And then somebody will say, well, why would anyone switch to us? And all of a sudden your total available market suddenly means nothing. You know, if a competitor sold 2000 QPCR kits at 850 USD each, that would turn into a 1.7 million USD in real revenue. So we know the market exists. But if you target the same pain points with incremental improvements, you're fighting for a share and an established category. The team that identified a 15 million USD segment with acute unmet needs gets funded because they answer three questions. What pain do we solve that the competition ignores? What economic benefit justify switching and which beach head has pain acute enough to drive adoption?

Matt Wilkinson [13:30]

Executives fund differentiated value and defensible segments, not market size without capture strategy. So I'm really curious about this. Jasmine, why do most product managers walk into portfolio reviews with, know, TAM models that prove market size, but can't answer the capture question? And what does that tell us about how we're teaching market sizing?

Jasmine [13:56]

Yeah. I think this is a really important question. What I found happens most often is that we teach the product managers to build the town models using bottom up. No problem. Lab counts, publication trends, competitor revenue triangulation, all good. They get that really good. They get really good at those mechanics, filtering to relevant labs, tracking methodology, adoption, estimation, competitor unit sales, all that. Then they walk in to portfolio reviews and present the addressable market is let's say $50 million and growing at 12% annually. And they get stumped when the exec says, why will anyone switch from their current solution to ours? Fair question. And often the product manager hasn't prepared for that kind of debate. And the model isn't isn't created in such a way where they can point to a part of the model and say, well, here are the numbers that I factored in to address that question.

Jasmine [15:07]

The gap is that market sizing became a forecasting exercise. And I've done that personally myself, and I've seen lots of product managers do that instead of a capture strategy. What I mean is we're training product managers to prove the market exists not prove they can win meaningfully in the market and win meaningful share in those markets. That's ultimately what it's all about. And the fix really starts with reframing what TAM analysis is for. It's not predicting revenue. It's building the evidence base that answers three executive objections. What pain do we uniquely solve? What economic benefit justifies switching and which beachhead proves both. Competitive revenue shows the market is real, all good, but the pain point differentiation shows you can capture it. And that ends up being what's most important and what the executive wants to hear.

Matt Wilkinson [16:15]

From a practical perspective, are you able to walk us through how pain point differentiation prevents you know, the flow cytometry reagent problem, as you call it, where, you know, conservative TAM analysis kills a viable product that actually had a 60 million USD market?

Jasmine [16:34]

So the flow cytometry example with flow cytometry agents got killed because the product manager led with the conservative town, town. Let's say 20, $25 million. You know, if you find minute exact listening to that, to me that says not really worth it. Especially if you compare to other projects that were approved at the 50 million or higher town. The actual market turned out to be $60 million. The product was viable, but the analysis failed. The pain point differentiation prevents this. Instead of leading with the total market size, lead with the beachhead intensity. Translational research cores running a 20 plus samples a day face a bottleneck. Current reagents require four hours hands-on time. Hours reduces that to 45 minutes. Whoa, that's a significant differentiation, delivering $12,000 annual cost savings per lab. Now you're speaking money, but you're also speaking time. And those are things that core facilities do understand. You're not asking execs to bet on your forecast. You're showing the acute pain, quantified benefit and a problem competitors ignore. The TAM becomes supporting evidence. Our beachhead becomes the core message.

Jasmine [18:12]

And at 30% penetration for $8,000 at annual average sales price, that's a $4.3 million year one revenue. Adjacent segments expand to 25 million plus, but we're funding beachhead domination that proves expansion. So it's basically flipping the story on its head where you're starting with the differentiation, you're starting with the beachhead and the pain point and ending with the total available market.

Matt Wilkinson [18:45]

That's such an important shift. So in terms of sort of product managers, what shifts when they stop treating market sizing as a forecasting exercise and start treating it as an objection handling framework for portfolio review?

From Forecasting to Objection Handling

Jasmine [19:04]

Yeah, so here's what changes. Instead of building a TAM model projecting revenue three years out, you build a defense answering questions you know are coming. The old approach, for example, is based on lab counts and adoption curves. Will capture 15% share by year three, generating seven and a half million dollars. The exact asks, what if adoption is half that? You don't have an answer in this old scenario. In the new approach, you walk in with evidence for three objections. Why will customers switch? The proof research cores run 20 plus examples samples, lose four hours daily, we cut that to 45 minutes, delivering $12,000 annual savings, validated with eight sites. That's bringing your voice of the customer and actual ethnographic research right into the town discussion. Is the market real? Competitors sold 2,000 units at $850 each, proving $1.7 million revenue. Why this beachhead? These 180 labs have most acute pain. They become references for pharma. You're not predicting what will happen. You're proving what behavior you've observed through your voice of the customer and which segment responds strongest.

Matt Wilkinson [20:36]

That's really interesting. So what you're telling me is that market sizing isn't about predicting the future, but about building evidence that answers, you know, those three questions that executives always ask, what pain do you solve that competitors ignore? What economic benefit justifies the switch and which beachhead segment proves it? Curious to sort of hear how you'd summarize it.

Jasmine [21:00]

Yes, those are the three questions that every product manager needs to prepare for in this TAM discussion. The productive tension between competitive revenue triangulation and pain point differentiation never fully resolves. You need both. But when you walk into portfolio reviews, lead with that story that proves you can capture meaningful share in a focus segment, then expand from strength, right? That voice of customer strength is irreplaceable. And it comes back to what we were talking about at the beginning with conferences, those listening skills and the challenging skills and clarification skills are so important. And you can bring all of that back in the story when you talk about TAM. Start with one beachhead where customer pain is acute enough to drive adoption, build your TAM defense around that intensity, not the total market size.

Matt Wilkinson [22:05]

Well, thank you so much. I've certainly learned a lot about, you know, how to avoid that challenge in future and I hope our listeners have as well. Thank you for, thank you for a great article, Jasmine. Really enjoyed it.

Jasmine [22:17]

Thank you, Matt. Looking forward to our next discussion soon.

Matt Wilkinson [22:21]

Me too.

Q&A

We have a conference in three weeks and no battle cards. Can AI actually help us prepare in time?

Yes, but only if you treat AI as a launchpad, not a finish line. Use the STAR framework prompt to generate first drafts in minutes, covering situation, talking points, anticipated objections, and references. Validate the output against real customer conversations you've had. Schedule two 90-minute sessions: one to generate and refine cards, one to role-play booth conversations focusing on listening and clarification skills. AI collapses preparation time from days to hours, but authenticity still requires human practice.

How do I prevent my team from just reading AI-generated battle cards like scripts at our booth?

Train booth staff to use the 2:1 listening rule - spend twice as much time listening as speaking. Open every conversation with a clarification question: "What brought you to our booth today?" or "What challenge are you trying to solve?" Use the battle card to inform your responses, not script them. Role-play scenarios where the prospect's pain doesn't match your prepared content. Debrief daily on what you heard versus what you expected. The card provides structure; your presence provides value.

Our last TAM analysis led with $50 million market size and got rejected. How do I reframe for the next portfolio review?

Flip your story structure. Start with beachhead intensity, not total market. Identify the segment with most acute pain - translational cores, specific therapy areas, high-throughput labs. Quantify their bottleneck in time and cost. Show how your solution delivers measurable economic benefit validated with real sites. Present competitive revenue as proof the market exists, then explain why your differentiated pain point lets you capture meaningful share. Lead with "we solve this problem competitors ignore" before discussing market size.

We conducted voice of customer research but our TAM model doesn't include it. How do we integrate those insights?

Build your TAM defense around three executive objections using VOC as evidence. For "why will customers switch," cite specific pain points from your research with quantified impact. For "is the market real," combine competitive revenue with adoption validation from customer interviews. For "why this beachhead," reference the segment where pain intensity was highest in your research. Transform VOC from background research into your primary proof that you can capture share in focused segments. Eight validated customer conversations outweigh elaborate forecasting models.

How do we balance AI efficiency with maintaining authentic customer relationships at conferences?

Use AI for pre-event intelligence gathering and post-event follow-up, but prioritize human presence during conversations. Pre-conference: AI generates competitive intel, battle cards, and booth training scenarios. At the booth: focus entirely on listening, asking clarifying questions, and capturing unique insights. Post-event: AI helps synthesize notes and personalize follow-up. The ROI comes from relationships built through authentic listening, supported by AI-accelerated preparation and execution. Treat AI as infrastructure that frees you to be more human, not less.

Shownotes and transcript

Shownotes and transcript

Summary

AI makes scaling easy - the winners use it to do better, not just more, by keeping human judgment in the loop.

Shownotes

Most life science teams are stuck talking about AI transformation while competitors execute it. The gap between 68% AI optimism and 7% power users isn't about awareness - it's about execution.

This episode is for biotech marketers and product managers navigating AI adoption and product development decisions. Matt and Jasmine explore why organisations retire risk by standardising rather than solving creatively - a pattern that undermines both AI implementation and product differentiation. The key insight: AI makes it dangerously easy to produce more without asking if the strategy underneath is broken. The winners use AI to do better, not just more.

What you will learn:

• Why "secret cyborgs" using free AI tools with zero guardrails are a symptom of execution failure
• How to move personas from static PDFs to interactive synthetic customers you can query
• When to pause AI implementation because you're optimising the wrong work
• Why Stage Gate committees systematically prune differentiation before launch
• How product managers can defend customer pain points with veto authority
• The connection between AI pilot mode and launching commoditised products

Keywords: AI implementation life science, synthetic customer persona, AI execution gap, Stage Gate product development, biotech marketing strategy, AI guardrails, product differentiation, crossing the chasm life science, voice of customer, AI literacy

Watch the full episode, subscribe for weekly insights, and visit strivenn.com for tools to close your own execution gaps.

Transcript

In this episode of A Splice of Life Science Marketing, Matt Wilkinson and Jasmine Gruia-Gray tackle two connected pressure points bleeding advantage from life science organisations: the widening gap between AI enthusiasm and actual deployment, and Stage Gate committees that systematically prune differentiation during development. Both problems share the same root - retiring risk by standardising rather than solving creatively.

The AI Execution Gap

Speaker: Matt Wilkinson

Matt, welcome to A Splice of Life Science Marketing. Your go-to show for life science marketing professionals in biotech, med tech and diagnostics. Join us for sharp strategic conversations that turn cutting edge insights into real world marketing advantage. I'm Matt Wilkinson.

Speaker: Jasmine Gruia-Gray

And I'm Jasmine Gruia-Gray. In each episode, we'll cut through the hype and complexity with practical plays you can use to earn trust, stand out in crowded categories and convert attention into momentum.

Speaker: Matt Wilkinson

Hi Jasmine, hello Matt and hello everybody. Today, we're examining two pressure points where most organisations are bleeding advantage. First, the widening gap between AI enthusiasm and actual deployment, and then the Stage Gate committees that systematically prune differentiation during development long before launch. Teams inherit a commodity product and wonder why positioning feels impossible. These aren't separate problems. They're symptoms of the same organisational pattern - retiring risk by standardising rather than solving creatively, whether it's defaulting to generic AI experiments or cutting awkward product features that address high value pain points. The mechanism is identical. Let's start today with where most companies are stuck right now - talking about AI transformation while their competitors execute it.

Speaker: Jasmine Gruia-Gray

What I really thought was important about your blog post, Matt, was that you call out the secret cyborgs - people in your organisation using free AI tools with zero guardrails because you haven't given them approved alternatives. That's not a future risk, that's happening right now in every life science company that banned AI tools without providing substitutes. And the gap between the 68% who are optimistic about AI and the 7% who are actual power users - that's not awareness, that's execution failure. But what stopped me cold was this insight: we're drowning in information but starving for absorption. AI can generate infinite content. The bottleneck is human understanding. That reframes the entire AI conversation from what we can automate to what actually helps us learn and decide. So your article isn't about AI capabilities. It's about why the gap between enthusiasm and execution keeps widening and what actually closes it. So I wanted to follow up with some questions. You identified three blockers: data readiness, AI literacy and incentive misalignment. But the data point that stands out is organisations taking two to six years to properly embed Salesforce. That's wild. If that's the baseline for a relatively simple system, what's different about the organisations that are breaking through with AI?

What Winning Organisations Do Differently

Speaker: Matt Wilkinson

Now it's really interesting, and the companies that I've worked with that are breaking through with AI are the ones that are using the tools at scale. They're creating spaces where people can really play with the tools, and they're really focusing on not just AI literacy, but sort of an AI culture - one where people collaborate together amongst themselves and with the AI. But they're ones that don't just treat AI as a magic bullet. They're ones that sort of look at it going, okay, these are the tasks that we can use AI to help us solve and these are the places where, really, we should be making sure that we've absolutely got human oversight, that we're working alongside the AI tools, rather than just handing everything over to them. And that's the big cultural challenge as well. Because one of the big concerns that you and I know from speaking, you know, from interviewing so many folks at ELRIG Drug Discovery 2025, was that the more people sort of talk about AI, there's a group of people that are very scared that AI is going to take their jobs. And I think it's going to take the tasks rather than jobs, at least in the first instance. And of course, the more tasks AI takes, maybe there is a need for fewer jobs, but we need to look at those tasks - where can AI help elevate what we do? And I think that's one of the things that the organisations that are winning are doing. They're the ones that are making AI easy to use, immediately available, and it's something that culturally they're adopting to try to do better and not just more.

Speaker: Jasmine Gruia-Gray

So I think what you're saying is it's a mindshift in not thinking about AI as your helpful copy paste tool, but thinking about it around how it can help you go deeper into a subject, how you can experiment with different questions and different ways of cutting data much more quickly than you can do on your own.

Speaker: Matt Wilkinson

Yeah, absolutely. And it's about looking at how can I really get to grips with more things, but also that there are tasks that AI really should now be helping us with, and we should be looking at how, as a creator, you know, whatever role that you're working in, how can I focus on creating a better end product that's more aligned with who I'm trying to communicate with, rather than just creating more, which I think is the great fallacy of where a lot of AI initially was going.

Persona as Agent, Not Artefact

Speaker: Jasmine Gruia-Gray

So you also talk about Persona AI, an agent that you created, as moving from persona as artefact to persona as agent. Can you walk us through what that actually looks like when someone's testing messaging or practising a sales pitch? What changes in the work?

Speaker: Matt Wilkinson

So one of the things that I know that you and I have discussed in the past is that there's that great, disheartening moment when you've been out and you've created these personas and they're just then left to rust on a hard drive somewhere. They're probably not even printed out, so they might just be a pretty PDF that people look at during a project and they never really use again. What this does in the first instance is to be able to take all of that information that you've gathered around your persona, and one of the ways that it allows us to do it is to actually create an average of even more members of our persona group, if you will, as human beings. It can be quite difficult to create a persona based off of more than a few examples of one customer, but if we know 10 or 20 LinkedIn profiles of who our persona really is, we can actually use deep research tools to create an aggregate of all of those into one average persona, and that gives us a lot of deep context. But then what it allows us to do is we can layer on interviews, feedback, all the sorts of information that we would need to really turn that persona into something really, really detailed, but that gives so much more information than a human could ever hold. One of the challenges of using personas is that you're trying to move yourself from standing in your shoes to standing in your persona's shoes and then writing content or creating something that works for the persona. Maybe you're doing new product development or creating a new website - whatever it is, you're trying to do something that really resonates with that persona. Now, by putting that all into essentially a retrieval augmented generative AI system, and using that as the context from which you create a synthetic customer, it allows you to interact with that synthetic customer in a way that they're then able to essentially embody all of that research that you've done. So in the first instance, you could use that to help you create content to test messaging, very much in a chatbot style, but you can also do it in some really fun ways. So there are now tools like Yoodli, where you can create personas based on that research and personality insights, and then have a conversation with those synthetic customers. Well, I'd set those up for, shall we say, a sales role play, or maybe a customer visit. So it doesn't really matter what the scenario is - anytime that you wanted to be able to interact with your persona, there are ways to do it. At the moment, there aren't tools that allow you to sort of encompass every single scenario straight off the bat. You have to sort of pick and choose a little bit, but there are absolutely ways to really, really be able to get under the skin of your persona and really be able to use them in ways that just a few years ago were absolutely impossible.

Speaker: Jasmine Gruia-Gray

Again, it comes down to how AI can help augment your thinking. In this particular case, with a synthetic customer, brainstorming with that synthetic customer - would you rather see the marketing message this way or that way? Which one resonates more with you? Which pain points are more acute with you - this one, that one or the third one? So it's a fabulous way to move away from copying and pasting and just taking what AI says de facto and instead augment your thinking and experiment with different outputs.

Speaker: Matt Wilkinson

Yeah, absolutely. And you can ask those personas to respond with the personality that you believe that persona would have. So along with the wants and needs and how they get measured in their job, you can also say, hey, well, this person, they're working in quality control, maybe they're a little bit more detail oriented than the creative marketer. And so you're really able to then get people engaged with that chatbot and get the responses back really aligned to that personality type that you input into that persona. And that allows you to help everything from R and D all the way through to sales conversations, and of course, with a big bunch of help for marketers in the middle as well.

When to Pause AI Implementation

Speaker: Jasmine Gruia-Gray

So in these last couple of minutes, we've been talking about ideas on how to implement AI, and of course, the guardrails that are important. Let's look at the flip side. When should a sales or marketing leader actually pause AI implementation because they're optimising the wrong work?

Speaker: Matt Wilkinson

Well, the short answer is when they're using AI to scale work that shouldn't exist in the first place. AI makes it dangerously easy to produce more - whether that's more content, more campaigns, more of whatever you want - without ever asking if the strategy underneath is broken. And one of the things that's really important is that with AI, of course, we can scale out to multiple people with multiple personas. We can be everything to anybody. We can personalise at such scale that we can appear as any of our potential customers might want us to be. But if we do that, and we personalise in the wrong way, and we sort of become this amorphous blob, we no longer stand for anything. And the whole point of marketing is to stand for something and to know who you're for, and perhaps more importantly, who you're not for. So it's really important, in my mind, to really make sure that you're not just doing more of the same, but actually you're using AI to help you test, measure, and then be able to do better at what makes a difference.

Speaker: Jasmine Gruia-Gray

I think it almost comes down to the saying "just because you can doesn't mean you should," and really understanding what problems you're solving by using AI is super important. So where have you seen teams get this wrong? Where do you think people cede judgment to AI where they shouldn't?

Speaker: Matt Wilkinson

I think it's really around not making sure that they've got humans in the loop. There are some hilarious - I mean, I say hilarious - some really worrying examples that make the news where people are looking at putting things out that include AI hallucinations. Some pretty big decisions were made by UK Police recently based on some AI hallucinations. And so there's some examples where you've got to be really careful for that. But I think the more sensible approach is making sure that you're not quoting false case law if you're going into court, that you're not quoting false hallucinated references. So really being able to make sure that you're fact checking everything, but also that you're not ceding that emotional judgment and taste. So we really have to make sure that we know what's important. And you know, we make sure that we're keeping human judgment in the loop at every stage of what we're doing. What we really want to be doing is making sure that we're communicating to the customer. And so we have to make sure that the content and the way that we show up to the customer is true, because still, at this point, the customer is making the final decision. And so we have to focus on the human still, with knowing that the AI is doing a lot of help, in some cases now along the buying journey.

Speaker: Jasmine Gruia-Gray

What I think you're also saying is AI may be very good at the logic side of how we think, but not at all on the emotional side or on the ego side. And if for no other reasons - and those are very important reasons - the human should be staying in the loop to inject emotion and to make sure that the outputs are not soulless, as I've many a time told my LLMs.

Speaker: Matt Wilkinson

Yeah, we absolutely need to make sure that those outputs aren't soulless, and I think that's one of the things that breaks me about that execution gap, and why closing it matters more now than ever, because so many more people are using AI now, and so there is so much more AI slop out there. Most teams discover their AI strategy collides with deeper problems - whether that's the data, whether that's culture, whatever it is - there are a lot of problems that people discover when they're going through AI implementations. And so organisations are like icebergs, and trying to turn them around is incredibly difficult. We really have to play that game of yes, okay, souls and small teams can scale quickly with AI, can do incredible things with AI, but big organisations are still full of people. And I think everybody in an organisation pointing the same direction and learning at similar levels - that's a big ask. And so I think that over the next few years, one of the big things that will be really challenging is do the learning and development teams really make sure that not only are their current teams trained up to use these tools in a landscape that is rapidly changing, but also that they're then moving into how do they hire correctly? And there's a whole thing about training students that I'm sure we don't have time to get into today.

Stage Gate Committees and Differentiation

Speaker: Jasmine Gruia-Gray

Fantastic. Well, thank you for this, Matt.

Speaker: Matt Wilkinson

Now moving on to your article this week, and you talked about Stage Gate committees pruning products the way that amateur gardeners prune trees - they're cutting branches that look risky, rather than solving for the harvest. And I love this, because trying to look after orchids and some bonsai, it's always been difficult to know when to prune, how to look after things correctly. And so you use something called a Pomona metaphor, and you looked at healthy looking branches, and people that would look at removing healthy looking branches and saving the awkward ones, because understanding which branches would actually produce fruit. Unfortunately, Stage Gate committees are often doing the opposite. They're retiring risk by standardising to proven approaches, which systemically destroys differentiation before launch teams ever see the product. What I thought was really brutal about your analysis is the mechanism - now each functional group optimises for risk reduction within their own domain. Regulatory wants proven formulations. Manufacturing wants conventional processes. Sales wants comfortable positioning. And nobody's really evaluated on whether the cumulative effect turns your differentiated innovation back into a commodity. And I wonder whether this is really why so many new products fail, or one of the big reasons why so many products feel like they're completely undifferentiated. And you know, when you're going in to try and help organisations to launch a product, actually finding a unique position is so tough. So I've got a few questions on this. So you argue that differentiation is decided upstream during feasibility and development, not downstream at launch. But most of us, especially those of us that work in marketing, don't get visibility into those Stage Gate reviews until it's too late. So what's the earliest signal that differentiation is being pruned, and what should trigger alarm and where should those alarm bells be going off?

Speaker: Jasmine Gruia-Gray

I really think that product marketing and product managers need to be the champions of the customers for whom the product is being developed. The alarm bells should be going off when something - some feature, some advantage - is being taken away for whatever meaningful risk, and the product manager thinks, "Oh, that person, Dr Smith, I received voice from him about a pain point he has. That feature would have addressed that pain point. And the business case I as a product manager developed was predicated on that main feature." Those are some of the signals and alarm bells that should be going off in that product manager's mind. And that's the importance of sharing Voice of the Customer across the whole core team, whether they're in R and D, whether they're in regulatory, whatever function they're in, so that others on the team can also have those alarm bells as they're thinking they're retiring risk.

Speaker: Matt Wilkinson

That's really interesting. So you know when you're looking at retiring risks, you identified three branches that you strongly recommend shouldn't be pruned: severe customer pain points, economic differentiation that changed unit economics, and workflow integration that reduces adoption friction. How should a product manager actually quantify these before you start going into the feasibility gate processes so they have some negotiating leverage to make sure those parts of the product definition don't get pruned?

Speaker: Jasmine Gruia-Gray

I think it's again important to connect the dots to the voice of the customer, and not sort of generic, you know, "let's make it easier" type of genericising it, but rather be specific. "Let's eliminate these three manual steps that cause 15% of early stage researchers to abandon a protocol." Yes, you're making it easier, but you're being very concrete and very factual on what that ease is going to mean back to the customer and how that connects to the pain points that they articulated to you, as well as within the workflow. So it's a combination of different factors - being specific. And really, I don't think that committees can as easily argue against ROI calculations and against these types of facts within a workflow.

Speaker: Matt Wilkinson

Nice. So on a more practical note, you recommend product management should have veto authority over this sort of "prove it" pruning, the same way regulatory or manufacturing can veto on compliance or feasibility. That's a big ask. How do you make that case to leadership without sounding like you're just defending, you know, pet features?

Speaker: Jasmine Gruia-Gray

It comes back to what I had said earlier. Product managers are the guardians of the customer and the future customer. And what I mean by that is they need to be able to articulate what the risks are in terms of the customer's pain points, and in terms of the business case. It's okay to prioritise certain features and build a business case based on those priorities, but if you, as a product manager, don't have the veto authority to keep that business case intact, then you end up at the end of the line when you're getting ready to launch the product where it's just me-too, and then you don't end up realising the benefits of that business case. So it's all a really important circle that the product manager is responsible and accountable for, and should also therefore have the veto authority when these really important, prioritised features are running the risk of being taken out.

Speaker: Matt Wilkinson

Yeah, really, really a lot of sense. One thing that I was really pleased to see in your article was that you touched on Geoffrey Moore's Crossing the Chasm framework, which I'm a big fan of, and you talked about how Stage Gates optimise for mainstream acceptance before establishing the beachhead market. And so does that mean that very often we're optimising for the mainstream and the people that are going to be buying a disruptive innovation once it's accepted, but we're neglecting those early adopters and innovators? Where does this break down? When should committees actually prune aggressively, even if it risks some of those differentiation points?

Speaker: Jasmine Gruia-Gray

When the differentiation solves a pain point - I mean, we keep circling around the same themes - the pain point that customers aren't actively seeking solutions for. Those are ones that should be pruned away. When the complexity exceeds the value that's being created - that's what should be pruned away. And in part, that's the value of alpha testing, of having an early prototype that you can go back to your group of VOC and asking their opinion and watching them use that prototype, or watching their reaction to you demonstrating the prototype. All of that can then help you further prune or realise that you have hit the right mark and that you have prioritised the right key features. And I think it's important that you understand from the get-go who is your target audience. Is it those early innovators, or is it the middle majority? Is it a line extension that's, you know, really focused on your middle majority? And that way you can more easily weigh where your features should be prioritised.

Speaker: Matt Wilkinson

Fantastic. Yeah. And I think that making sure that you've got that beachhead and you've really stabilised it before going on is such an important point that, especially in our industry, you've got to prove that it works. And I think that's a really, really strong takeaway - but you've got to prove that you're meeting those needs for customers. So love the framework.

Your Move This Week

Speaker: Jasmine Gruia-Gray

Thank you for that. Here's what connects these conversations. Execution gaps compound. The AI implementation gap and the differentiation pruning problem are both symptoms of organisations retiring risk by standardising rather than solving creatively. When you're stuck in AI pilot mode or launching commoditised products, the pattern's the same. Each function optimises within their domain. Nobody's accountable for strategic advantage, and by the time you see the problem, the critical decisions have already been made. Your move this week that Matt and I are challenging you to is: pick one place where your team is optimising for comfort instead of for competitive advantage. If it's AI, identify one high frequency task and solve it, rather than strategise it. If it's product development or product management, attend one gate review and watch which features get de-scoped. The differentiation you lose upstream determines the positioning battles you fight at launch. Thank you so much, Matt, this was a really interesting conversation.

Speaker: Matt Wilkinson

Always is. And thank you, Jasmine, that was great to see you again.

Speaker: Jasmine Gruia-Gray

Thank you all for attending another episode of A Splice of Life Science Marketing, and we look forward to seeing you again.

Speaker: Matt Wilkinson

Soon. Bye bye. Thank you for listening to A Splice of Life Science Marketing. We hope you enjoyed the episode.

Speaker: Jasmine Gruia-Gray

If this conversation helped you, the single biggest way you can support the show is to subscribe and leave a review on YouTube, Spotify or Apple Podcasts. We'd really appreciate it, and it makes a huge difference.

Speaker: Matt Wilkinson

You can find out more about us and the topics we discuss at strivenn.com or on LinkedIn. Thank you so much for listening. We hope to see you next time.

Q&A

How do I identify if my team has "secret cyborgs" using unapproved AI tools?

Run a simple survey asking what tools people use daily - include AI options without judgment. Watch for inconsistent output quality or speed between team members. Check browser extensions during screen shares. The goal is not to punish but to surface hidden productivity so you can provide approved alternatives with proper guardrails.

What is the fastest way to test synthetic customer personas without a big budget?

Start with one persona. Gather five to ten LinkedIn profiles of real customers fitting that type. Use Claude or ChatGPT to synthesise common patterns into a detailed profile. Then create a custom GPT or Claude project with that context and test three marketing messages against it. Total cost: under fifty pounds and a few hours.

How do I know if I am scaling work that should not exist?

Ask: "If we stopped this activity tomorrow, would anyone notice within 30 days?" If not, you are scaling noise. Check whether the work ladders to a measurable outcome tied to pipeline or revenue. Content that fills a calendar but generates no engagement or leads is the first candidate to cut before you automate it.

What single question should I ask at my next Stage Gate review?

Ask: "Which customer pain point does this feature removal affect, and how does that change our business case?" This forces the committee to connect de-scoping decisions back to Voice of the Customer evidence. If nobody can answer, the decision is being made on internal comfort, not market reality.

How can a marketer get earlier visibility into product development decisions?

Request observer status at feasibility gate reviews - position it as ensuring launch readiness. Offer to present Voice of the Customer data at each gate, framing it as reducing commercial risk. Once you demonstrate value by catching a differentiation threat early, you earn a permanent seat at the table.

Shownotes and transcript

Shownotes

Your buyer has already decided before they ever speak with you. The question isn't whether you can persuade them—it's whether you've made it easy for them to justify choosing you.

This episode is for life science product managers and marketers in biotech, medtech, and diagnostics who want to close the gap between what customers say and what they actually do. Matt and Jasmine unpack the uncomfortable truth about where purchase decisions really happen, why field intelligence beats advisory boards, and what survivability signaling means for vendor selection in 2026.

What you will learn:

• Why buyers build mental shortlists long before filling in contact forms
• How to audit content for quotability, not just persuasiveness
• What fragility signals accidentally broadcast vendor instability
• Why product managers must spend time in the field (and what to capture)
• How ethnographic observation reveals pain that surveys consistently miss
• Why messaging must evolve through the product adoption curve

Chapters:
[00:03] Introduction
[01:46] Article 1: The execution gap in life science marketing
[03:48] Where decisions actually happen (before contact)
[06:43] Survivability signaling and fragility signals
[13:18] Article 2: Field intelligence and product management
[16:21] Why specificity matters more than reach
[21:18] Spending time in the field transforms requirements
[25:39] Ethnographic research vs theoretical voice of customer
[28:17] Post-launch observation sharpens messaging
[31:25] Closing thoughts

Keywords: life science marketing, buyer decision journey, product management, voice of customer, ethnographic research, B2B marketing, biotech marketing, field intelligence, vendor selection, customer discovery, product lifecycle, marketing execution

Watch, subscribe, and visit strivenn.com for more strategic insights on life science marketing.

Transcript

Matt and Jasmine explore two critical shifts in life science marketing: the execution gap between AI optimism and actual use, and why field intelligence transforms product development. They discuss where purchase decisions really happen, how to signal vendor stability, and why product managers must capture customer language before it gets sanitized into feature requests.

Introduction and Episode Overview

Matt Wilkinson [00:03]

Welcome to a splice of Life Science Marketing, your go-to show for life science marketing professionals in biotech, medtech and diagnostics. Join us for sharp strategic conversations that turn cutting edge insights into real world marketing advantage. I'm Matt Wilkinson.

Jasmine [00:22]

And I'm Jasmine Greer-Grey. In each episode, we'll cut through the hype and complexity with practical plays you can use to earn trust, stand out in crowded categories, and convert attention into momentum.

Matt Wilkinson [01:14]

How you doing?

Jasmine [01:15]

I'm doing great and how about you?

Matt Wilkinson [01:17]

I'm good, thank you. And I'm really excited to be talking about these two topics today. In this episode, we start with an uncomfortable truth. Your buyer has already decided before they ever speak with you. And from there, we look at why your field teams see problems product and marketing never hear about, and why that disconnect is now a real commercial risk.

Article 1: The Execution Gap and Where Decisions Happen

Matt Wilkinson [01:46]

And on to our first article.

Jasmine [01:53]

Matt, in your article, you state that by the time a buyer reaches your website looking for product information, they've already decided on the shortlist and is already building the narrative ready for procurement. Your job isn't to convince them, it's to help them justify what they've already concluded.

Your latest piece cuts through the 2026 predictions to expose the execution gap that's actually deciding winners and losers in life science marketing. 68% of companies say they're optimistic about AI. However, only 7% are actually using it at scale. The pattern's identical for marketing strategy, widespread acknowledgement that something needs to change, minimal evidence of action. This isn't about tactics. It's about whether your marketing signals resilience or fragility and whether anyone in your organization owns how buyers actually understand you. So let's get into it. What I really, really love about your article is this pivot from marketing being the lead generation department or marketing qualified lead department to the persuasion and alignment department. For me, that was a huge mental shift. And you also open with your buyer has already decided before they contact you. That reframes the entire marketing conversation. What are teams actually getting wrong about where decisions happen? and what does that misunderstanding cost?

Matt Wilkinson [03:48]

Well, thank you for that. And teams still think the decision happens during the sales conversation, but it doesn't. Your buyer is building a mental shortlist long before they ever fill in a contact form or they reach out to you. They're reading Reddit or research gate threads you never know existed. They're asking colleagues in the lab or on social media. You know, they're scanning your about page late at night.

or they're actually asking CHAP GPT or Claude to do all of that for them and to create a buyer's guidance summary for them. By the time they actually reach you, they're not evaluating whether to choose you or not. They're looking for evidence that confirms what they've already decided. They think of it not as persuasion, but more like defensive justification. The cost of missing this?

You optimize for the pitch when you should be optimizing for the silent research phase. You create content that's persuasive in conversation, but not quotable in a procurement document. Here's the thing. If a budget holder can't copy your value proposition into a grant application or business case, you've lost before you've started. They'll choose the vendor who made it easiest to defend internally, even if your product's better. The behavior change audit your content for quotability, not just persuasiveness.

Jasmine [05:17]

Yeah, I love that. I think that more and more marketers, as well as the sales organization, needs to think about pulling that thread on persuasion to quotability and give the prospective buyer that language so that that can help not only if you're an academic and writing a grant, but if you're part of your decision-making committee in a pharma organization.

Matt Wilkinson [05:57]

Yeah, it's and it's and it really is showing up more and more. AI is helping here, of course, but our buyers are becoming so much better informed that the ability for us to persuade is diminishing. And so really what we have to do is to provide evidence in formats that the whole buying group needs.

Survivability Signaling and Fragility Signals

Jasmine [06:15]

Yep, completely agree. So you talk about survivability signaling, the idea that buyers optimize for vendor stability, financial stability, not just product performance. What are the fragility signals that life science teams accidentally broadcast? And what's the fastest way to audit for them?

Matt Wilkinson [06:43]

Yeah, there's when you're engaged in long term research, particularly for capping, you know, for long term capex, or you're looking to, you know, do research that might take many years and, you know, to complete, you know, those fragility signals are really important to watch out for. They show up in patterns that we don't really necessarily see. So aggressive discounting whispers desperation.

Constant pivots of branding and messaging signal uncertainty. High turnover and customer facing roles broadcast chaos. Now these aren't secrets. They travel fast through tight-knit scientific communities. Here's a concrete example from our LRIG research. We watched exhibitors at Drug Discovery 2025 focused entirely on new customer acquisition, but failing on differentiation and follow through.

You know, the booths were, you know, that looked identical to almost every competitor. You know, most exhibition floors feel like you're wading through a sea of beige. You know, yes, some of the colors stand out, but everybody's doing the same thing. And those conversations that you have, most of them could come from anyone, any company. They're not saying anything differently.

And then the follow-up that never arrives or maybe it arrives is generic and it just forgets the conversation that you've already had and each one of those signals that the companies aren't paying attention. So I think of survivability not as what you say, but what buyers infer from how you show up. know, when funding environments are constrained, buyers optimize for vendor survival, not vendor performance.

Article 2: Field Intelligence and Voice of Customer

Jasmine [13:18]

So Matt, in your article, you make the case that marketing's biggest execution gap isn't AI literacy, it's specificity. You argue that most teams fail not because they lack tools or expertise, but because they refuse to choose who they're actually for. You write, pick customers, not markets. Company X or academic institution Y won't buy from us because our platform requires high throughput workflows and they run low volume fixed cell work. Now you've got focus. You've got specificity. You stop debating edge cases and you price with confidence. Your messaging speaks directly to people who actually need what you built. And honestly, there's an ego here too. Teams want to believe everyone could be their customer, but that's not reality. That's not real customer centricity. That's wishful thinking. And sort of going back to your blog around persuasion, in order to focus on persuasion and carry through to alignment, You have to be laser focused on specific customers within your market and it can't be everybody.

Matt Wilkinson [21:18]

That's really interesting and absolutely speaks to the challenge of positioning where you have to choose who you're for and who you're not for. Your blog also reminded me or recommends that how important it is to go and spend time in the field. And you recommend that, you know, a product manager should be spending at least a day, you know, in the field per month with 24 hour debrief. Walk us through what what you believe that product managers and managers should capture that transforms those observations into requirements engineering that can actually execute.

Jasmine [22:02]

Yeah, so I want this to be every product manager's New Year's resolution. Get out there and travel with your sales and FAS colleagues at least once a month. I've worked with way too many product managers that just sort of sit in the office. They say they don't have time to get out. They'll call a salesperson when they have a question. That's not good enough.

Customer language, when something breaks, is so, so important to experience firsthand. And not think of it as a feature request. The exact words they use when they're frustrated is important. Because customers very, very rarely say, I need better signal stability.

What they will say instead is it worked one day, then the signal vanished after fixation. And I've been there as a bench scientist and that frustration comes out, hopefully not in expletives, but it comes out. hearing that firsthand is so important. That's the emotional side.

that every scientist has as well as their logical side. And that makes things so much more memorable and so much more urgent. And when you write that directly into your product requirements document or PRD, now your engineering team stops debating about abstract specs and starts solving a real problem. Plus that language becomes your launch messaging and you're able to pull that through from true VOC into actions during the launch. Every requirement document should have at least one line per section in the customer words. It keeps your team honest. It makes it so much more memorable and relevant. And it's a great reminder of who you're actually developing and launching product for. Here's the thing. Customers buy on emotion. The idea that we're scientists and we only work on logic, that's a Vulcan. That's not a scientist. We also have an emotional side to us, which helps to justify the logic. And you need both. So again, I implore product managers, go to the lab. travel with your sales and FAS colleagues, hear the frustration firsthand, and capture it before somebody sanitizes it into a feature request. Meaning, do that debrief before the 24-hour clock is over, because we tend to forget after that.

Ethnographic Research Reveals Hidden Customer Pain

Matt Wilkinson [25:12]

Fantastic. Now, you claim that most voice of customer is theoretical because it captures what customers say they do. And you touched on this a minute ago, not what they actually do. So what does firsthand observation reveal customer pain that surveys and advisory boards, maybe even working groups consistently miss?

Jasmine [25:39]

So a lot of it is firsthand observation. So ethnographic research and watching that customer do things in the lab that a customer may not articulate or remember to articulate. There is a gap in memory sometimes between what you're actually doing and what theoretically you should be doing. So watching that workflow in real time, firsthand becomes so important. It also gives you as a product manager that opportunity to ask, why did you make that choice versus that choice? We had originally thought the workflow would go ABCD, but you're choosing to do it as CDEF. Tell me a little bit more about that. So that interaction and that observation is so critical.

Matt Wilkinson [26:42]

It's hard not to agree with that. Now the number of ethnographic studies I've conducted where people will say one thing, but actually they're doing something different. Those examples are really, really interesting. And it's not that the customers are lying. It's just that they actually think they're doing what they're told. But in reality is they're having to use workarounds to get things to work just the way they need it. And that, I think, gives us a great opportunity for looking at product improvement.

But even if that's just instruction improvement, but also it helps us look at actually, maybe there's some competitive advantage we can be gained from doing CDEF rather than ABCDE. And so if we if we look at those things, it becomes really, really important. I think the other thing about being in, you know, getting to observe people in their natural habitat is that it gives you a really good grasp of the of the subtlety of the messaging that's needed, or how to really pull those levers. And of course, one customer visit doesn't make an entire portfolio, but it does make a, if those things compound over time, those visits compound and that insight really compounds. And if you're able to capture that, especially if you can record audio, video, get transcripts and start using that with, starting to make sense of all of that data with AI, you can actually use that through the CRM and kind of create a knowledge base that is far greater than the sum of the individual visits.

Post-Launch Messaging Must Evolve

Jasmine [28:17]

I think you're making a really important point, which a lot of product managers miss. Just because a product is launched doesn't mean your job is done and you need to move on to the next product. There's an opportunity through this observation to sharpen the message, to fine tune the pains and gains, the two sides of that messaging as the adoption curve continues.

Matt Wilkinson [28:54]

And one of the things that we know is that as we go through the adoption curve as well, buyers needs change, our customers needs change. An innovator wants to play and learn and experiment with the technology and a process, whereas a laggard just wants to get it done. If wants to get the result out at the end of the day, they don't really care. And so depending on the maturity of the technique and that really depends on what do we need to... What kind of capability do we need? All the functions that maybe an innovator wants is maybe way too much for somebody as a late majority or a laggard who actually just wants to hit a button, get the result and say, did it give me a yes or a no?

Jasmine [29:38]

Yeah, it's so critical to marry that stage in the product lifecycle with that messaging. And again, keep in mind who your customer is, what their job to be done is, and what their mindset is.

Matt Wilkinson [29:57]

Yeah. And, and it's so interesting that you say that because, you know, and, and if innovators are often, you know, so have such a different mindset and they might be using, you know, with some techniques, they might be innovators in others. They might just be using them as sort of part of the process and they just need it to work every single time. So it's not that somebody is an innovator or, you know, a late majority as a person, it just could be that their needs. For some things, we just need it to work and it needs to be simple and just predictable. Other times, actually we need to be innovating. And when you're trying to push the boundaries of science, it's really interesting that actually a lot of what we're trying to do is to build on, push the boundaries of science by building on a stable foundation. So we have to realize that even though somebody's an innovator here, they can't be building innovations on a foundation of sound.

Closing

Jasmine [30:55]

So true. This has been tons of fun, Matt. Thanks.

Matt Wilkinson [31:00]

Yeah, thank you. It's been a lot of fun as well. For those of you listening, hope you've enjoyed sort of us deconstructing and digging in a little bit to the two topics about what's changing in 2026 for marketing teams and some of the things that product marketers can do to help adapt and make sure that they're really keeping up with what their customers need.

Jasmine [31:25]

Well, looking forward to having everybody listen again in the next episode of a splice of life science marketing.

Matt Wilkinson [31:35]

Likewise.

Q&A

How do I audit my content for quotability rather than persuasiveness?

Open your latest product page or case study. Highlight any sentence a procurement officer could copy verbatim into a business case or grant application. If you struggle to find three quotable lines, your content is optimised for conversation, not justification. Fix this by writing value propositions as complete, standalone statements. Include specific outcomes, timeframes, and comparison points. Test by asking a colleague: could you defend this purchase using only our website content?

What fragility signals should I audit for first?

Start with your exhibition presence and follow-up process. Review your last three trade shows. Did booth conversations sound identical to competitors? Did follow-up arrive within 48 hours and reference specific discussions? Check pricing consistency across channels. Aggressive discounting or frequent promotional pivots signal desperation. Finally, audit LinkedIn for customer-facing team turnover. High churn in sales or support roles broadcasts operational chaos to tight-knit scientific communities.

How do I get product managers into the field when they say they don't have time?

Make it mandatory and calendar it now. Block one day per month as non-negotiable field time. Pair each product manager with a specific sales or field applications scientist. Create a simple capture template: customer exact words when frustrated, workarounds observed, questions asked three times. Schedule the 24-hour debrief before leaving the field visit. The ROI shows immediately in sharper requirements, better launch messaging, and engineering teams solving real problems instead of debating abstract specifications.

What's the difference between theoretical and ethnographic voice of customer research?

Theoretical VOC captures what customers say they do in surveys and advisory boards. Ethnographic VOC captures what they actually do in their natural environment. The gap matters because customers can't always articulate unconscious workarounds or recall exact frustration language. Watch a scientist run your protocol in their lab. Note where they deviate from instructions, what triggers visible frustration, and which steps they repeat. That observational data reveals product improvement opportunities surveys miss entirely.

How should messaging evolve through the product adoption curve?

Innovators want technical depth, experimental flexibility, and learning resources. They'll tolerate complexity for capability. Late majority and laggards want predictability, simplicity, and guaranteed results. They don't care about features—they want the outcome. Audit your current messaging. If you're selling workflow flexibility to customers who just want to hit a button and get a yes/no answer, you're misaligned. Match message complexity to adoption stage. Innovators get detailed protocols. Laggards get push-button reliability statements.

Shownotes and transcript

Shownotes

AI can scale research, but delegating presence has hidden costs. Life science marketers face six seismic shifts in 2026 that demand human judgement.

This episode is for marketing and product managers in biotech, medtech and diagnostics navigating the tension between AI efficiency and strategic presence. Matt and Jasmine unpack when AI accelerates discovery and when showing up yourself is the only option that builds conviction, captures contradictions and earns trust.

What you will learn:

• The PHASE framework to decide when you must be present in customer discovery versus when AI can handle it
• Why cognitive offloading erodes your ability to defend strategic pivots with conviction
• Six converging trends from Forrester, McKinsey, Gartner and IDC reshaping life science marketing in 2026
• How AI is changing discovery processes and why account-level metrics now matter more than lead volume
• The shift from the golden age of digital marketing to what Jasmine calls the "platinum age" of hyper-personalization
• Why human connection at events and in field application conversations remains irreplaceable

Chapters:

[00:00] Welcome and Season Two introduction
[02:44] The Janus threshold explained: when to delegate presence to AI
[06:56] The PHASE framework: Purpose, Hypothesis, Action, Solutions, Endorsement
[11:07] Being present to check AI summaries and build relationships
[13:24] Six major shifts hitting life science marketers in 2026
[16:53] From golden age to platinum age: staying human while using AI
[19:53] One opportunity to focus on in 2026

Keywords: life science marketing, AI in marketing, customer discovery, voice of customer, ethnographic research, product management, 2026 marketing trends, buyer enablement, account-based marketing, trust in B2B, peer review, cognitive offloading, strategic presence

Subscribe to A Splice of Life Science Marketing for sharp strategic conversations every week. Visit strivenn.com for frameworks, tools and insights that help you earn trust and stand out.

Transcript

In this Season Two premiere, Matt Wilkinson and Jasmine Gruia-Gray explore two critical articles: Jasmine's Janus Principle on cognitive offloading in customer discovery, and Matt's six major predictions reshaping life science marketing in 2026. They debate when AI accelerates research and when human presence is the only path to conviction, pattern recognition and strategic authority.

Welcome and Introduction

Matt Wilkinson [00:00]

Welcome to a splice of Life Science marketing. Your go to show for Life Science marketing professionals in biotech medtech and diagnostics join us for sharp, strategic conversations that turn cutting edge insights into real world marketing advantage. I'm Matt Wilkinson and I'm

Jasmine Gruia-Gray [00:18]

Jasmine Gruia-Gray in each episode, we'll cut through the hype and complexity with practical plays you can use to earn trust. Stand out in crowded categories and convert attention into momentum. Hello, gentlemen, Happy New Year. Happy New Year. Matt And Happy New

Matt Wilkinson [00:36]

Year to everybody that's joining us. Welcome to Season Two of a splice of Life Science marketing lasted this long in today's episode, we're going to be talking about two articles that we've written recently. One that Jasmine's written called the Janus principle, that looks at cognitive offloading and, you know, some really interesting insights and a fantastic framework that I know that everybody should be using. And then an article that I've written that really looks into some big predictions for 2026 and certainly some ideas that marketers should be thinking about and paying attention to. So without further ado, dive in. Let's kick it off. Jasmine, in your latest article, the Janus threshold, you argue that product teams and life sciences are at a pivotal moment. We know that AI can scale voice of customer and ethnographic research fast, but delegating presence has a hidden, hidden cognitive cost. The promise is really seductive. You know, you talk about how transcripts, themes create really tidy summaries, but the risk is that AI gives you conclusions without the mental activation that makes insights stick and really helps you get under the skin of those insights. You point to evidence that people who rely on generative AI struggle to recall what they produced, and that can translate into a warning for product managers who outsource too much discovery. The punchline of your pieces isn't not to use AI, it's to use AI for volume and post experience synthesis, and to use the phase framework that you introduce to decide when you must show up yourself based on my own experience of conducting hidden needs. Research your you know your comment that we still need to go and observe workarounds or observe people in the field, and to look at those workarounds that people use potential contradictions of what they say and what they do that really resonated with me. Now, one ethnographic project I conducted while working at LGC highlighted just how important it was to visit customers, observe them in their natural habitat and look for those workarounds, pain points and contradictions. So before getting too far into this, can you just define a little bit about what you mean by the Janus threshold itself,

The Janus Threshold Explained

Jasmine Gruia-Gray [02:44]

The way I look at it, the Janus threshold is the moment you decide whether to be present in discovery or to delegate the presence to AI. So Janus is a Roman god with two faces, and this serves as a good analogy to the product manager needing to look in both directions, forward at AI's efficiency promise, and backwards at what you're trading away by not being there. So if you cross that threshold, badly meaning. You surrender thinking, you surrender processing, you surrender reflection. You become what I sometimes call the pm vending machine, someone who puts in customer requests into that vending machine and outputs features without any transformative thinking in between. And that's that's a shame you operationally lose by doing that is human agency in shaping the research itself. Yes, AI executes the plan you gave it. It can pivot mid conversation, but it cannot pivot mid conversation when a researcher mentions an unexpected work around when that core facility director actually says that she's running weekend shifts only a human asks, why? The second thing you lose is pattern recognition across contradictions, when a researcher says they trust your your kit and the sensitivity of that kit, but then runs triple replicates on every sample your brain registers the gap between the words and the behavior. AI captures what the person has said, but it misses what their actions reveal. That contradiction is where insights really live. And the third thing that you have the potential of losing is the one that keeps me up at night the most, and that's the loss of conviction when you present a strategic pivot based on AI summaries, you sound uncertain because you are uncertain. You didn't live through the discovery. You don't have that first hand memory of being in the room when that market research is being made, or when that voice of the customer is being captured, and you're just reading someone else's the AI's field notes. So when you're in a conversation with R and D or with another product manager, that uncertainty comes through, that lack of first hand experience in the VOC capture comes through.

Matt Wilkinson [05:39]

So what I'm hearing you saying is that without the deep work, we don't actually activate our, sort of our mental capacity enough to really then be able to become the expert. It's almost like we're just, we're just reading something, and we know we can read something and sort of forget it almost instantly. So you're saying that to really be able to put ourselves in the customer's shoes and to be able to embody them and be able to really represent them, we need to be doing some of that deep work ourselves.

Jasmine Gruia-Gray [06:07]

Yeah, there's, there's so many neurological studies that show there's a lot of thinking, there's a lot of brain activation that's going on when you're in the room, when you with another person or with a group of people there, there's a lot of neural synchrony that goes on in that room that activates thinking, processing and reflection, and giving that up to an AI summary is actually a disservice to the job that you're meant to be doing.

The PHASE Framework

Matt Wilkinson [06:48]

So I know that you propose a phase framework as part of the article. Are you able to share what that looks like?

Jasmine Gruia-Gray [06:56]

Yeah, so phase stands for purpose. Hypothesis, action, solutions and endorsement. It's it's a quick acronym to help you ask yourself some questions to be able to decide whether it makes sense to have AI be present, or whether you should be present. So purpose. P for purpose. Am I clear on what this research is designed to answer if you're still figuring out whether some scenario is a real opportunity or just an edge case, delegating that to AI, and for AI to be present in voice of the customer is a mistake. H for hypotheses? What assumptions am I carrying into this research? If my hypothesis is that customers are using some kind of kit for a specific type of work in an underserved segment, and I'm wondering whether they pay a premium price for that solution. That's a hypothesis that I need to be present for to test in real time, and not abdicate that to AI, A is for action. Is this evidence for a decision already made, or evidence that could change the decision? S is for solutions? Could this research surface alternative solutions? I haven't considered there is really vital cognitive thinking in being in the room to be able to answer that question that, again, you don't want to abdicate to AI an E for endorsement. Will I need to defend these findings in a very deep, detailed sort of way? When I go back to R and D and say, we're not building some sort of instrument or kit or software solution, we're going to build an integrated platform instead, because I heard ABC during voice of the customer from from four or five core facility managers, that kind of deep connection of the dots and the ability to defend my position really requires you to be in the room for that.

Matt Wilkinson [09:32]

So I absolutely love that framework. And actually it brings me to a point where some work I conducted with University of Leicester last year now where we'd gone out, and we interviewed a number of different organizations. I was in the room or on the calls for all of those conversations, so I was part of the learning process, so I'd already been in the room, but then to go through and actually to conduct the thematic analysis, I was able to use reasoning models to do that markup, to markup transcripts, and to be able to kind of create those summaries, but because I'd already been there, I was able to check that it was a true representation of what had happened, rather than me sort of approaching it for the first time. So I think it's that. Can we use it to do some of the manual, tedious work, but can we make sure that we're doing the really important things? And of course, one of the things about VOC that's so important is actually being in the room anyway, and showing the customers that you value them with that, with your time. And so I think that there's the two parts there. So it is to making sure that you're part of that. And when I've been part of those, going in and talking to customers in the past and visiting facilities. You know, I would have loved to have had some of these AI tools that would allow me to generate transcripts on the fly so that I didn't miss any of those quotes. Often, ethnographic research is best conducted on video, and you know, when you're capturing everything on film, but so many people feel uncomfortable with that. But if we can make sure that we're using these tools to capture more information, then sort of do the downstream analysis. You know, as long as we're present, I get the sense that we're in a really strong place, right?

Being Present to Build Relationships and Check AI

Jasmine Gruia-Gray [11:07]

So I think you bring up a really important point about being present, to continue to build relationships with with the people that are providing the VOC, as well as having a firsthand understanding of the discussion from the VOC to be able to check what the AI summaries are giving you back. Because, as we all know, there are points of hallucination. There are analyzes that it falters on, and if you're not present to have that first hand experience, you wouldn't be able to check that effectively, absolutely.

Matt Wilkinson [11:47]

So if we only remembered, you know, one thing from from this article, what should it be?

Jasmine Gruia-Gray [11:52]

AI can tell you what a customer said, right? It's, it's capturing those notes, effectively, only you as the product manager, as the marketing manager, can understand what it meant in that context. The difference between a strategic product manager and one with better tools is whether you were in the room when the Insight was built.

Matt Wilkinson [12:17]

I love that. And of course, in any role, with whether we use AI or not, we're still responsible for the work that we deliver, and so we really should make sure that we're we're there, we're present for capturing that information and making sure that we really do understand why we're proposing things, rather than just outsourcing everything to the AI.

Six Major Shifts Hitting Life Science Marketers in 2026

Jasmine Gruia-Gray [12:35]

Matt life science, marketers face six major shifts in 2026 buyers now trust peers over vendor claims. They expect websites that answer their questions without friction and use AI tools to find solutions before ever contacting sales. Success now depends on account level metrics rather than lead volume strategic investment in events with measurable ROI and measurement systems that track actual revenue influence the gap between knowing these trends and building the capability to respond will determine who wins this year. What inspired this article. I'm so curious to hear your thoughts.

Matt Wilkinson [13:24]

Well, I love to keep up to date with and what everybody's predicting for the coming year. What what really inspired this was that it's very unusual that everybody's predicting things that sort of correlate so so succinctly. So you know, Forrester, McKinsey, Gartner and IDC all converged on pretty similar themes, all independently and all within weeks of each other. Maybe that's a sign of groupthink, I don't know, but those six themes really hit home, and so I figured it was worth sharing. So trust has become our only defensible moment in the sciences. We know that peer review is our most important form of kind of social proof, followed by key opinion leader recommendations and your and also your own. You know lab mates and the people that you work with recommending things. We know that poor buyer enablement really does cost deals. So people love to self serve. They love to be in a scientist in particular, trained to do research in the library, now online, but trying to sort of find out answers themselves. So we really need to enable buyers to come to a sales conversation with a hypothesis, and if we don't give them those tools, you know we're failing them right from the start. But AI is changing the discovery process. It's not just about content creation, but most people are starting to use AI in actually looking for answers. And while that may not be everywhere across the sciences, it's definitely a trend that's coming in with these shifts how people are using AI. It's also changing the way that people are engaging in our websites. We know that we're getting more no click searches, so people not visiting websites because they've already got the answers. But it also means that we're sort of changing the way that what's known as a marketing qualified lead has become more of a vanity metric, and we really need to be looking at sort of account level engagement, because it might be the one person from a company's gone in. But actually, recent research has shown that the buying group, certainly for bigger purchases, are getting bigger and bigger, and that adds complexity. And so we really need to be making sure that we're getting a view across who's engaging across an account, rather than just at an individual level. We know that human connection is really, really important, and so we know that that investing in connecting with individuals in target accounts, it really does prove ROI and it steals as well, if you're not doing that effectively, and that, and all of those things mean that our measurement frameworks need to be rebuilt and looked at again. I mean things like cookie deprecation and other changes around privacy, as well as sort of AI kind of creating this sort of opaque wall about who's engaging with things really means that we really need to shift what we're measuring and how.

From Golden Age to Platinum Age

Jasmine Gruia-Gray [15:58]

Yeah, all fabulous points for me, what resonated the most was the human connection in combination with trust. I'm sure that lots of folks are like you and I, where it's one thing to use AI to do the homework, the background checking, it's another thing to look at websites and read the materials and the data, but it's something completely different to be in front of a salesperson, a field application specialist, and really have the story behind the data and that human connection of that field application specialist using the technology that, to me, is is irreplaceable. So I'm curious what resonates the most for you, and could you sort of elaborate on that a bit more?

Matt Wilkinson [16:53]

I think it's actually the fact that so many of the things that we held true in sort of the what we might want to call the golden age of digital marketing are kind of coming to an end. 15 years ago, we were sort of hailing the idea that we can email lots of people, that we can get social media to engage with lots of people. All of a sudden, we could reach people in different ways. We could track we could get ROI off marketing far better than we ever could before. That's kind of disappearing, and I think we're moving into a more complex world, you know, that's, that's, you know, there's more channels, there's more opaqueness again, but I think it's also means that there's a simpler element to it again, which is now that customers can come to a conversation far more prepared than ever before, because of all these tools. We really have to be the sense makers, and it's something that I talked about at SAMPS in Boston, but we really have to point bring a point of view. We really have to make sense of what our offer is in the context of our customer. So that means that we have to do our due diligence, we have to do our research onto the customer. We have to get to know their problems, and we have to spend time really understanding what it is that's going to make them successful, so that we can be building the trust as the trusted partner, not just for providing the right solution, but for providing the right platform for them to be successful. And I think that's the really important thing. They want somebody that's going to not just be here's, here's a kit that works, or here's, here's something that's going to answer this part of the solution. They want somebody that's now going to be able to walk side by side with them and take responsibility for making sure stuff works.

Jasmine Gruia-Gray [18:27]

What I'm interpreting from what you're saying is we as marketers and product marketing teams need to go back to the basics, the basics of understanding your persona, who your target audience is, understanding their pain points, the gains that you're providing and telling a coherent story. Is that right? Absolutely.

Matt Wilkinson [18:52]

I think there's then the additive level of this, where, with AI, we can now start using those tools to not just tell one story or a few stories based on segmentation, but actually looking down at getting far more targeted with that context, and being able to really understand what is going to matter to individual companies and targets as well as the individuals in them. And I think that that then gives us a huge opportunity to use AI, if we do it right, to connect far better with people and make sure that our offers are far more personalized and far more tailored towards the customer itself. So hopefully if we do this right, we'll be able to use AI to actually do a better job of connecting with other human beings. And I think that's where I would hope that commercial teams in 2026 end up.

One Opportunity to Focus on in 2026

Jasmine Gruia-Gray [19:43]

Fabulous. What is the one opportunity you suggest someone in marketing or product marketing dig into further?

Matt Wilkinson [19:53]

We know that marketing is a broad church, so I would look at the thing that is going to make the most difference to the customer. So look at, how do you interact most with your customer, whether that's in person, at events, whether that's online, and look at those key touch points with the customer. And really focus on, how can I make sure that with all of this change, we're trying to make sure that we're doing the best possible job for our customer.

Jasmine Gruia-Gray [20:17]

So what I've learned in this discussion is we as marketing and sales organisations need to pivot from sort of what was called the golden age of digital marketing to maybe what we can call the platinum age. The Platinum age being hyper personalization, hyper connection and staying human, even with the perhaps foundation of AI behind it all. So that was a fantastic discussion. Matt, thanks so much. We talked about two areas. One is the Janus threshold and cognitive offloading. And as part of the blog post that I wrote, I created this framework called PHASE that helps product managers and product marketers think about how and when to use AI versus how and when to be present in the room and prevent cognitive offloading. And then you gave us five or six areas where we should be thinking what 2026 is going to look like, and maybe pivot from the golden age of digital marketing into what we might want to call the platinum age of personalisation and staying human. Thanks again for a fantastic discussion.

Matt Wilkinson [21:43]

Thank you. Thank you for listening to a splice of Life Science marketing. We hope you enjoyed the episode.

Speaker 1 [21:49]

If this conversation helped you, the single biggest way you can support the show is to subscribe and leave a review on YouTube, Spotify or Apple podcasts. We'd really appreciate it, and it makes a huge difference.

Matt Wilkinson [22:04]

You can find out more about us and the topics we discuss at strivenn.com or on LinkedIn. Thank you so much for listening. We hope to see you next time.

Q&A

How do I apply the PHASE framework to my next customer interview without overthinking it?

Print the five questions on a card and ask them 48 hours before your interview. Purpose: what am I trying to learn? Hypothesis: what assumptions am I testing? Action: could this change a decision? Solutions: might alternatives surface? Endorsement: will I defend this to R and D? If three or more answers are yes, block your calendar and show up. If fewer than three, delegate to AI and review the transcript yourself within 24 hours while memory is fresh.

What's one practical way to shift from lead volume to account-level engagement this quarter?

Pick your top 20 target accounts and create a simple dashboard tracking engagement breadth, not depth. Count how many people from each account touch your content, attend events or engage with sales in a 30 day window. Set a threshold of three people minimum before labelling an account as actively engaged. This shifts your focus from individual MQLs to buying group activation and reveals which accounts have momentum versus isolated interest.

How can I use AI for VOC analysis without losing the insight that builds conviction?

Attend the first three customer conversations yourself, even if AI captures transcripts. Use AI to generate themes across all ten interviews, but personally review every transcript where the AI flagged a contradiction between what customers said and what they described doing. Those gaps are where product pivots hide. Schedule a 90 minute session to map contradictions on a whiteboard before presenting findings. This hybrid approach gives you volume with conviction.

What's the fastest way to test if my website enables buyers properly before they contact sales?

Run a 15 minute test with someone outside your team. Give them a realistic purchase scenario and ask them to research your solution without contacting anyone. Watch where they get stuck, what questions go unanswered and how long it takes to find technical specs, pricing signals or proof points. Record the session. The friction points they hit are exactly what your real buyers experience. Fix the top three blockers before investing in new content.

How do I make a case for event investment when budgets are tight and ROI is questioned?

Reframe events as account acceleration, not lead generation. Before your next conference, identify 15 target accounts you know will attend. Pre-schedule meetings with at least two people per account. Track post-event pipeline velocity for those accounts versus accounts that didn't attend. Compare deal cycle length and win rates over 90 days. Present results as cost per accelerated deal, not cost per lead. This shifts the conversation from expense to strategic investment in shortening sales cycles.

Shownotes and transcript

Shownotes

Face-to-face events still matter. In an age of AI-generated content and digital twins, the SAMPS Boston conference proved that real human connection drives breakthrough learning.

This episode is for life science marketers and sales professionals navigating AI adoption while keeping customers at the centre. Matt Wilkinson and Jasmine Gruia-Gray unpack lessons from the SAMPS annual conference, including why AI's greatest value isn't automation - it's exposing the deficiencies in your current workflows and helping you understand your customers better.

What you'll learn:

• Why cautious optimism about AI is widespread, but real adoption remains narrow
• How AI reveals deficiencies in processes, not just improves efficiency
• The power of AI role-play for sales training and why Matt scored only 50% on his first attempt
• Why cognitive offloading prevents real learning and how to absorb intelligence data effectively
• Customer experience lessons: handoffs between AI and humans must be seamless
• The three key takeaways from SAMPS Boston 2024

Chapters:

[00:04] Welcome and SAMPS Boston overview
[04:15] Surprising learnings about AI adoption
[06:26] AI reveals deficiencies, not just efficiency gains
[08:42] Learning styles and AI personalization
[12:52] Customer experience across the entire journey
[17:59] Walking in your customer's shoes
[21:46] Three key takeaways from the event

Keywords: life science marketing, AI adoption, customer experience, sales training, SAMPS conference, AI role-play, prompt engineering, life science sales, CX strategy, B2B marketing, buyer intelligence, workshop facilitation

Watch the full episode, subscribe for weekly insights, and visit strivenn.com for more resources on AI-powered marketing strategy.

Transcript

Matt Wilkinson and Jasmine Gruia-Gray reflect on the SAMPS Boston conference, exploring how AI is transforming life science sales and marketing while amplifying the need for genuine human connection.

Welcome and Event Overview

Speaker 1 [0:04]

Hey, Matt, how are you? I'm good. Thank you, Jasmine. How you doing good?

Jasmine Gruia-Gray [0:09]

Good. Welcome everybody to another episode of Splice of Life Science Marketing. This is actually going to be our last episode for the year. I can't believe how fast the time is gone.

Matt Wilkinson [0:21]

I know 14 episodes in I think it is. And you know that'll draw to the end season one, and we'll start Season Two at the beginning of 26 which will be really exciting.

Jasmine Gruia-Gray [0:31]

We should have confetti flying around now to celebrate.

Matt Wilkinson [0:34]

Or we should be wearing Christmas jumpers. All right, I've

Jasmine Gruia-Gray [0:38]

got my reindeer earrings. That's as close that as I'm going to get right now.

Matt Wilkinson [0:42]

That's fair enough. As you can see, I'm still wearing grey, so I need to go and grab a red jumper. If we're going to do anything, I'll spare myself the embarrassment of that.

Jasmine Gruia-Gray [0:53]

So today we wanted to talk about an event that you and I were at last week, that's the SAMPS or Sales and Marketing Professionals in Life Sciences annual conference that took place December 3 and fourth in Boston, and the theme was about the human AI advantage. And you were one of the speakers and also one of the workshop facilitators.

Matt Wilkinson [1:22]

Yeah, it was a fantastic event. And I know that you did a lot of work putting together the agenda and working to put it together. It was so well organised. The room was fantastic. We had views over the Charles River and sunset just from up the top of that the hotel, the Hyatt Regency, was just phenomenal. But, yeah, it was really, really good event. Had so many exciting conversations, and the sorts of conversations that you couldn't have with a nail that was, that was exciting to get out of this little room here and get to see you in person, but also so many people in person. It was a really great event. And ever since, my LinkedIn has certainly blown up with just how much positivity from the event and how much people enjoyed the whole the whole day. It was really, really good.

Jasmine Gruia-Gray [2:05]

I agree, the energy was really, really high. The buzz in the room during the networking event, as well as during the breaks, during after the presentations, was just so palpable. And in fact, there were times when I had trouble getting everybody to get back into their seats to kick off the next presentation. And I think similarly, those kinds of conversations don't happen over Zoom or over Teams meetings, those kinds of opportunities where you can listen into some other conversation and join another conversation doesn't happen when we're all working remotely. So there's a lot of power still, even today, in meeting in person and coming together as a community.

The Growing Need for Human Connection

Matt Wilkinson [2:54]

Actually, I think it's probably going to increase the need for those I think as we see more AI generated content and the quality of video, the quality of imagery that AI can create, we may even see AI avatars joining meetings and being a digital twin of us and having some knowledge of what we you know what we're doing, and being maybe able to ask questions at least, about the status of projects and things like that. The need for that human to human connection is probably going to increase.

Jasmine Gruia-Gray [3:23]

I hope so. Also, thought where you were going to go with this is, you know, as people are starting to not trust what's real or what's AI created, the only reality, in fact, is when you meet in person.

Matt Wilkinson [3:42]

I think that's going to be a huge part of it. I think there's already a bit of a backlash in some places about what is, what is real, what isn't. And while I think there are huge advantages of using AI and applying AI to solve challenges, I think that there's going to be a big distrust of certain media as well. So I think it's a case of those face to face interactions becoming more and more important.

Jasmine Gruia-Gray [4:05]

Over time. I certainly hope so. I enjoy that. Was there anything that you found surprising over the day and a half?

Surprising Learnings About AI Adoption

Matt Wilkinson [4:15]

I thought, what was really surprising? I know that you started off by sort of asking people's opinions about AI, and where they sort of sat on a scale of being an AI sceptic to being all in most people kind of had the same sort of opinion of cautiously optimistic about AI, the same sort of thing that we found it when we're interviewing people at ELRIG. So I thought that was that was interesting. I think the other thing that was interesting was that a few people changed their opinions and became a little bit more positive about AI. But I think what really surprised me was the number of people that sort of thought I need to do a little bit more now, and how the talks and the day really opened their eyes to what was possible and what we could be doing with AI, if we, if we just look around it. So that was, that was what's interesting at companies that are so on the cutting edge. What they do, and yet maybe not cutting edge at applying the tools that are available to them.

Jasmine Gruia-Gray [5:06]

Yeah, what was surprising for me was how wide eyed everybody was at what was possible, you know, during your workshop, and especially during Andy Crestodina's workshop, where he would talk about a particular use case and then give folks prompts to try that particular use case in their own situation. And it was almost sort of like this childlike Wow, oh my God, look at this. I just built a castle out of Lego kind of feeling in the room, which I found surprising. I somehow thought that more people would have had had those experiences already. I think

Matt Wilkinson [5:53]

that there's a level of adoption of AI where people are quite comfortable using it for writing text, but I don't think people are using it in the same way, as, you know, in a create persona, Persona AI, and then I'm going to use that to check my work, to check what's on the website, to use it to create content. I don't think people are putting disrupting entire processes that they're looking at kind of point solutions. And I think that, I think a lot of people are still using AI as very narrow tools rather than sort of seeing the systemic disruptive effects that it really can have.

AI Reveals Deficiencies, Not Just Efficiency

Jasmine Gruia-Gray [6:26]

Yeah, I like the way Andy Crestodina summarises this. It's not so much about the efficiency, it's about the deficiencies that AI can point out and help you resolve.

Matt Wilkinson [6:41]

Oh, absolutely. And I think it shows as well the deficiencies in the existing processes. You know, one of the things that I absolutely love about AI is, you know, when we're talking about persona, we struggle to put ourselves in the shoes of the customer and understand whether that's whether that's the persona, or actually the person that we're speaking to at the other end of a call or in a meeting, we struggled to be able to bridge that gap. And so I think that with AI, we can really show, well actually, here's the spectrum of all space. This is where our personas sit. We, you know, within our ICPs, here's where their personalities are. And let's look at how we can get closer to them. And I think it really helps us to show that deficiency of our understanding, of our ability to move towards the customer and really be able to solve their problems and their needs and really be able to communicate how they want to be able to communicate it too. And I think the other thing that I learned through trial and error and quite an embarrassing public failure that I showcased was how even when you build an AI role-play for a sales that even though you've built it and you've specified the targets that you can miss them so wildly, because I only scored 50% on that test the first time I did it, and that was not made up. That was me genuinely trying to do a sales pitch and see how I did. And I'd created the persona and the scenario within Claude AI, and yet I missed the mark by so much on that pitch and and I showed the snippet of the best bit, which still showed a failure. But I think to me,

Jasmine Gruia-Gray [8:11]

that was a hugely valuable example of how you need to go back and forth. It's not just you know prompt, get the right prompt, and out comes the answer, and off you go to generate the next set of tasks. It's really a dialogue and and it there's a continuous improvement loop that I think a lot of people have been missing out on, that you really brought to the fore with your example?

Learning Styles and Cognitive Offloading

Matt Wilkinson [8:42]

Yeah, I think there's also the false comparison as well. We look at AI, and if AI doesn't give us the right answer first time, it doesn't work, it's not good enough, which is fair. But then there's the human in the loop that's maybe not giving it the right information either. But then I think we also have to look at ourselves and how many humans are perfect. I know I've certainly never written anything myself before, AI that didn't need some spell checking. In fact, when I was writing my PhD thesis, I somehow managed to add an extra character. So it was it apostrophe s, it was the wrong version of the word, and I had to go and do a global change, because I'd made this mistake time and time again, because my fingers just somehow made a mistake. Now that's pure human error, but we expect the AI to be better than that. And that's what's really interesting, that kind of we expect those outputs, and I really hope to show is that we can use AI, not just to create things for that are outputs that are better, but actually how we can help use AI to help ourselves get better, both from role playing, but also how to learn better and more in our own modes as well. And that was something that really struck me, was that by getting all this information about account intelligence and buyer intelligence and personalities, I could get all of this massive data and I could overwhelm myself with just too much information that I couldn't absorb. And even though I'd read it and think I've understood it, I haven't actually taken that in. You know, we talk about cognitive offloading quite often, that we give that the AI jobs to do, but then we're not really absorbing that information ourselves. We haven't done the hard work. And I think that's something that I really noticed by through that, through the creation of that talk, that actually by creating that, that scenario where I was going to go out and pitch as to a potential customer of theirs, and I was going to go through this process, that what I realised is, actually I don't learn as fast as maybe I think I do, and that really being able to absorb that level of detail, you have to go through different learning modes to really absorb it, to be able to get better at this stuff. And you can't just expect to, oh, the AI's got all this information for me. It's at my fingertips, yes, but it's not on the tip of my tongue. I've not learned to, I've not absorbed it. And I think that's something that's going to be that's going to be really, really interesting as we go forward into this sort of Age of AI where real expertise is going to show by the ability to actually absorb and communicate that information.

Jasmine Gruia-Gray [11:04]

I've had the same, same experience about learning about myself and my learning style, and through learning about that, I actually am prompting differently. I'm asking for it to provide more combination information, both written and visual, those are my learning styles, not as much auditory. And I think, I think you're right, there's so much power, yes, in the education system, but even as professionals, in having AI understand our learning abilities and customising to that so that in turn, we actually elevate ourselves as professionals.

Matt Wilkinson [11:50]

Yeah, I mean, I know that I learn by doing one of the best things that I do I could Yes, I can learn by reading and I can understand, but I learn best just by doing and experimenting and and that side of things. So I find that if I can combine different modes of learning, so reading, listening, maybe watching a video or something, and getting those, those different combinations, and then being able to practise as well, that's really where I get the most value. So I love experimenting and learning from the experiment. I mean, I guess that speaks to the scientist in me, but yeah, that that kind of feels that that's where that combination of modalities really, really benefits. And of course, with AI now and tools like Notebook LM, you can create those other modalities. You can create the quizzes, and AI gives us tools that we didn't have even just three years ago. And that's, to me, is just such so powerful, and something that I think we're, you know, this getting overlooked in so many of those conversations about, oh, AI is going to steal our jobs. Maybe get better at our jobs.

Jasmine Gruia-Gray [12:52]

I agree. The quizzes have been fascinating. The flashcards, I really like, the flashcard function in Claude and in ChatGPT, those, those flashcards, have taken the place of the post it notes I used to have on my desk and on my wall. So, yeah, I think there's, there's so much potential that you and Andy Crestodina surfaced at the SAMPS meeting the the other learning I had was from Stacey Sherman, who was the keynote speaker, and she spoke about customer experience or CX, and her point is that there is a role for AI in CX, but keep in mind that there has to be a workflow connection between that AI and the ultimate human. They're not two separate departments, two separate entities.

Customer Experience Across the Entire Journey

Matt Wilkinson [13:52]

Yeah, when you when you look at service design and service design theories, I think that one of the big problems that we the organisations, typically face, and Stacey did a great job of highlighting this and sort of bringing it to life. But I think one of the challenges is that we often have discrete departments where information doesn't flow across those those areas. And I think very often what we find is that with AI, at the moment, AI doesn't have the agency that a human can have. But very often, we get frustrated with customer service if we find customer service because they don't have the agency to fix the problem either. And so one of the challenges, I think, is with organisations, is making sure that we're able as customers to get through to somebody that has agency to resolve the problem. And actually that resolution or problem may only to be, be to make you feel heard. I think there's a great example from Simon Sinek who says it doesn't matter that he can or can't fix the problem, but can you make it feel like you've heard and that somebody's tried to do it and try to solve your problem? And that's a big difference, you know, rather than just getting the sort of computer says no response, but let me have a look and see what we can do. I'm so sorry we can't book you on another flight. There's just nothing else we can do today. But here's this, and let me see if I can do something for you, and really just sort of being able to sympathise and understand the human condition behind the problem, that can make all the difference in how we feel about the situation we've encountered.

Jasmine Gruia-Gray [15:25]

Yeah, I think it's also around the handoffs from AI to the human, while years and years ago, those handoffs from one human to another were not efficient. Over time, they became more efficient. And so the customer's expectation now is that the handoff between AI, your chatbot, whatever, whatever form of AI you're using, and the human is going to be efficient. There's there's no longer a customer understanding that, okay, there's a gap. There. We get it. You know, technology is technology? No, no, that's that's not an excuse anymore. And I think she really surfaced that customer pain point quite highly that I think a lot of people in the audience may have either forgotten or didn't realise was happening.

Matt Wilkinson [16:22]

And I think it's really interesting, because you made me think of some stories I've heard of Jeff Bezos, CEO of Amazon, who would say that you can look at data, or you can look at the anecdotes, and quite often it's the anecdotes are correct that there are problems that the data doesn't show, or the aggregate data doesn't show, but the anecdotes really will show up. And sometimes it's really important for us to try and be our own customer, to try and phone up the helpline and see what happens. And I wonder how many CEOs in our space have ever done that, have ever actually phoned up and said, Hey, I've got a problem with this, what's going on, and actually being able to try and resolve a problem on it. So I think that sort of undercover boss, that undercover experience, as it were, is so important. Because actually, I think often the aggregate data that gets reported up doesn't surface those challenges. And so I think that there's a real opportunity for us through AI, just really focusing on who is our customer and what is important to them, is going to be really, really important. And in the age of AI where information is going to be democratised and shared, and where experiences are going to be spoken about, and the AI that we use to do our searches is going to find out about those experiences, we actually need to be far more focused on customer experience. And what does it mean to be a customer, and what does it mean to our customers, to be a customer and for that loyalty? So I think there's a huge opportunity for us now to really focus on making those experiences not just okay, but really something magical, as Stacey said. And you know that that's going to be more and more important.

Walking in Your Customer's Shoes

Jasmine Gruia-Gray [17:59]

It's walking in your customer's shoes. I know that's a really, really old saying, but it's never been more relevant as it is today. What is that experience? Whether it's a new workflow or whether it's an old workflow, walking in those shoes and putting your customers hat on is so, so important. You know, what I've done in the past is divide my team up into Team A and Team B, and once there is a new workflow, have one of those teams experience that new workflow, you'd be surprised how many times you press a different button than what you intended to press when the workflow was designed, or you don't see a certain section on the menu when you thought, Geez, this is a no brainer. It's obvious when you're looking at it through a different lens.

Matt Wilkinson [18:56]

I think it's really interesting to do that exercise and really be able to understand, you know, what are those behaviours? I did quite a bit of hidden needs research during my MBA, and we and we looked at what those behaviours were, and tried to understand some of those customer needs. But also, what are those customer work around? And one of my favourite stories was that a European manufacturer called Miele, and they make, you know, home equipment. And one of the things that they did was they studied asthmatics and people with dust allergies. And so they actually watched them vacuuming their hurt their houses. And what they realised was that people would be cleaning the same spot over and over again because they didn't know when it was clean. Dust particles are charged. And so they did a very simple to a charge metre within the within the airflow, so they could see when the spot was clean or not. And so by doing something simple like that, you could really change your customers workflow, because rather than them having to clean the floor 10 times, they could clean it once and go, Oh, it's clean. Now you. And just those little things can make such a big difference. And so I think really being able to focus in on those customer needs across the entire service journey, from understanding what the product needs to be to actually understanding how service delivery should be is so important. You know, I did some research and when I was leading a big e-commerce transformation, and one of the things was, was that when people were ordering oligonucleotides, that there was a huge amount of stress involved in actually copying the sequences from one document to another for placing orders. And partly that's because, particularly when you were selling into diagnostics labs, and that these are bigger, big orders, if they got one letter wrong, that was a big expense. But also could be a long time to get the manufacturing right, to then be able to get through the validation, the testing and everything else, it was a big deal. And so by simply making reordering easy, we got rid of that stress for them entirely. And so there was sort of this, these things where you can, you can look at those needs and those sort of service experiences and go, You know what? We're making this really difficult for somebody without intentionally doing so, even though it's easy to think, Oh, we've just copy and paste a cell, well actually from one Excel sheet to another, or one Excel sheet to an ordering system. But actually, some of those transformations can be incredibly difficult to do. And so if we can make sure that we understand what our customers need and that whole service journey, we can make life so much easier and build so much customer loyalty and and brand affinity.

Three Key Takeaways

Jasmine Gruia-Gray [21:32]

So speaking of making things easier, let's, let's round out this conversation by making it easier for our audience to understand what were the three take home messages that you got from SAMPS last week?

Matt Wilkinson [21:46]

Well, I think that the first one has to probably come from Stacey, which would would have been the customer experience. Is the entire journey. We know, we all know this, but the way that she articulated that in a beautiful example and sort of case study was really, really powerful for me. So that was the first the second, it was probably from my own talk and and that you almost have to earn the right to educate. So by trying to bring humour and entertainment into that journey, I really felt that made a big, big difference. And it's something that I've worked on a lot this year, thanks to Mark Schaefer's public speaking course really sort of taught me to try and be as audacious as possible, but also to try and make sure that you're breaking things up to earn the right to educate. So that was something that I thought was really important. And then I think finally, my last takeaway would be probably from Andy's workshop, which was just seeing people light up, seeing the potential of being able to use AI in different ways. So those were probably my three big things, apart from, of course, just the fact that you'd organised a fantastic event and that connecting with people is just incredibly important.

Jasmine Gruia-Gray [22:53]

Yeah, yeah. For me, my takeaway with Stacy was that it's everybody's job to think about customer experience, from the CEO to the intern. You don't have to have a specific title. You don't have to be in a specific department. It's everybody's role to put that customer hat on and look at it through the customer's eyes. From you. It was all about the power of workshops and the power of learning and practising what you learn. The whole idea and the scenario you created around a competitor has just launched a new product a little bit before you're going in to make a major sales pitch. Was such a real world experience that I've had many of my colleagues experience and the way you involved everybody by rolling up their sleeves and creating a value proposition, redefining the pitch felt very real world, and to me, was a take home of we should all be doing these workshops on a much more routine basis. From Andy, it was about the word engineering in the term prompt engineering, it's always tweaking, it's always refining. It's going back and forth with your LLM. It's using it and then realising, no, that wasn't quite right when you you try and implement it and go back to the LLM was, was a real take home message for me. Yeah.

Matt Wilkinson [24:38]

And that's interesting, because, you know, we saw people talking about prompt engineering being the big next job, and I think that that that sort of drive has disappeared maybe, maybe it's just fallen out the news, but it's something that certainly I took to heart very early on, about trying to engineer and optimise the process of using AI so I'd get repeatable outputs. And so it's been really interesting for me to sort of watch a room full of people kind of come to that real same realisation. AI is, is incredibly intelligent, for wonderful word, I mean, it's artificial intelligence, but it's also incredibly stupid. So if we give it bad prompts, it's going to give us bad outputs. So it's, you know, garbage in, garbage out, but if you give it sort of repeatable, really helpful inputs, and you're choosing that the most appropriate model, you'll get pretty repeatable outputs. And I think that's a really powerful takeaway for me, that there's still a lot of AI adoption, there's a lot of lot of learning for organisations to go through and sort of that adoption and adaption, or, you know, journey agreed.

Jasmine Gruia-Gray [26:10]

And there is no perfect prompt, and that's often why I end almost every one of my prompts these days with Ask Me Anything before starting, because it will know what gaps it needs to fill before delivering the output you want. That's that's my, my little tip for the for this episode.

Matt Wilkinson [26:10]

Yeah, I love that. And then I go back and recycle multiple times Exactly.

Closing

Jasmine Gruia-Gray [26:17]

Well, this has been super fun. Thank you so much Matt, not only for being my partner in crime on this podcast, but for just a spectacular presentation and workshop at the SAMPS event last week.

Matt Wilkinson [26:32]

Well, thank you so much, Jasmine. It's been it's been fun, and thank you for the invite to go to Boston. It was great to be a Britain Boston and to to misquote the late great Paul Revere, with the bots are coming. The Bots are coming, and I think we can say that they're already here. So thank you so much.

Jasmine Gruia-Gray [26:48]

And thanks everybody. Looking forward to seeing everybody in the new year on another episode of a Splice of Life Science Marketing. Bye for now. Happy holidays. Everybody. Happy holidays.

Q&A

How do I start using AI role-play for sales training without overwhelming my team?

Pick one upcoming high-stakes pitch and create a simple persona in Claude using your customer research. Have your rep practise the pitch against the AI, then review the transcript together. Start with 15-minute sessions once a week. The key insight: expect to score 50-70% initially - that's where the learning happens. Document what the AI catches that you miss, then incorporate those patterns into your prep process.

What's the fastest way to identify deficiencies in our current workflows using AI?

Choose one repetitive process your team complains about - proposal writing, competitive battle cards, lead qualification. Feed the entire current workflow into Claude and ask it to identify friction points and unnecessary steps. Then ask for a redesigned version. Compare the two outputs with your team in a 30-minute workshop. You'll surface 3-5 concrete improvements within the first session that can be implemented immediately.

How can I avoid cognitive offloading and actually learn from AI-generated intelligence?

After AI generates account or buyer intelligence, force yourself to synthesise it in your own words. Create a one-page summary by hand or record a 2-minute voice memo explaining the insights. Then use Claude's quiz function to test your retention. Schedule this review 24 hours after the AI generates the brief. This multi-modal approach - reading, synthesising, testing - ensures the intelligence moves from fingertips to tongue.

What's one practical way to improve AI-to-human handoffs in customer experience?

Map your current customer journey and identify every AI touchpoint. For each handoff to a human, test it yourself as a mystery customer. Record where context gets lost. Then create a simple handoff protocol: AI must pass three pieces of information to humans - customer intent, conversation history, and specific pain point mentioned. Implement this in your CRM notes or ticketing system. Test monthly with real scenarios.

How do I get my team to move beyond using AI for text generation to more strategic applications?

Run a 90-minute workshop using Andy Crestodina's approach: identify one strategic challenge your team faces - persona validation, competitive positioning, or content strategy. Then work through it together live with AI, showing the iterative dialogue. Let people experience the "aha" moment of AI revealing deficiencies in their thinking. Follow up with office hours twice weekly where people can bring specific use cases and work through them collaboratively.

Shownotes and transcript

Shownotes

Most buyer personas end up in a drawer. You spent weeks on workshops, interviews, and research - then created a pretty PDF that nobody opens again. What if that document could talk back?

In this episode of Splice of Life Science Marketing, Jasmine Gruia-Gray interviews Matt Wilkinson about PersonaAI - a process that transforms traditional persona documents into interactive synthetic customers powered by AI. This isn't about replacing human insight. It's about making that insight usable every single day.

Who this is for: Life science marketers, product managers, and commercial teams who want their buyer research to actually work for them - not gather dust.

The key idea: Static persona documents become powerful strategic tools when converted into AI-powered synthetic customers you can query, challenge, and create with.

What you will learn:

• Why traditional personas fail after the initial project ends
• How to build synthetic customers using LinkedIn profiles and AI reasoning models
• Practical applications: website reviews, sales pitch rehearsal, content creation, and product development
• How Matt used PersonaAI to prep for a keynote and received harder questions from the AI than from the live audience
• The role of voice-of-customer data in enriching your synthetic personas
• How to personalise ABM campaigns without being creepy

Chapters:

• [00:00] Introduction to PersonaAI
• [01:03] Why Matt created synthetic customers from personas
• [02:48] What is a synthetic customer?
• [04:45] The problem with traditional persona workshops
• [09:18] Pressure-testing a keynote with AI
• [12:05] AI-powered sales role play and scoring
• [13:35] Static PDFs vs interactive synthetic customers
• [16:33] Product management and user requirements
• [20:59] The PersonaAI offer - $1,000 in 48 hours
• [24:01] Overcoming the "too busy" objection
• [25:47] Account-based marketing applications
• [27:40] Building around feelings and needs that endure

Keywords: buyer persona, synthetic customer, AI marketing, life science marketing, PersonaAI, account based marketing, voice of customer, product management, sales enablement, biotech marketing, ChatGPT, custom GPT

Watch the full episode, subscribe for more life science marketing insights, and visit strivenn.com to learn how PersonaAI can transform your marketing strategy.

Transcript

In this episode of Splice of Life Science Marketing, Jasmine Gruia-Gray interviews Matt Wilkinson about PersonaAI - a method for converting static buyer persona documents into interactive AI-powered synthetic customers. The conversation covers the limitations of traditional persona approaches, the technical process behind building synthetic customers, and practical applications across marketing, sales, and product development.

Introduction to PersonaAI

Speaker: Jasmine Gruia-Gray [0:04]

Hello, Matt. Hi, Jasmine. How you doing today? Good. Thanks. How are you? I'm good. Thank you. I'm good. Well, welcome to A Splice of Life Science Marketing, everybody. In today's episode, we're going to do something slightly different. I'm going to interview Matt on a synthetic AI process. It happens to be a process that he's developed and that he calls PersonaAI. So ready, Matt?

Speaker: Matt Wilkinson [0:36]

Absolutely. Okay.

Why Matt Created Synthetic Customers

Speaker: Jasmine Gruia-Gray [0:40]

So you've built buyer personas for years using traditional methods - workshops, interviews, desk research. I think we've all been there. What made you decide to turn those static documents, some of which I have to admit are in my drawer, into interactive AI assistants? And when did you realise that this was something other companies needed?

Speaker: Matt Wilkinson [1:03]

It dawned on me pretty quickly after the launch of ChatGPT 3.5 and I think it was then, when four came out, custom GPTs became a thing. And one of my friends had essentially created a group of different developers to talk to, and then somebody else had created a different group of consultants, and I went, we can do this with personas. So while people had put in some very basic groups to kind of help guide them, I thought, well, why don't I use these detailed persona documents that we created?

And so I created a persona group, or a buying group, and very quickly found that with some careful prompting in the back of that, I could get them to act as synthetic customers, and that was great. And I could get them to very quickly test messaging and test strategy that I'd already created. And I could get them to argue amongst themselves, which is really fascinating to watch. The AI essentially arguing with itself, the different personalities within an AI persona group arguing amongst themselves.

And so that was kind of the first step. But the thing that I think really changed the way that I looked at this was when I realised I could use it to create content, and then when I realised that we could actually disrupt the process of generating the persona in the first place. And that really came from the launch of the reasoning models and the deep research capabilities. And when that happened, I started realising that we can replace a lot of that manual work with a lot of AI work. And that was really when the big shift happened.

What is a Synthetic Customer?

Speaker: Jasmine Gruia-Gray [2:37]

That's fantastic. So maybe we can take a pause here for a second and just describe what you mean by a synthetic customer.

Speaker: Matt Wilkinson [2:48]

Essentially, what we do is we put those flat, boring personas into the AI, and it's essentially creating a RAG or a retrieval augmented generative AI system. And with some clever prompting, we get that information about our customers to behave as if they are a synthetic version of our persona.

And with that, as long as we've generated information related to the questions we're asking them - what's important to them, what are their buying needs, what stands in their way of achieving a certain job, what jobs do they have to be done - all of those great questions that I know we talk about a lot. If we've covered all of those within our persona, we can then use that and interrogate the AI, and we can use that to help guide brainstorming sessions, come up with strategies, come up with messaging, come up with creating content.

And it breaks my heart when I know that personas I'd created for you in the past never got looked at again after being used in the project. So that process made me realise that we can turn these documents that hold loads of great information into something really, really useful.

The Problem with Traditional Persona Workshops

Speaker: Jasmine Gruia-Gray [3:59]

While I had admitted that my personas, whether you helped develop them, or I worked with another group, ended up in the drawer, PersonaAI has never ended up in the drawer for me. So yeah, I agree with all of what you've said. So moving on to my next question, most small life science companies can't afford six-figure persona research programmes that go into excruciating depth on the personalities and so on of personas, but they can't afford to guess at buyer motivations either. How does PersonaAI solve that tension for teams with limited budgets and even less time?

Speaker: Matt Wilkinson [4:45]

So if I go back to the standard process, and it maybe helps, we can then look at how I've tried to disrupt that process, to make great results more achievable. So the standard process very often involves getting a big group of people together on a workshop, whether that's in person or remote. And typically you'd be asking, you start off trying to identify an ideal customer profile, and then within those ICPs, who's involved in the buying group, what kind of job roles are involved in that buying group, and what role do they take in the buying process.

And so in the room, you typically want to have your sales and your marketing and your customer services people. And even if you take the external costs out of that equation, the costs for those people's time is immense. Now there's huge value still to be had for getting everybody involved on agreeing who are we selling to. And so that strategic alignment is absolutely vital.

But the next step always seemed incredibly vacuous to me. People would end up in a workshop trying to answer questions, and very often they'd end up looking at a LinkedIn profile of somebody that they sold to and basing their responses off kind of, oh, we know a little bit about this. Here's a LinkedIn profile. Let's just type some answers out into a question, and then they present their persona. And very often, after that process, you'd go away and take that and the organisation supporting that effort would go away and try and embellish it and make it nicer, and then you would create a pretty PDF and there's a static document that would then get used to help inform the rest of the project, whether that be branding, inbound marketing, product development, whatever it is.

For any of those projects, understanding who your customer is is really important. But that kind of falls down a little bit when people are having to make the psychological jump from going, this is what a document says, therefore this is what I need to do. And I think that's why they get forgotten.

So that's kind of the old process, and that's why I think those personas have limited value, not because of the information process, but because actually people still have to do too much thinking to make that jump. And AI is brilliant at helping make those correlations and those connections.

So the process that I've been through still involves some of that upfront agreement. It really does help to know who you're trying to sell to, who's involved in that. And I think that strategic alignment is really important. But then the process that I developed involved getting as many LinkedIn profiles of the type of person you're trying to sell to, whether they're customers, prospects, or just people that we think we want to sell to.

And then, using a long-winded prompt and 40-odd questions, I try and get the AI to answer, using one of the reasoning models, deep thinking, deep research, and going away and researching that. There's a lot of acronyms for what AI stands for, whether it's artificial intelligence or whether it's just average information. And I love the one that says it's average information, because here we want average information, but we want specific average information. We want average information about a subset of people.

So we get the AI to study a group of LinkedIn profiles and create an average of them. And what's great is that it doesn't just look at here's this career history. It looks at everything they've posted. But we can then take that, and I will get maybe a persona document with 10-point font, just to give a scale, that can be six pages long as an Excel sheet. So these things can be huge. You can have loads and loads of detail, and that detail will be absolutely useless for a human to use, because we can't cope with it. But the AI loves that context. The AI absolutely thrives with that context.

So we can do that, but we can go even further. If we have transcripts of conversations we've had with customers, we can add those in. We can embellish them further. So there's a process that you can do to really get a great starting point. And I think those personas work better than most I would have created previously. But then you can embellish them with real-life information, real-life thoughts, feelings, quotes, and really get a deep, detailed perspective of who your customers are. And then once you've understood who your customer is and generated one of these, all we have to do is put that into the AI itself, and all of a sudden we've got that synthetic customer that we can then interact with through the chat window. And that's really where the process kind of changes.

Pressure-Testing a Keynote with AI

Speaker: Jasmine Gruia-Gray [9:18]

So I'd like to pull on that thread of how we can interact with the AI. I love the story you had told me just before you were giving your keynote or preparing for your keynote in Washington, DC this summer, where you had used PersonaAI or Atlas to interview you to pressure-test your message. What did that conversation reveal that traditional speech prep wouldn't have?

Speaker: Matt Wilkinson [9:49]

I was able to use - I'd created personas for who I thought was going to be in the room and who my targets in the room might be. And so that was the first piece. Clearly, I'm trying to market to those people anyway. So that was helpful because that had a dual purpose there.

Then what I was able to do was to upload my slides, provide a transcript of my talk as well. I practised my talk to myself, and then I was able to ask the AI, based on who you are, what questions are you going to ask me? Where are the gaps in what I'm saying, where don't I make sense? So rather than this being a generic "these are the holes," it's actually "these are the holes that are going to be important to people I want to sell to, the people I want to influence, the people I want to connect with."

And so that's really where that difference happens. I got far harder questions out of the AI than I actually did in the room. Now, did it ask me every question from the people in the room? Absolutely not. But it had two roles. One, was it able to ask me some really interesting questions that I could then respond to, and I could answer those just by pressing the microphone on and actually speaking back to it, get the transcript and then put that in. So that was helpful.

Point number two, that then turned into a great blog post anyway. So that was helpful. That's a really fun way to create some content. But the third thing I think it really did was it highlighted that there were things that I needed to make sure that I said to bring people on the journey that I wanted to take them on. And it gave me the confidence that when people were asking questions that I probably already covered about 80% of them, which was true, and that meant that the questions you get asked are maybe on something a bit different, but it just made me feel a lot more prepared.

Speaker: Jasmine Gruia-Gray [11:30]

Yeah, for me, it's the same thing. It's about this feeling of being prepared. I mean, you're never going to know all the potential questions that somebody might ask you, but the AI is a pretty good approximation, as long as you've trained it accurately and got a good sense of who your target audience is, whether you're preparing for a speech or whether you're a salesperson and you're preparing for a visit to a new client, for example.

AI-Powered Sales Role Play

Speaker: Matt Wilkinson [12:05]

That brings me beautifully onto the talk I'm going to be giving next week in Boston at SAMPS. And the talk I'm giving actually shows - walks people through the process of how you can use AI both to research and then prepare for those conversations. So while I don't go into a lot of detail about creating personas as such, it does talk a lot about the personality side of it, and then uses tools where you can actually create those personas in conversational AI programmes, where you can create personas and then actually pitch to a buying group and get Q&As from a buying group, and where you can actually engage - they will then talk back to you, and you have a conversation with them, and they then grade you against key metrics.

Now, of course, for me, I'm practising, I'm creating the whole thing, so I'm saying I need to hit these milestones. So you might think that I would, if I've created this as a scenario, actually do pretty well, but actually, my first attempt at that, I only got scored 50%, and we know that if you're a salesperson and you're only hitting 50%, that's a fail.

And so it was really interesting to see that even though I'd created all this information and I'd been through a process to try to read it, actually what I really needed to do was to use the AI to help me learn and digest it in ways that I would absorb it better, so that I could then take that into another role play and be able to score higher.

Static PDFs vs Interactive Synthetic Customers

Speaker: Jasmine Gruia-Gray [13:35]

Yeah, there are so many places we can take this role-playing idea, and maybe that's an idea for another podcast. But another part of this whole concept with PersonaAI is that it is intended to be a synthetic customer, and not back to the old static PDF that gets shoved into a drawer. Walk us through what actually happens when someone uses PersonaAI versus pulling up a traditional persona. What's the difference in practice, not just in theory, and maybe we can use a marketing example this time instead of what we just talked about in sales.

Speaker: Matt Wilkinson [14:18]

Sure. So let's start off by imagining that we're looking at a web page. I've got a new product that I'm coming out with, and I need to make sure that it appeals to my persona. Well, I pull out my persona on one document, and I've got my web page on the other, and I've got me in the middle, and what I'm having to do is I'm having to try and read the persona document, put myself in the shoes of that person, and then look at that web page through that lens of that persona and see, how am I actually answering those questions? And I have to keep bringing myself out of myself into the shoes of the persona to do that.

Now, if I'm editing something on a web page, that's maybe not so bad, because it's a constant comparison. If I'm then creating something from scratch, let's just say I'm writing for a specific persona a blog post, or I'm writing that page in the first place, I'm constantly having to shift who I am, to try and be in the persona. And that's a really difficult leap for most people to do, to put themselves in the shoes of that person, and remove their own perspective, their own bias from that.

AI is really good at doing that. And so if you're using it with AI, rather than you trying to do all of that manual mental gymnastics - you know, who am I in? What role am I playing on this piece here? And am I looking at brand tone? Am I looking at what the persona wants? All of that stuff. Actually, using PersonaAI, and a more complex version where you go through a number of stage gates, what I've called Atlas on the website - what we're able to do is really use the AI as "I want to achieve this, this, this, and this." You give it some specific instructions as to what you want it to do, and it puts it through the filter - the persona, and then maybe the brand style guide, whatever else, maybe there's some rules we need to adhere to, and it will jump through those hoops for us.

So the outputs, they're not just generated by the AI, but what they are is actually generated by the AI for that persona. And so we're getting something that's really close to where we need to be. Is it going to be perfect? No, but it's going to be a heck of a lot quicker to go from A to B and then edit and then do that double-check than it is to go through and try and do that mental gymnastics for yourself.

Product Management and User Requirements

Speaker: Jasmine Gruia-Gray [16:33]

The other use case that I think is often missed is using personas and PersonaAI in the product management role, especially when you're trying to put together user requirements. You've got lots of voice-of-customer input that you then have to synthesise and describe as user requirements to hand into R&D in order to generate the functional requirements. How do you see PersonaAI helping in that way?

Speaker: Matt Wilkinson [17:07]

Well, I think it helps in a really similar way, in the fact that - and I think in some ways, with the voice of customer, and you're looking at user requirements, it's even more important. When we're collecting voice of customer, we'll hear quotes, we'll have certain bits of information that maybe resonate with us personally, but maybe become overweighted in the way that we look at things. So using the AI to synthesise all of that information, maybe thematically, and analyse it before we go into the persona - that's really important.

I've been doing some work with friends at the University of Leicester and some others who are actually going through and using that thematic analysis approach. It allows you to analyse data in ways that most organisations just don't have the time or the bandwidth for. To give you some perspective, what probably took about eight or more human days to complete in terms of thematic analysis by going painstakingly through and coding each of these statements, and making sure that you're putting those into the right models, you can accomplish that in about 15 minutes with AI.

Even more, what you can do is you can actually go away and ask what sort of questions people are asking. What are people saying online about these things as well? So you're not just getting voice of customer from the interviews and maybe your CRM and those sources that you've got internally, but you can also look at things online. What are people saying? What sentiment are they about these problems? So you can actually enrich your data from what people are talking about online as well.

So you can get a lot of that information, you can then build that into essentially your synthetic customer that you're going to then be able to interrogate through the life of the product. And of course, then if you're getting voice of customer and feedback at all, which I would strongly recommend - by the way, I don't suggest that we ever use these tools to replace human input, but rather to represent that human input and to keep us on track.

So if we've used it to get through the first phase gate of a development project, and then we go to the customer and say, here's what we've got, let's get some feedback on this. Once we then update our persona with that extra information, we're able to carry that forward, and it becomes like a living record of how we really resolve customer needs and really make sure that we're meeting their jobs to be done at every stage of our development process.

Speaker: Jasmine Gruia-Gray [19:34]

And then there's the opportunity to have a dialogue with the AI and ask it questions. Something simple, like, should I state the user requirement this way or this other way? Which is more accurate based on the voice of the customer? And then once the R&D team has had a chance to put the functional requirements together, you can input that data as well to augment the PersonaAI and ask, what's missing? It sounds like a simple question, but this is now your personal assistant and your helper to make sure that at this very important and expensive phase in product development that you're getting it right.

Speaker: Matt Wilkinson [20:22]

And I think the other beauty of this is that some people will want to see personas, they'll want to see requirements. They want to see different things out of that document. And the translation from one document to another may be imperfect, and then the interpretation of that may be imperfect. We don't have to do as much of that if we actually base it off of a synthetic knowledge base, a synthetic customer. Because even though we still need those documents signed off as part of a rigorous product development process, we're able to go back to that persona, that synthetic customer, and ask it questions. Am I interpreting this wrong? And that becomes really important and really powerful.

The PersonaAI Offer

Speaker: Jasmine Gruia-Gray [20:59]

So you're offering fully built PersonaAIs for $1,000 delivered in 48 hours. That's a radically fast and inexpensive offer compared to traditional approaches. Some people out there might be sceptical and say, what's the catch? And what does someone actually get at the end of these 48 hours that's really of value?

Speaker: Matt Wilkinson [21:31]

Well, the first thing you don't get is a pretty PDF. You do get a PDF, but that PDF is actually of the output - the data that goes in the back end. We want to make sure that that's validated. Typically, that might be somewhere between four and six pages long, so people get the chance to check that there's something real in the back end.

And then the other thing that they actually get is, depending on the systems that they use, we can either build it inside their Copilot environment, or custom GPT so they can access - and anybody with a paid ChatGPT account can access - or can even build these things as skills or projects in Claude. So we can use these in multiple places.

What they get is actually the real-life version of - well, a synthetic version of their customers that they can then engage with. That's what they get. If there are specifics that they want to get, the inputs are pretty simple. There's a series of questions that I would need to ask somebody. I would definitely need to make sure that I've got a series of LinkedIn profiles that they provide me. But if there's also VOC that they want to give me, other bits of information, they just have to provide that. And we use the AI to convert all of that into a really rich design.

Speaker: Jasmine Gruia-Gray [22:44]

And then I think the key is that you then need to use it for a myriad of different opportunities like we've already discussed. Help me review my current website. How well am I articulating the positioning, the value proposition? Are your needs as the buying persona being addressed on this web page? Again, lots and lots of questions you can keep asking it, and it will provide you with suggestions on how to improve.

Speaker: Matt Wilkinson [23:20]

Yeah, absolutely. I think that's the key - that we can then engage with them in such a nice way that it just makes life easier and quicker, and it makes these personas that so many of us go through those processes for - it just makes us able to engage with them in a way that's actually helpful rather than a hindrance. And I think one of the reasons why so many personas just end up rusting away on a hard drive somewhere is that it's hard work keeping them up to date, and it's actually hard work using them. So if we can make that easier, it just makes life easier for everybody. And it increases the value tenfold or more.

Overcoming the "Too Busy" Objection

Speaker: Jasmine Gruia-Gray [24:01]

So here's a scenario. A lean marketing team might think I barely have time to execute. Why would I spend time talking to an AI about my buyers? How do you respond when someone says they're too busy to use the tool that could save them time and, more importantly, make them more efficient?

Speaker: Matt Wilkinson [24:23]

Well, you can take a horse to water, but you can't make it drink. So that's the first thing. But it's so easy when you show somebody an example of what these can do. So I can very quickly build a persona that just knocks the socks off of what most people would be able to build themselves using the process that I've developed. And then by being able to build that into the AI, quickly show how, even with no extra training, we can turn pieces of content that are technically sound but maybe not focused on our persona, into something that is focused on our persona. So we can really quickly show that. We can quickly say, hey, look at this on the website and go here. And that's really easy to do.

Account-Based Marketing Applications

Speaker: Jasmine Gruia-Gray [25:09]

So we talked about how PersonaAI can be helpful for website reviews. We talked about it in terms of how it can help salespeople prepare for a pitch and prepare for objection handling. We talked about it in the context of product management and the use case in user requirements and functional requirements. And you now just mentioned helping to review marketing materials and brochures. Any other examples? Maybe some examples on how it can be used in your personal life?

Speaker: Matt Wilkinson [25:48]

Yeah, well, I can definitely talk about the personal life, but I think that the other great example that we haven't touched on today is account-based marketing. And I gave a webinar for SAMPS - some of this feels like it's a running advert for SAMPS today, so thank you, SAMPS. It's great to sponsor and be a member. But yeah, gave an account-based, AI-empowered ABM webinar, and showed how we can then use these tools - at least synthetic customers - to take generic marketing that's focused at just one of our buying group and cascade that out into multiple messages.

And how we can then go even further, if we really want to target individuals, using personality AI tools like Humantic AI, to then further understand within our persona - actually, what are the groups that we're looking to target? The personalities of the different people that might be in there. So we can do a whole range of things there that allows us to go from very generic marketing through to something that's feeling very personal and really hitting on the jobs that those people need to complete, the jobs to be done, without being kind of, hey, we've seen you visit X, Y and Z, and you've gone to this trade show and you published this, without feeling like we're being stalkerish. So that's one of the things I think is really powerful about this. We can personalise without being creepy.

In personal life, I have to say, I don't trust the AIs too much with my personal conversations. I still, as much as I love working with AI, still have some boundaries with it. But I think that you can get advice as to what's going to appeal to them, what's going to appeal to your persona, and so that really becomes the key thing. And again, talking about giving talks, getting up in front of people, being able to engage with your persona can give you a huge amount of confidence to be able to do that.

Building Around Feelings and Needs That Endure

Speaker: Jasmine Gruia-Gray [27:40]

Yeah, couldn't agree more. Maybe we can end with this. I just finished reading Mark Schaefer's latest book on how AI changes your customers. If our audience hasn't had a chance to read it, I strongly encourage it. You can see lots of dog-eared pages here and lots of ideas in the margins. But I wanted to read one sentence because I think it really encapsulates what PersonaAI is about: "In an age of AI, don't build your brand around features that are constantly evolving. Build it around feelings and needs that endure."

Speaker: Matt Wilkinson [28:22]

I think that's very, very powerful, and that whole book is amazing. And I think it's absolutely right. These personas should not replace the customer. But if we can use them to capture customer needs, customer feelings, and then cascade that through the process, we can really use them to focus on what's important to them. And I think that's how we make a difference, certainly in this first stage of AI.

Speaker: Jasmine Gruia-Gray [28:48]

Completely agree. This has been a fabulous discussion. I don't even think it was an interview. So thank you for your time, Matt, and thank you for creating PersonaAI and Atlas, which, by the way, more information is available on the strivenn.com website. And want to thank all our listeners for listening to another episode of Splice of Life Science Marketing.

Speaker: Matt Wilkinson [29:14]

Thank you, Jasmine. And thank you to everyone listening. All right. Bye for now.

Q&A

I only have three personas from an old project. Can I still use PersonaAI?

Absolutely. Start with what you have. Upload those existing persona documents into a custom GPT or Claude project. Even basic personas become more useful when you can ask them questions directly. The AI will work with whatever context you provide - and you can enrich it later with LinkedIn profiles, call transcripts, or survey data as you gather them.

How do I get started if I don't have budget for the full service?

Build a basic version yourself. Gather 10-15 LinkedIn profiles of your ideal customers. Use a reasoning model like ChatGPT or Claude with deep research to analyse them and generate a detailed persona document. Then upload that document as context for a new chat. You'll have a working synthetic customer within an afternoon.

What's the first thing I should test with my synthetic customer?

Your homepage headline. Ask your PersonaAI: "Based on who you are, does this headline speak to your biggest challenge? What would make you stay or leave?" You'll get specific, persona-grounded feedback in seconds. It's a quick win that demonstrates the value before tackling bigger projects.

Can I use this to prep for a single important sales call?

Yes - and you should. Upload what you know about the prospect: their LinkedIn profile, company information, any previous correspondence. Ask the AI to roleplay as them and challenge your pitch. Request the three hardest questions they might ask. You'll walk into that meeting far more prepared than winging it alone.

How do I keep my synthetic customer from going stale?

Feed it regularly. After every customer call, win-loss interview, or trade show conversation, add relevant quotes or insights to your persona document. Even a monthly update with fresh voice-of-customer data keeps your synthetic customer sharp and prevents the drift that makes traditional personas obsolete.

Shownotes and transcript

Shownotes

Your team is using AI right now. The question is: with or without you?

 

In this episode of A Splice of Life Science Marketing, Matt Wilkinson and Jasmine Gruia-Gray unpack findings from their ELRIG Drug Discovery 2025 survey of 107 life science exhibitors. The data reveals a troubling pattern: organisations are buying AI tools, but adoption remains fragmented, unguided, and risky. Most concerning? 38% of employees in organisations without AI programmes are already experimenting personally - becoming what Ethan Mollick calls "secret cyborgs."

 

This conversation challenges the assumption that access equals adoption. It explores why light users hold the key to AI growth, how the Dunning-Kruger effect shapes power user confidence, and why software-as-a-service companies are outpacing traditional life science vendors in AI maturity.

 

What you'll learn:

• Why 44% of survey respondents have tried AI but aren't using it daily - and what that means for commercial teams
• The "secret cyborg" phenomenon: how employees are adopting AI without organisational guidance or guardrails
• Why AI readiness matters more than budget - and how structured programmes drive 53-73% daily usage rates
• The cognitive offloading risk: what happens when we stop challenging AI outputs and exercising our mental muscles
• How SaaS companies achieved higher AI maturity than tools, reagents, and automation providers
• Practical steps to close the confidence gap between light users and power users through AI clubs and train-the-trainer models

Chapters:

• 0:04 - Introduction and survey context
• 1:30 - The light user paradox: gym memberships vs AI adoption
• 3:27 - Are power users overestimating readiness? The Dunning-Kruger effect
• 5:31 - Cautious optimism: what 68% of respondents really feel about AI
• 8:52 - Cognitive offloading: the hidden risk of AI dependency
• 11:03 - Why correlation isn't causation in AI-assisted analysis
• 12:58 - SaaS companies and AI maturity: why they're ahead
• 15:00 - The secret cyborg problem: 38% are experimenting without guidance
• 18:17 - Beyond text generation: where AI sophistication breaks down
• 21:15 - Closing the confidence gap: AI clubs and collaborative learning
• 24:34 - AI maturity as competitive differentiator: WPP vs Publicis
• 27:26 - Creating space for human connection in an AI-augmented world
  
  

Keywords: AI adoption, life science marketing, secret cyborgs, AI readiness, ELRIG survey, cognitive offloading, AI maturity, light users, power users, AI governance, commercial teams, life science vendors

 

Subscribe to A Splice of Life Science Marketing for more insights on marketing strategy, AI adoption, and commercial growth in the life sciences. Visit strivenn.com to explore our AI readiness diagnostic and persona development tools.

 

TRANSCRIPT

Matt Wilkinson and Jasmine Gruia-Gray discuss findings from their ELRIG Drug Discovery 2025 survey, revealing a critical gap between AI access and adoption in life science organisations. The conversation explores why 38% of employees are becoming "secret cyborgs" - experimenting with AI personally without organisational guidance - and what commercial leaders must do to close the readiness gap before it becomes a competitive liability.

Introduction and Survey Context

Matt Wilkinson [0:04]

Matt, hello and welcome to a splice of Life Science marketing. This episode is brought to you by humantic Ai. Imagine if you could close 22% more deals overnight using a buyer intelligence platform. Imagine slashing prep time by 70% and boosting close one deals by 37% that's what customers the world over achieving using humantic AI's account and buyer intelligence system. Today, we're going to be staying on the theme of AI and talking about some of the results that we got when we were interviewing exhibitors in L Riggs drug discovery, 2025 conference in Liverpool. I'm joined today by my co host Jasmine. Hey there, Matt, and we're going to be digging into all of the all of the responses that we got when we were talking to our exhibitors, or the exhibitors of El Rigg up in up in Liverpool.

The Light User Paradox: Why 44% Have Tried AI But Aren't Using It Daily

Jasmine Gruia-Gray [0:57]

Yeah, there are so many surprises in this survey, it's almost hard to figure out where to start, but let's kick it off anyway. So we know that from the survey, 44% of the respondents have tried AI but aren't using it daily. They've overcome some skepticism, but haven't seen enough value yet. Why do you think these light users hold the key to AI growth?

Matt Wilkinson [1:30]

I like to think of the analogy of AI being a bit like a gym membership. You can have a license or you can have a gym membership, but if you don't ever grow, you never grow. And I think that's exactly the same sort of thing. So if you've got a light user, it's not so much that they've they've not got the gear, they haven't invested enough time to start seeing the gains that they would be getting. It's not about getting more information. Everybody knows that the more you go to the gym, the fitter, the stronger you get, and it's the same by exercising your AI muscle, if you will. Really, it's not about telling people how I want it's really about showing them the use cases that matter and then getting them to be able to understand how they can then adopt it for their own bespoke use cases, B to B marketing terms. It's the difference between selling capability and really selling the resolution to a problem. It's a lot

Jasmine Gruia-Gray [2:21]

about how people learn. People really learn by showing each other rather than just telling each other and then expecting you're going to go off as a novice and try it out on your own. So yeah, I think use cases of all varieties are super important, not only to embed it in sort of your current processes, and as we learned about taking the donkey work out of out of what you're doing, this is sort of the mundane stuff, but also helping to stretch and augment what you're doing. Still requires people to show you when for you to be able to ask questions and learn that way?

Matt Wilkinson [3:04]

No, absolutely. I mean, as our friend Dr Lisa Palmer, who's a real AI expert, would say, you know, we have to show AI and not tell it. And I think it's, I think it's really important that people really get the opportunity to roll their sleeves up and become familiar with these, you know, these really, really powerful assistants that can help them accomplish so much more than they could do on their own.

The Dunning-Kruger Effect: Are Power Users Overestimating Their Readiness?

Jasmine Gruia-Gray [3:27]

So do you think these two groups that we're calling regular users and power users are overestimating their AI readiness?

Matt Wilkinson [3:36]

I kind of feel that actually, that the regular users probably not, but I think the power users probably are underestimating their capability. There's something called the Dunning Kruger effect, which means that sort of, the more that you know, the more you realize you don't know. And so the most advanced users of AI that I know actually realize how little we know about how AI actually works, the inner workings of the AI itself. I mean, even the experts that are building these large language models don't really know what's going on within those neural networks. So, I mean, we have to be really careful to understand that they are black boxes that we really don't understand the insides of. And so it's, it's really important for us to realize that actually, as users, that definition can become quite tricky to understand. What does that look like and the well, realms of coding. There's a lot of people that are using vibe coding and using platforms like lovable and others, or people are creating agents in platforms like M A 10 that have multiple steps. But then when you look at the real capabilities out there that some of the advanced Apple people that are applying this, there's a gentleman called Reuven Cohen that is using agent swarms to create vast amounts of code and to check that code. And when you start seeing how people are really using this in practice, you start realizing that even if you I'm a power user within the Gen AI tools for creating text and creating multi step workflows, that actually is an advanced compared with some you know, where some other people are going with this, particularly in the world. Realms of

Jasmine Gruia-Gray [5:00]

coding, I completely agree it's I think the Dunning Kruger effect really summarizes it really well.

Cautious Optimism and the Psychology Behind AI Sentiment

Matt Wilkinson [5:06]

And I know I've been introduced once or twice as an AI expert, and I shy away from that absolutely, because I know how little I know. I think one of the really interesting things that I found was just how people felt about AI itself. And in terms of that, I was surprised that more than 60% said that they were cautiously optimistic about the use of AI. How did you feel people responded to that question?

Jasmine Gruia-Gray [5:31]

I actually wasn't too surprised at this cautious optimism. In part, a lot of sales and FASS are scientists at heart. By the way, we didn't see very many marketers and product managers on the trade show floor, which is why I didn't mention them in the at the beginning here, they're scientists at heart. And as scientists, we're looking for the evidence. We're we build up that evidence and that knowledge and test it out over the course of time, and in many cases, the exhibitors we spoke to were just getting their hands wet on using AI and in thinking about use cases and practicing it out and having others to talk with and brainstorm with. So I think part of that cautious optimism, optimism comes from that that scientific background. I think the other part is that many of whom we saw and spoke with, as the previous slide showed, were light users. So it's almost a factor of, well, I don't know what I don't know. There's also what you hear a lot of in the news. Should I be worried about it taking my job? So that was lurking somewhere in the background, I think unnecessarily. But that's, you know, that's a fact of what, what some people were thinking. I think some of the caution also comes from what people are hearing about guardrails and depending on the organisation. Some some organisation had really well thought through guidelines and how tos, but others did not, and I think all of those things sort of factor into this cautious optimism. Did you see the same thing?

Cognitive Offloading: The Hidden Risk of AI Dependency

Matt Wilkinson [7:27]

Yeah, I actually found this question quite interesting, because as I was asking it, I realized that I probably am cautiously optimistic myself, but actually, and that's how I would have responded, but actually, I'm both excited and optimistic, but I'm also very concerned about the risks. There are risks in terms of the way that cognitive offloading. I think there are big risks about the data that we're giving up to organisations that are owned by some very, very wealthy and powerful people. You know, you just have to look at the really, the race towards AGI and the number of data center, the vast number of data centers that the US is is building in comparison with the rest of the world. And then you start looking at who are the big players that have big vested interests in that, and we're almost concentrating the power of this intelligence in a handful of very, very powerful people, and that that that in itself, carries some risk. And so the more you do to sort of understand the kind of the politics behind who's creating the models, the politics behind who's funding the models, and actually the risks to the economy because of the vast amounts of money that's going into AI development. There are some risks that I think we all need to pay attention to, because they risk not just necessarily our data. And, you know, the potential risks of AGIS going, you know, going mad and wiping out humanity in kind of Terminator style. But there's also the risk of, what does that this mean towards the, you know, the general financial situation of the world? And so I think there's some some economic risk in there as well.

Jasmine Gruia-Gray [8:52]

I think it's worthwhile pausing here for a second and talking a little bit more about cognitive offloading, because some of our listeners may not know that term to me what, what is most important to understand about cognitive offloading is this trend towards people giving up the ability to think and not continuing to exercise that really, really important muscle, so that it becomes an exercise where whatever the AI tells you, you just accept it as fact, rather than constantly challenging it and constantly thinking yourself, what other questions are there behind the information that the AI has given me. Do you have another interpretation of cognitive offloading?

Matt Wilkinson [9:45]

Maybe not a different interpretation. But I think there's two key things that I've definitely seen, and people are starting to make memes on social media about this. And as soon as you start seeing as a meme, you start knowing that it's real. And so I think that the big concern that I have is. Making sure that we're we're still exercising our mental muscles. There are definitely things that we don't need to do maybe as frequently as we can offload some of the drudgery work, but we really need to make sure that we're exercising the things that keep us human. I like to think of AI as a bit like a cover. It's great. They create fantastic things. And actually, when it's really tracked, well trained, and you give it great context, it can do a really, really good job of creating stuff. And actually it can do a better job than most people can, but you still have those geniuses where there's the creative spark, the ability to do that. And in our own right, as human beings, we all have have our own genius. And I think we have to be really careful to preserve our own genius and not offload everything, so that we still have our own opinions, that we still start to think about, how do we do this? There's a fantastic couple of episodes of The Simpsons where they're talking about what they're calling cheat GPT. And again, you know when, when the Simpsons is featuring something that it's become something that you need to pay attention to, as you know, as a societal shift and something that is worth paying attention.

Jasmine Gruia-Gray [11:03]

I think that's well said. I tend to think of cognitive offloading as the sort of but what if situation? It's about constantly asking the what if it's this and not that, to constantly challenge yourself, as well as not accepting what, what the AI has said.

AI-Assisted Analysis: Correlation vs Causation

Matt Wilkinson [11:25]

I think even looking at the results of this report, absolutely used AI to look at correlations across different data, different questions, and that was really, really interesting. AI is brilliant at find correlations that maybe that humans wouldn't find, but it can very easily fall into the trap of assuming that correlation equals causation, and humans do that as well. But I think that, you know, we need to be able to learn from our own experience, to be able to say, hey, this hasn't necessarily caused this. It may be a byproduct of something else. And I think that really then speaks to one of the more unsurprising things that we found was that software as a service companies, those companies that do an awful lot of software development, had, by far and away, the highest level of AI maturity. And so that was then really, really interesting to see. But then, of course, they're working with code, and code has already been one of the most disrupted areas of the industry, where I don't necessarily know that we're seeing huge numbers of developers being laid off just yet, although that may be coming soon, but we are seeing huge increases in the amount of code that's being created by AI. And so I think that that just goes to show that those organisations that treat AI as an infrastructure, non novelty, it really is just baked into their daily workflows, and then they're looking at, well, this is our core product, but we could also use it in sales. We could also use it in marketing, and so it's far more accepted across the organisation, whereas, I think in some of those organisations, where maybe we're more focused on the lab, AI, doesn't impact day to day operations as much, just yet. What was your perception of those conversations?

Jasmine Gruia-Gray [12:58]

I think software as a service or SaaS companies are so much more mature than than the exhibitors, more closely associated with our industry, the tools providers, the reagent providers, even the automation folks, because they've got all the use cases right. Coming back to what we said earlier, people learn from use cases, and it just felt more like a natural fit for them to adopt AI much sooner than any of us had, because they had the proof right in front of them. So even in folks that said their organisation is not using AI or it's not embedded in their processes, 38% of those folks are experimenting personally with AI. It feels almost like a grassroots movement that suggests AI innovation is bubbling up from the bottom. Should leaders identify and empower these internal AI champions, even if they are light users, instead of imposing a top down program. Do you think

Secret Cyborgs: The 38% Experimenting Without Guidance

Matt Wilkinson [14:10]

this is a really interesting observation? It's one that's not new. Those 38% of individuals are what I like to call secret cyborgs. I think it was a term that was coined by Ethan monarch, Professor of innovation at Wharton University, and these secret cyborgs are essentially using AI in private because they're using it in their daily lives, and they're bringing those tools into work. It's pretty much like saying, Hey, we're not allowed to search the internet anymore at work. And so for those people, why would I not use AI? The problem with that is, is that very often, people that are using AI, personally, they don't have paid for accounts. They may not have set up privacy restrictions on them, and they may not have been given guidance around what data they can and can't use in the AI. And so that's really where the real risk lies. So for me, that the first bit is. Is about understanding that this is going to be happening in any organisation. And an organisation that doesn't yet have an AI policy doesn't necessarily mean to say that they don't yet have aI users just because you haven't given people licenses doesn't mean to say people aren't using it. So that's the first thing that's that's absolutely clear to me. Then I think it's the case that we really need to be building both top down and bottom up approaches to usage of AI, maybe even also sort of side on from it as well. So from a top down perspective, what we really need to have in place is to understand a culture of experimentation within the organisation and actually enabling people within a set set of guardrails. Maybe there's some approved tools that you're allowed to use, there are some approved ways of using that and actually encouraging people to be able to use those tools. And one of the most important things about that is, I think it actually helps us to not only start to understand the tools, start to understand what we can achieve and actually benefit the organisations that we work in, but it also helps to allay the fears that we're going to be made redundant because of AI. So that's a really important thing, from a bottom up approach, absolutely identify as AI innovators, celebrate them, celebrate those use cases, and encourage that culture of experimentation, and also write experimentation and making sure that we're doing that. There's some great examples of this across the life sciences. You've got organisations like Promega, like moderna, that were very, very early on in the adoption of AI in various different ways. And they've really, really been able to achieve some fantastic things within their organisations. Speaking with people in Promega, is adoption as uniform as the case study on the open AI, you know, website might say, probably not, but the adoption is there, and everybody's using it, everybody's becoming aware of it, and people are using these things without, without it being in the shadows. And that's really, really important.

Jasmine Gruia-Gray [16:55]

I think this is an opportunity for companies to elevate the culture of collaboration within their companies, and have groups made up of of the C suite, made up of middle managers and people on the ground level to come together and create some initiatives. It doesn't have to be really extreme business cases that require integration of multiple systems, but create an initiative within each department so that you get the collaboration from the users with the C suite and its purpose built for that particular department. And that way, I think you integrate guardrails along with use cases, along with sort of taking the fear factor out of learning it and embedding the so what into your day to day. When we

Beyond Text Generation: Where AI Sophistication Breaks Down

Matt Wilkinson [17:54]

when we asked about what people were using it for? Unsurprisingly, 75% were using AI for text generation after you are for analytics or automation, or maybe they didn't know that. What do you feel that sort of next natural, next step on that sort of sophistication ladder sort of comes into what did you hear from, from folks on the on the exhibition floor?

Jasmine Gruia-Gray [18:17]

It was very natural for people to to to use it for content for content creation, for content editing. I didn't find very many people using it for graphics creation. For example, I didn't find very many people using it for process efficiencies. And, you know, taking the grunt work out of scenarios where you're copying and pasting from one document into another document, or for creating dashboards in that can be updated in real time from meeting minutes that you just completed a few hours ago. So I think part of it is that folks, again, need more use cases. They they need more inspiration for what what's possible before they're going to understand how to go up that, that sophistication ladder beyond content creation,

Matt Wilkinson [19:22]

it's interesting, isn't it? I think the other thing that that really struck me was that people aren't even thinking about more than I'm going to create a piece of content. They're not necessarily thinking about, how do I actually daisy chain prompts or, you know, custom gpts, whatever we want to call them, AI assistants together to achieve tasks and that may end up with still needing to copy and paste text out together. Didn't hear many people really think using it for reviewing or even that much for research, although a few were using it for competitive analysis, which I did think was a really interesting change from maybe some of the things that we found out when we were talking to people in in Washington over the summer.

Jasmine Gruia-Gray [20:00]

Yeah, brainstorming is a fantastic use case. You know, I'm going to put a shameless plug in here for Atlas and Persona AI and creating a GPT that is a synthetic customer that you can then brainstorm with that synthetic customer on your marketing content, on your sales presentation. And here's shameless plug number two, for folks who are in sales and marketing that really want to dig deeper into use cases and how to think beyond the traditional uses of AI, I strongly suggest they attend the upcoming samps conference, the sales and marketing professionals and science conference on December 3 and fourth in Boston.

Matt Wilkinson [20:48]

Yeah, of course. I hope they come as well, because I'll get to meet them in person, which would be fantastic. So how do you think we can sort of help close that confidence gap between light and power users of you know, for myself, because, you know, I've been realized I had to dive as deep as I could into this early on, I find it quite difficult for me to sort of see that, that disconnect between, you know, the fear of adoption. So what can we do to really help people become more familiar with using these tools?

Closing the Confidence Gap: AI Clubs and Train-the-Trainer Models

Jasmine Gruia-Gray [21:15]

Here's where I'm going to combine a little bit of high school with our day to day lives, professional lives, high school, from the sense of we always used to have these after school clubs. You know, there was the chess club, and that's where you got to teach each other moves and learn from each other. So combine sort of a club kind of atmosphere with an initiative in your your office or which ultimately means a train, the trainer perspective. So even if you're a light user, you can contribute to helping others learn how to use AI and build different use cases. And the novice. The beauty of a novice is in asking questions. There's no such thing as a dumb question, especially when it comes to AI. And that way the light user and even the power user help is learning more and can sharpen their skill sets as well. So that that's, that's my challenge to our listeners, built, built an AI club and work together and try it out.

Matt Wilkinson [22:24]

Yeah, one of the things that always surprises me is how few people actually ask the AI how to do that. How to prompt the AI better. When Claude launched its skills, which was in the last month or two, I was quickly came up with the use case of being able to then call that, call my, you know, one of my groups of Persona AI into any chat that I have within Claude, and I didn't know how to do it, so I upload how to create a skill, and I gave it all of the materials, and it did everything for me. It created the database it needed to and then it told me how to go about putting that skill in in the back end of Claude. So then I would have suddenly got a skill that I can use wherever I want to use it. The AI is incredibly helpful. So just ask it to help you do what you want to achieve, especially, you know, with it itself. It doesn't necessarily always have the most up to date knowledge about how to do things within itself, which is always interesting. So you do sometimes have to rely on a web search, but the information is there. And I think being able to collaborate with the AI, and getting the AI to help you get to where you want to go. That can be really powerful. The other thing I found to be incredibly helpful as a top tip for anybody that's struggling to get out of an AI, what they want to get to if they put their prompts in and they've got a first draft of something, and then they get to, you know, they've gone through edits, and they end up in a certain place that's different to the original output, put that final copy back in and say, Hey, this is after we've, you know, been through editing and everything. This is where we ended up help me build better prompts, guide my prompting, so that we would get closer to this, you know, this final output. And by doing that, you actually end up getting to a point where you can build something that's repeatable and really, really robust

Jasmine Gruia-Gray [24:03]

and keep remembering challenge AI's answers. It may not link to what your experiences are. It may not really understand the nuances of what you're trying to get out of it. So use it as a companion. Ask it questions back and forth, just as you're saying. So do you think that AI maturity could become this new differentiator, and differentiate between thriving and lagging commercial teams?

AI Maturity as Competitive Differentiator

Matt Wilkinson [24:34]

Absolutely the ability for people using AI to accomplish things in almost no time, compared with or to compress timelines to create something is just astronomical. You can create websites in minutes. You can create new position. You can interrogate positioning through the eyes of your persona. And if you're using persona AI, you can create new text. You can create new things. Now, of course, there's going to be things about getting it so that it's. Is ready, and that you're comfortable with it, and there's still a need for the expert in that loop. And please don't think I'm ever going to say that. There isn't, but the speed at which you can operate, the volume at which you can create really, really high quality outputs, the way that you can become more focused on the customer through using AI, you know, if you're if you're able to, you know, create customised messages to your different persona within your buying group, which you absolutely can then that's more likely to hit if you're then going to take that a step further and understand somebody's role and then maybe their personality type using a tool like humantic Ai, apologies for the shameless plug, there you'd be. You're able to get even closer to how not just what looked important to that person in their role, but also what's important to how they want to receive information. And so by doing all of those things, what you're really able to do is to accelerate your path to results. And of course, we're still working with humans, at least for the time being, but I think that we really have to be able to look at there's that moment where if we don't jump on board, we can, we're going to end up in trouble. You just have to look at the contrasting fortunes of two of the world's biggest ad agencies. Up is currently struggling and has seen significant amounts knocked off its market value, whereas publicist has invested heavily in AI and has seen a completely contrasting shift in shift. So while WPP has lost about 5 billion euros off of itself, 5 billion pounds off of its share price over the last three, three or four years, publicist has done almost the opposite. And I think that just goes to show where there's other things going on behind the scenes, but that that ability to adopt and adapt is just so crucial for organisations today.

Jasmine Gruia-Gray [26:44]

Yeah, I'd add another word to the adopt and adapt. It's preparedness as professionals. This is, this is the gift that keeps on giving in helping you prepare for a customer presentation and debate with it what objections the customer is likely going to ask. It helps you prepare for an internal marketing presentation. It helps you become more persuasive when you want to ask your manager something for something. So I think that it's absolutely going to be the in all of our professional lives as well as our personal lives.

Creating Space for Human Connection

Matt Wilkinson [27:26]

No, absolutely, I think it's, I think it's one of those things where we're just going to have to be really, really careful about maintaining the personal connections and the creating those spaces to connect with real human beings, but we can use AI to help us get to that and maybe create more space to spend more time with humans, rather than just spending time behind spreadsheets.

Jasmine Gruia-Gray [27:48]

Completely agree, yeah, it is about those relationships and giving us back time to have deeper relationships with with the folks that we care about, as well as new folks that we want to learn about. Well, this was spectacular. Thank you. Really appreciate your insights, Matt and looking forward to seeing everybody again on a splice of Life Science marketing the podcast. Thank you, Jasmine

Matt Wilkinson [28:18]

and thank you for everybody that's listened. You

Q&A

What should I do first if I discover secret cyborgs in my team?

Start with a no-blame conversation. Ask what tools they're using and why they sought them out - this reveals the gaps in your current processes. Then establish basic guardrails: which tools are approved, what data shouldn't be shared, and how to use privacy settings. Create a safe space where experimentation is encouraged within boundaries, not punished. This turns risky shadow adoption into sanctioned innovation.

How can I help light users become more confident without overwhelming them?

Show, don't tell. Pick one repeatable task they do weekly - perhaps formatting a report or drafting meeting notes. Demonstrate how AI handles it in under five minutes. Let them try with your guidance. Once they experience the time saving on something mundane, confidence builds naturally. Avoid dumping 50 use cases on them. Master one workflow first, then add another next month.

What's the simplest way to start an AI club with limited budget?

Schedule a 30-minute weekly session where one person shares a use case they tried. No preparation required - just screen-share and talk through what worked and what didn't. Rotate facilitators so everyone contributes. Use free tools like ChatGPT or Claude. The goal isn't polish, it's creating psychological safety to experiment and ask questions. Start with three people. Growth happens organically.

How do I measure whether our AI adoption is actually improving productivity?

Track time-to-completion on specific recurring tasks before and after AI adoption. For example, measure how long competitive analysis takes in week one, then again in week twelve after introducing AI tools. Also monitor daily usage rates - structured programmes achieve 53-73% versus 31% for unstructured experimentation. If usage stalls below 40%, your guidance or tool selection needs adjustment.

What's the first guardrail every organisation needs before encouraging AI use?

Define what data never leaves your organisation. Create a simple one-page document: customer data, unreleased product specs, and financial information stay internal. Everything else can be used to prompt AI with care. This removes paralysis while protecting what matters. Pair this with a list of approved tools where you've verified privacy settings. Clarity beats comprehensive policies when you're starting out.

Shownotes and transcript

Shownotes

Your antibody copy could land you an FDA warning letter. One wrong phrase on your website transforms a research tool into an unapproved diagnostic device in the eyes of regulators.

This conversation is for marketing managers at life science tools companies who need to promote Research Use Only (RUO) products without crossing regulatory lines. Josh Levin, former FDA compliance officer and diagnostics consultant, explains exactly which words trigger investigations and how to communicate analytical performance without implying diagnostic intent. Words matter: phrases like "used in diagnosis" or "detects disease" create regulatory risk; safer language references literature findings or analytical capabilities.

What you will learn:

• The specific RUO disclaimer language FDA requires and where it must appear
• Red flag phrases that imply diagnostic claims versus safe alternatives that reference published research
• Why written website copy receives more FDA scrutiny than verbal sales conversations
• How to present clinical data and analytical performance without crossing into diagnostic territory
• What compliance language belongs in distributor agreements to prevent off-label promotion
• Three strategic tips for tools companies considering entry into the diagnostic space

Chapters:

• [0:00] Introduction and defining Research Use Only
• [1:26] What RUO means and how FDA determines intended use
• [2:29] Required RUO statement placement and formatting
• [3:58] What manufacturers can and cannot say about RUO products
• [6:35] Red flag words versus safe phrasing for website copy
• [9:23] Framing analytical data without implying clinical validity
• [12:01] Application notes and case study guardrails
• [13:09] How AI might change FDA compliance monitoring
• [14:24] Working with diagnostic and clinical lab customers
• [21:38] Distributor compliance and partner training
• [22:51] Combined-function instruments and software considerations
• [26:13] Three tips for tools companies entering diagnostics

Keywords: Research Use Only, RUO products, FDA compliance, life science marketing, diagnostic claims, regulatory copywriting, antibody marketing, lab developed tests, ISO 13485, clinical lab regulations, tools manufacturer compliance, FDA warning letters

Ready to ensure your marketing copy stays compliant? Watch this essential conversation, subscribe for more regulatory insights, and visit strivenn.com for expert guidance on life science marketing strategy.

 

Transcript

Former FDA compliance officer Josh Levin explains the regulatory minefield of marketing Research Use Only products. This conversation covers the exact language that triggers investigations, how to present data safely, and strategies for working with diagnostic customers without becoming a diagnostic company yourself.

Defining Research Use Only and FDA Intent

Jasmine Gruia-Gray [0:00]

Hey, welcome to a splice of Life Science marketing. I'm Jasmine Greer Gray, and as always, I'm joined by Matt Wilkinson. Hey, Matt, hi. So today, tracking what you can and can't say about Research Use Only products or sometimes that's abbreviated as r u O, and how to avoid tripping the FDA wires. Our guest today is Josh Levin. He has led post market work at FDA, CDRH for molecular and pathology devices, focusing on compliance around Ruo products and lab developed testing as well as working with firms on transitioning their CLIA quality systems to an FDA compliant system. He later drove quality system implementation at pgdx and diagnostics regulatory strategy at A, S, E, L, L and G, s, k, through iron line consulting, he now advises labs and diagnostics teams on regulatory strategy and QMS. Prior to FDA, Josh worked at research product firms for 10 years, including a stint as director of R and D at kpl. Matt, why don't you kick us off? Thank

Matt Wilkinson [1:26]

you so much. Now, Josh, Research Use Only sounds pretty simple, but in one minute, what does it actually mean, and how does the FDA determine a product's intended use in practice versus the sort of totality of claims and context, and not just considering this a disclaimer,

Joshua Levin [1:46]

sure. Well, thank you Matt and Jasmine for the opportunity to speak on this subject. So FDA views Research Use Only as almost a diagnostic product that's in the developmental stage. So you know that because the product is being developed, FDA understands that there might be kits that are being developed for the purposes of researchers to try and use in their research, but there's also an understanding that FDA quality system regulations don't apply at this stage, and the claims that companies can make around these products are very limited. Not sure. I completely answered your question, but happy to elaborate

Matt Wilkinson [2:26]

further. Thank you for that. Now that was that was

Required RUO Statements and Labeling

Jasmine Gruia-Gray [2:29]

really useful. So what specific Are you? Oh, statement has to appear where and does punctuation, capitalization matter for ads, data sheets and slide decks.

Joshua Levin [2:42]

Yeah, so there's a standard statement, which is defined in the FDA regulations. And there's, there's actually a very useful FDA guidance on this. And the statement, I don't remember exactly, but it's something about for Research Use Only, not for use in diagnostic procedures. And this is something that our UL company should have on their labeling, just to make it very clear that they're not promoting the product for diagnostic use.

Jasmine Gruia-Gray [3:06]

Yeah, I've often used this in companies where I've worked and seen it in many others, for example, on antibodies. On the label of the vial, it says Research Use Only, so that that sort of covers it, does it?

Joshua Levin [3:21]

Yes, I think we'll probably talk about how Research Use Only manufacturers tools. Manufacturers can work with their partners who might be using the product and diagnostic applications and and what firms can do to protect themselves against FDA inspection or oversight.

Permitted and Prohibited Claims

Matt Wilkinson [3:39]

I can't wait to get into that. But before we do, what can a manufacturer say and not say about a Research Use Only product? And maybe more importantly, is there a difference between written copy and verbal communications, or are you creating both of those treated exactly the same?

Joshua Levin [3:58]

That's a very good question, especially around the written and verbal communication. So So companies, tools companies, or research use products companies are allowed to talk about the analytical capabilities of their product. So to give you an example, I had a and that should be very transparent to the user. We had a company who we were investigating at FDA, where the company had a combined score of 80 different biomarkers, and they were claiming this was a Research Use Only tool. And we said, Okay, well, what are the 80 markers you need to disclose that to your to your customer, and what the analytical performance is on each of those? And because they were unwilling to do that, we said, this is not a research tool. You're marketing. This is a diagnostic because of that lack of transparency. So tools companies should be very transparent about the analytical performance of their product on the particular marker. So for example, if you have an antibody, you know, being able to show western blots or Elisa data or what have you on performance of that antibody.

Matt Wilkinson [4:57]

So that's really interesting. So you're allowed to talk about the analytical capabilities, and then it's up to the user to go that extra step if they want to use it for diagnostic purposes, they have to then do the proof in the lab developed test. And this is

Joshua Levin [5:10]

where the difference between written and verbal communication comes in handy. So written communication is what FDA is going to scrutinize, and website copy, for example, is probably the most accessible medium to FDA, and where companies tend to get tripped up a lot, because the website copy is often written by marketers. No, no offense, but there's, there's a bit of a desire to overstate what that product can do, and be happy to share some suggestions for how to imply a diagnostic claim and written labeling in verbal communication, you have a lot more flexibility, because the court has ruled, I don't remember the exact case, but there was a court ruling that FDA cannot regulate freedom of speech. So for example, if you have a salesperson saying something to the effect of, well, we we know customers are using this in diagnostics. We're not supposed to be marketing it for that purpose. But you know, you can do what you want with it. You know something that statement like that FDA, FDA can control what a salesperson says. That being said. However, I definitely recommend training your sales staff very closely that they don't cross that particular line and that. Again, I think statements like the one I mentioned are ways you can kind of get around saying what you want to say without actually saying

Matt Wilkinson [6:26]

it brilliant. And that's it. That's such a subtle distinction. And I love that free speech comes in even to a conversation about, Oh, you are brilliant, yeah.

Red Flag Words and Safe Alternatives

Jasmine Gruia-Gray [6:35]

So I'd love to go back to what you said earlier, about how what's written, particularly in very public information, like a website, can trip you up. What are some of the red flag words that you've seen in your experience? What is some safe praise phrasing? What kind of guidance would you give our audience?

Joshua Levin [6:57]

So let's say that you you had this antibody, and the antibody had a had a particular diagnostic use for, I don't know, let's say prostate cancer, just to make something up. If in your website copy, you were to say, our product is useful in the diagnosis, diagnosis of prostate cancer or something like that, that could trip you up, and FDA could say you're making an implied claim. But if you said something like this antibody has been shown to be a useful marker in prostate cancer, and then you list literature references, then you're not saying it's your product, you're just saying that this is what the scientists say. So FDA can't or will be less willing to call you on that, because you're not making a claim specifically about your product, but about this class of products and this particular biomarker

Jasmine Gruia-Gray [7:42]

can are there documents that can help researchers with this phrasing and with how to avoid making claims on diagnostic intent

Joshua Levin [7:54]

happy to use the opportunity to promote iron Line consulting services, where we certainly work with clients on on this issue, you know. And I'll give maybe another example that we worked on with the client where the clients marketing department wanted to claim that they were interference free for a particular interference this was a diagnostic company, but they hadn't performed the testing, so we recommended that they use the language of interference free by design, rather than simply interference free, because we didn't want to imply that they had done the testing. So that's an example of where we can work with clients. I don't think there's anything specifically written. Again, the Research Use Only guidance the FDA puts out as a good general guidance, but it doesn't necessarily talk about specific examples, promote the use of a good regulatory consultant, even for tools firms, to help avoid that particular issue, and it's usually something we can solve fairly quickly. That's that's

Presenting Analytical Data Safely

Matt Wilkinson [8:52]

got me thinking. I mean, I know the words can be interpreted in different ways, but so can data. So if I'm marketing an iuo product, I want to look at analytical performance and talk about some of, you know, some of the performance of, you know, the tool that I'm selling. I could imagine that even that data could actually imply clinical validity. So is there a way to ensure that you're framing that data in a way that isn't implying clinical or diagnostic validity, but it's actually just sort of showing this is the analytical performance

Joshua Levin [9:23]

of the product. Yes, certainly. And it you mentioned, Matt, it's a very subtle distinction. I think, if you're clear that you're you're testing, you're doing retrospective testing on pay, on patient samples, for example, like if you, if you're not implying that this is part of a clinical study. It's especially useful if it's someone else's data. If you have like a research collaborator, and then you're presenting that person's data, it can be a very subtle distinction. So you do want to make sure that the language really avoids any implication that it's a diagnostic tool. Let it start with the thought that this is a research tool and you're showing research. Research data's data in support of your research clients. I think you know, you'll, you'll be in good shape, if you kind of look at it that way. I think firms get into trouble where they try to, they try to be cute, and they know they have diagnostic customers. And you know, again, happy to get into how you might actually work with a diagnostic customer.

Matt Wilkinson [10:18]

I'd love to do that sort of just to dig into sort of something that made me think of there, which was, if I was to, you know, to have a product, you'd look at some retro, you know, retrospective samples. Then if I look at a correlation between, you know, the analytical capabilities of my tool and my test, and shall we say, the diagnosis of condition is that then implying that it can be used as a diagnosed, as a diagnostic, or am I actually just saying, In this study, we showed a great correlation between the analytical capabilities and diagnosis?

Joshua Levin [10:52]

I think that you do want to be careful there. I think saying if, again, if it's sort of like put in a research frame of mind that that we took patients with prostate cancer, we measured them, and, you know, our antibody showed that X percent had had this marker. As long as you're, you know, you're not, you're not going out and and implying with your data that people can then use this product directly for diagnosis. But kind of the goal of the study is a research goal, and that is, it's put in that context. I think that's kind of the mindset to have in mind. But it can be very subtle, because a lot of these tools, obviously are of interest for for diagnostic purposes as well. So

Application Notes and Case Studies

Jasmine Gruia-Gray [11:31]

as as you probably know, Josh, a very common tactic in the r, u, O space is to have application notes, case studies, whether it's from a key customer or a key opinion leader. Are there any guardrails around applying these, these success stories or these application notes and any disclaimers that that need to be included in the messaging,

Joshua Levin [12:01]

I would again promote the use of a good regulatory consultant to screen the language. But I think keeping to the same mindset that you're you're putting out an application note as a research tool. If you were putting it out for diagnostic purposes, you wouldn't be a tools company anymore. I think you know if, it's written by a third party, I think you're a little safer there, because it's the third party's opinion. If, for example, that third party the Kol was to say, I think this product could be useful in the diagnosis of XYZ, that's their opinion. And you may, you may get away with that. It's, it's often a little difficult to know what FDA is going to pick on, because oftentimes FDA gets told about things by competitors. You know, what's, what's the risk of putting that language in your documentation, and how important is it to you? That's what you really have to balance. Do

AI and FDA Compliance Monitoring

Jasmine Gruia-Gray [12:51]

you? Do you think that AI will actually have an impact on what the FDA finds out that they no longer rely on information from competitors, but they actually go searching out for these misses themselves. So

Joshua Levin [13:09]

Jasmine, I think you've given FDA a great idea, because, as you know, FDA is very resource strained at the moment, and you know, there's been, as you can imagine, a brain drain from FDA, especially in the diagnostic group, and they're really struggling to get good people they so they have, even when I was there, we had very limited resources to do compliance, so AI could really potentially help a lot there. FDA has tried to use AI, not entirely successfully, in the review process, but I think in the fact that they took a step in that direction indicates that that your idea might not be too far off about them using AI for compliance, it could actually be very effective there in as a screening tool that would be later vetted by humans you

Working with Clinical Lab Customers

Matt Wilkinson [13:55]

mentioned earlier, sort of looking about how r u O companies can maybe work with, you Know, diagnostics or clinical labs, you know, if I'm selling iuo product and my, you know, my sale, you know, I get a request from somebody in a clinical lab, how do I handle it if I learn a customer is using an r, u, O product diagnostically, is there anything I need to say or do or behave differently if they're if it's obviously an academic lab versus if it's actually a clinical lab that is Using an IUI product in a maybe a lab developed test or something like that?

Joshua Levin [14:24]

Matt, that's a good question. As you know, this is an area I'm very passionate about. I've been in kind of all sides of that. As director of r&d kpl, we were balancing the needs of our research customers and our our diagnostic customers. Then again, at FDA, and then at pgdx diagnostic company. So I can, I can speak to my experiences at a Tool Company at kpl. And there we we knew who our customers were and what they were doing with it. So we would, we would put the disclaimer in very big letters. Now it was a little different, because we sold antibodies for food detection salmonella and Listeria and things like that. Well, was FDA regulated, but not in the same way diagnostics is, but with diagnostic companies, sometimes they would come to us and say, well, we need you to be GMP. And we would say, No, we're not going to do that, you know, but we're ISO 9001 you take it or leave it. And then they would come in and do an audit and satisfy themselves, and then leave. So we knew what they were doing. They knew what we were doing, and our attitude was, whatever you guys want to do with the product that's on you. We're selling it for research. You want to make it into a diagnostic have fun. Try not to make any any bones about us being a diagnostic company, and that was one of the reasons for not going to full GMP with our production at pgdx. We were selling our product as as kind of for beta testing or alpha testing. We did have to train our sales staff. I remember a meeting where they put up a list of our customers for the Ruo product and and they had clinical, clinical, clinical, and so we made them take that off. So don't put that on slide. That's discoverable. We are going to get FDA inspected at some point, and well, with lab developed tests, that's a whole nother situation. And when I was at FDA, we were actively trying to get a handle on lab developed tests by going after tools manufacturers. And you can see this with a couple of recent warning letters. We actually have a blog post about a warning letter that was sent to a Gina diagnostics Well, first of all, the name of the company makes it sound like a diagnostic company. So okay, that there's that, but they had an Ruo product where they were making very explicit diagnostic claims, and FDA called them on that, and then when FDA showed up for an inspection, the quality manager just didn't handle it very well, so they weren't really prepared to deal with that kind of question. So they got a warning letter, and there have been a couple examples like that. We did put a lot of effort into to trying to find some use cases when I was at FDA, and it was very difficult to prove intent. So what I would recommend to the tools company is imagine getting inspected by FDA, and think about how you would handle that and make sure your documentation, and, more importantly, your quality people are well equipped to handle a kind of visit from FDA, because it can happen

Matt Wilkinson [16:59]

that's pretty interesting. So in terms of being able to, you know, you wouldn't necessarily go down the GMP routes, there's actually almost things, from a business perspective, to avoid, to almost protect yourself from, saying, Hey, we're a diagnostics company. There's clearly some, some language that you've just you've just told us that have to be really careful of it still feels like there's a, there's this, there's a there's a large sort of gray space that you have to operate in to be really successful

Joshua Levin [17:23]

here. That's true. I would say that some tools companies do go to the effort of creating GMP products and and they'll tell the IBD customers in vitro diagnostic customers, do you want the GMP product or the regular product? Because FDA is very clear on that component suppliers to an in vitro diagnostic do not have to be GMP. So for example, if you're making that antibody and you're selling it to a diagnostic company, you're not required to be be under GMP. But there is a lot of gray space in that. We FDA knew that these tools manufacturers knew where they were selling to and the laboratory developed test situation, which we could talk for another hour about, is another reason why FDA would like to get a handle on Research Use Only products, because they've been told by a court they can't go after lab developed tests. So that that then gives FDA the incentive to move backward into the supply chain to try to get some control over these diagnostic tests, which laboratory developed, tests not cleared or approved by FDA, make up probably 99% of the diagnostic testing market. So 1% of the diagnostic tests are actually FDA regulated. And so FDA would like to get some sort of handle on this, if they could. And that does put tools companies under the microscope

Matt Wilkinson [18:39]

a bit that's almost frightening, that whatever the lab says they are, where's the accountability in that?

Joshua Levin [18:43]

So the lab position on this is that they're regulated by CLIA regulations. Our position at FDA was that CLIA does a very good job of regulating the laboratory operations and and making sure that patient results are not, say switched, for example, which is a very important thing. So you know, you send a sample into Quester LabCorp, and you want to make sure that test result is is yours, and CLIA does a very good job on that. Where we FDA felt that CLIA wasn't doing a particularly good job, was on the test validation. So the requirements are very weak on what constitutes a validated test. And not to go into some detail, but New York state actually has some more sophisticated requirements. But to make long story short, FDA doesn't believe that CLIA is adequate for assuring develop a validation of a diagnostic test. But the labs disagree that the you know, and many of the big labs actually do have very good validation programs. You know, the very sophisticated FDA approach was, well, not we're not worried about you big labs. We're worried about fact that anybody can do this. So there's been a debate for many years to which the labs currently have the upper hand, both because of the court case and because, frankly, the labs FDA has mentioned doesn't have a lot of people left who can really speak to this issue. Most of us have gone to industry where we feel like we can, honestly, we feel like we can do a lot more good by by helping companies work with FDA.

Distributor Compliance

Jasmine Gruia-Gray [19:59]

So if we can pivot a little bit on the business side of things, as you know, many tool companies use distributors, what compliance language belongs in a distributor agreement? What kind of marketing approvals and partner training is typical to prevent off label promotion of an r, u, O product by a distributor.

Joshua Levin [20:24]

So I do have to admit, this is an area where I don't have a large amount of knowledge, but speaking from the regulatory side, or when I was in a research products company working on the R and D side, it would be, you know, scrutinizing, making sure we had the ability treating that distributor as a supplier and making sure we had adequate supplier controls. We had the ability to scrutinize their language. We had the ability to audit them. For example, you know, putting those things into place. And the tools companies I worked with were ISO 9001 certified, which is a good way to build quality system that lets you perform those kinds of supplier related tasks. So I kind of turned my answer into a quality regulatory answer, which I probably didn't fully answer your question, but some of that I have less experience with the contracting side. Well, I

Jasmine Gruia-Gray [21:09]

think what you said makes, makes perfect sense, auditing your distributors, having some kind of approval process to review the documentation before it goes public, review their their website language before it gets public, goes public.

Joshua Levin [21:26]

Absolutely, I think treating them like a supplier, like you would handle your critical suppliers that way. At least I'd hope tools companies do that. They're obviously not required to. But the supply chain is, is a very important piece of these products.

Combined-Function Instruments

Matt Wilkinson [21:38]

Absolutely, just for keeping on that sort of supply chain, but maybe sort of, sort of looking at usage again. If an iuo reagent runs on an instrument also used clinically, or your analysis software outputs a report that could be perceived to be patient like, what marketing lines can't be crossed before you trigger this sort of the IVD requirement. So

Joshua Levin [22:02]

this is a great question. Matt And FDA actually has a guidance on instruments with combined functions. So if you think about like an Illumina sequencer, for example, or PCR device, those can be used both in IBD and Ruo environments. And FDA wants to make sure the instrument manufacturer has a way to carve that out, you know, in order that the instrument isn't going to report. Going to report out, ru, oh, data in a clinical environment. So for example, if you're selling that product, if you're an if you're an instrument manufacturer, like an Illumina or or bio rad, or whoever makes the PCR device, you're selling the instrument into a clinical laboratory, you're able to keep from from that reporting. It is complicated somewhat by the laboratory developed test situation. But FDA does have a guidance on on how you, you, as an instrument manufacturer, can appropriately close off certain functions for diagnostic customers,

Matt Wilkinson [22:51]

and what sort of functions would those be, just so they can, so they can understand, I'll give an example

Joshua Levin [22:55]

from next generation sequencing, which is probably the technology I'm most familiar with. With a lot of these tests, there's, there's a limited number of variants that the system can report in diagnosis mode. So if you're using but of course, next generation sequencing, you can port out, report out an infinite number of variants for research purposes. So FD will want to help have the instrument designed that if it's being run in diagnostic mode, that only those variants can be reported out. In fact, there's a couple of approvals where FD went further and tiered the analytes in terms of how much evidence there was around that particular analyte marker. So some had very clear clinical data associated with them. Some only analytical data, and some were more preliminary.

Matt Wilkinson [23:39]

That's really interesting. And I love the example, because you've got me thinking about some work I used to do with, you know, with the software company that essentially created a software as a service to be able to look at genomic data. And it's being used in by the NHS and, you know, so it is a in some ways, you'd call it a diagnostic product, but it's when it, you know, it was focused on rare disease, and often rare disease. We don't have diagnostic markers yet. We have Mark variants of interest, if you will. So it isn't, it isn't, it's almost impossible to get it validated as a diagnostic instrument in its you know, tool in its entirety, because what you actually want to do is, okay, we've got these markers, as you say, that could be reported out as a diagnosis, but everything else could actually be something of interest, where they are variants of interest, where, actually, if we get enough data, we might be able to one day say these are diagnostic of this particular rare disease.

Joshua Levin [24:34]

That's correct. There are four companies who've gotten approval under pre market approval for their next generation sequencing system. I mean, in addition to Illumina, that has a clearance for the instrument, but those four companies were asked by FDA to come up with a plan for how they were going to continue to monitor variants and update their their test as more information became available from the variants. And to be fair to clinical labs, clinical labs that are doing next generation Sequencing, especially the good ones, are doing this all the time. They're also updating their knowledge base.

Matt Wilkinson [25:06]

Yeah, it's so important, and it's getting that right balance between being able to help a patient with a diagnosis and actually also being able to continue to further knowledge about what are many times are underserved and understudied diseases. No,

Joshua Levin [25:19]

absolutely. And this is one of the areas where the line between Research Use Only in in vitro diagnostics is very tenuous. And at FDA, we were very interested in this, because there's obviously a lot of there were a lot of companies offering sequencing services, and then there were other companies, as you mentioned, Matt, offering the software. So labs were able to put two and two together and come up with a next generation sequencing, oncology diagnostic we were at FDA a little concerned about how to deal with that particular situation. Again, I think, given that laboratory develop tests are no no longer under FDA peer review, that effort has at the moment, it's not a priority of FDA, but that's something to look for. They look at at companies combining different r, u, O, components to make an in vitro diagnostic, which is completely legitimate if they're bringing that diagnostic through FDA. You know, again, as I mentioned before, there's there's nothing wrong with having Research Use Only components in your in vitro diagnostic, long as you go to fda

Three Tips for Entering the Diagnostic Space

Jasmine Gruia-Gray [26:13]

with it. So to round out our discussion today, which has been fantastic, and it's just surfaced how complex this talk is, but to put you on the spot a little bit, are there three tips you can give to tools companies about getting into the diagnostic space? Well, that's

Joshua Levin [26:33]

a great question, Jasmine. So first of all, I would say, go in with your eyes wide open. And again, I'll give you a brief plug for what iron line consulting does. So we look at four elements, the science behind it, which presumably, is there, if the tools company has been doing good science all along, the commercial goals, which are very important, and what is as a company? Maybe this is one of the three tips I'll give us as as a tools company, what is your goal in getting into the diagnostic space, and really understanding that. And then the other question is, what diagnosed diagnostic Do you want to develop? And why? You know, we often see companies coming to us with great technologies, and we ask them what they want to do with it, and what disease do you want to look at? Because you when you develop a diagnostic, you have to have that sees expertise on your team to understand how customers are going to use your product. So I think that would be one, is understanding is understanding the commercial space. And maybe I'll say four tips, counting the good science, because that becomes even more critical as you go to validation under diagnostic realm, then your quality system is very important. As I mentioned, we think it's best practices for tools companies to be under ISO 9001 that's not always possible, but it's it's a good general practice, and but then how do you elevate that, from that to the medical device quality system, which is ISO 1345, which is a lot more explicit around the design process, and has is tied into risk management and things like that. So that is kind of a step up that tools companies need to take. And I'll give an example of something that happened during the covid era. I was working in a diagnostic company at the time, a cell and we wanted to partner with this, a little tools company that had this really cool technology, and they said they could run their assay in two hours. And I said, How is that possible, given all the all the steps you have in your assay? So they invited me over to see them, and I said, Okay, I get how you're doing it in two hours. You're not documenting anything where, in a medical device company, you're writing everything down when you when you run that assay, where they were not even lab enough, this wasn't even best practices. But anyhow, so like quality system, I think is the third, and then regulatory engagement. What's your strategy around engaging with FDA? We recommend something called the pre submission, which is usually a really good way to engage with FDA, and doesn't cost any money, you know, and it's a good way for companies to tow into the water with FDA and get an idea about what the validation requirements might might be and what the regulatory pathway might be. So I think those are the those are the areas we we would look at. That's a great question. I think those companies are often afraid to take that leap.

Jasmine Gruia-Gray [28:58]

It's a big commitment, both financial, well as culturally. Really, really appreciate all your insights today. Thank you so much for joining us on a splice of Life Science marketing and

Joshua Levin [29:10]

thank you both. I enjoyed this conversation. I appreciate the opportunity you.

Q&A

How do I audit our current website copy to identify phrases that might trigger FDA scrutiny?

Start by searching for any language that directly connects your product to diagnosis, detection, or clinical outcomes. Flag phrases like "diagnose," "detect disease," "for use in clinical," or "identifies patients." Replace these with references to published research and analytical capabilities. For example, instead of "detects cancer biomarkers," use "has been shown in literature to correlate with cancer biomarkers" with citation. Have both marketing and regulatory review every product page, especially antibody descriptions and application notes.

What's the fastest way to train our sales team on safe RUO language without overwhelming them?

Create a one-page comparison chart showing "Red Flag Phrases" versus "Safe Alternatives" with five real examples from your product line. Include the key principle: describe what the product does analytically, not what a doctor could do clinically with the result. Role-play customer scenarios where they're asked about diagnostic use. The safe response formula is: acknowledge you know customers use it in various ways, state you cannot market for diagnostic use, and redirect to analytical specifications and published literature.

If a clinical lab customer asks us to become GMP-compliant, how should we respond?

Clarify your positioning as an RUO supplier upfront. Explain you maintain ISO 9001 quality standards appropriate for research tools, and that FDA regulations do not require component suppliers to be GMP. Offer to host their audit of your facilities and quality system. This allows them to qualify you as a supplier while you maintain clear RUO status. Document everything and ensure your sales team never promotes the diagnostic application. Consider offering both RUO and separate GMP product lines if diagnostic business becomes substantial.

How do I present retrospective clinical data in a case study without implying diagnostic validation?

Frame all clinical data within a research context, never a clinical decision-making context. Use language like "research study on archived samples" rather than "clinical validation." Always attribute findings to the research collaborator, not your product's capabilities. Include the research goal explicitly in your description. For example: "Dr. Smith's research on prostate cancer tissue used this antibody to explore biomarker expression patterns in 50 archived samples." Never state or imply the data supports using your product to diagnose patients.

What documentation should I prepare now in case FDA shows up for an inspection tomorrow?

Ensure your quality team has: clear standard operating procedures for how RUO disclaimers appear on all labeling, documented sales training records showing RUO compliance education, audit trails of website and distributor marketing material approvals, and customer communication templates that reinforce RUO status. Create an FDA inspection response plan identifying who speaks to inspectors (quality manager, not marketing). Keep a clean separation between research and any potential diagnostic activities in all documentation. Review recent FDA warning letters to tools companies to understand current enforcement priorities.

Shownotes and transcript

Shownotes

Most life science tools are complex, technical, and sold to skeptical, data-driven buyers. This conversation shows how to turn that complexity into a story scientists believe—without dumbing anything down. Matt Wilkinson and Jasmine Gruia-Gray speak with Bruce Scheer about simplifying solutions, naming your big ideas, and building a business case that wins over both scientists and the C-suite.

Who this is for: Biotech startup marketers and commercial leaders selling high-consideration solutions with multiple stakeholders and high switching costs.

What this covers: How to collapse sprawling product lines into a human-friendly solution map, craft a buyer-led narrative (Problem → Outcome → Solution), and back it up with economic proof. You’ll also hear why a shared narrative aligns sales and marketing, reduces buyer confusion, and boosts renewals—not just new deals.

KEY IDEA (suggested): Name and simplify your story—lead with the buyer’s problem, paint the desired outcome, then prove economic value to overcome switching pain.

What you will learn:

• A practical method to simplify complex portfolios into 3–4 named solution categories

• How to name your problem, outcome, and solution in four words or fewer

• Why tension between current state and future state is your persuasion engine

• How to build CFO-ready business cases for high-consideration deals

• Using a shared narrative to align sales and marketing and reduce buyer confusion

• Visualizing your story (whiteboards first) to increase recall and internal retell

 

Transcript

In this discussion, the team digs into how life science companies can translate complex, technical products into compelling buyer narratives that scientists and executives trust—by simplifying the solution, naming big ideas, and proving economic value to overcome switching pain.

Translating Complex Tech Into a Narrative

Speaker: Matt Wilkinson

Thank you. A lot of our readers, listeners, sort of market life science tools or products that are really, really complex and technical, and their buyers can be really skeptical, sort of data driven. People completely get the emotion piece. And I'm a big believer in the approach. But do you have any tips on being able to translate something that's really, really technical into a compelling story that still rings true to the scientist, especially when that pain of switching can actually sort of feel like an insurmountable kind of mountain.

Speaker: Bruce Scheer

The pain of switching again, you know, I do start as we're crafting a narrative, and it's complex. You know, I was brought into Alcatel Lucent at the time they had 146 products. And talk about complexity, so we quickly went to simplifying that into a solution framework with four pieces. So all those, all those random, various products, had had a home within a category, four categories. So we had to figure out the solution side of what we were selling, and how do we simplify this in a way that a human brain can wrap their head around it? But then also, still, you know, getting into a core narrative. So with Alcatel Lucent, and working with our head of strategy, he said something that I heard, and I'm going, oh my gosh, that's it, we coined a big idea concept called dynamic enterprise. And the beauty in that is, when we went in to talk to enterprises, we could talk about this big idea of dynamic enterprise, but contrasting that with where they are today, which is anything but dynamic. You know, bureaucratic, slow, lethargic, and that created just an immense amount of emotional tension. And again, the value very often, is in that tension or contrast between, you know, what current state is and what future state could be. But that's typically what I'll do. I'll try to find some core concepts like that. I'll name them, which is a little bit unusual. I don't see other people in my in my field, naming things as much as I do, but I like to name my big problem. I like to name the outcome. I like to name the solution in four words or less. And it used to be three words or less, until Trump ran with the campaign Make America Great Again, which is probably one of the, you know, best campaigns ever run, even though, you know, I, you know, politics aside and fandom aside, I do give him credit. He didn't invent that. Ronald Reagan ran with that way, you know, way in the past. But boy, talk about moving the universe with a powerful phrase, so that helped me break my model into no more than four words or less. But I don't know if that helps in terms of helping people. You still want to get them excited about the potentiality before you start doing all the due diligence and stress testing, and you know, and you know all that detail to follow. Here's another really interesting statistic for you, a 2025, statistic. There's an organization called g2 that does a lot of buyer research, and I met their head of research a couple months ago, and he had just come out with a fresh report with a huge data set. But if you have a high consideration solution, meaning it's disruptive, it's very complex, multiple buyers, expensive, big investment, chances are 42% of those opportunities that you might be selling against 42% of those require C suite check off. And of that 42% 20% of the 42% of that number is CFOs. So if you've got a high consideration solution, chances are one out of five of your deals opportunities require CFO check off 42% of your opportunities at least somebody in the C suite. Very often those people in the C suite are held accountable for fiscal responsibility of their organization. And so, you know, my argument is, you better have a business case for those folks and showcase the economic or the financial value associated with that change. And that's very helpful. And rare is the seller who does that supports the buyer in doing that, hopefully that's helpful. Thank you.

Sales–Marketing Alignment via a Shared Narrative

Speaker: Jasmine Gruia-Gray

If we sort of continue on the theme of organizations and how they think, one of the topics that's very near and dear to my heart is sales and marketing alignment. I actually moderated a session about a month ago at the marketing elements summit in DC on this topic. From your experience, how does a shared narrative help get everyone on the same page, and equally importantly, what kind of outcomes can an aligned commercial team realize?

Speaker: Bruce Scheer

Well, an aligned commercial team isn't very normal. I'll tell you that, based on my 25 years of working with them, normally, marketing is doing what they're doing and banging a drum, and their metrics are around, you know, MQLs or so. They've got their own vanity metrics that they're trying to hit. And then they, you know, throw that over the wall. Hey, sellers, here you go. Here's your leads that you've been asking for. And then the sellers, you know, you know, gosh, it was really funny. I was in a workshop with sales and marketing, because I normally do a cross functional team when I'm building a narrative Autodesk. You guys have probably heard of I had their rock star sales rep in the room, and I'm like, going, Hey, how's marketing working for you? How are their leads? And he goes, Oh, Bruce, I tell one of my SDRs that works for me. I tell him to ignore anything from marketing. And so Peter, the marketing leader, was in the room too. I go, Hey, Peter, did you hear that? And he's like, you know, gives me this sheepish, you know, embarrassed look. He did hear that. But you know, the, you know, the rock star seller said marketing leads are a waste of time. So they're just complete misalignment, misalignment on the buying journey, where marketing saying something and sales saying something else. And so that creates buyer confusion, which is never a good thing, especially the role they have to play in buying, which can be quite daunting, especially for high consideration solutions. If you create Miss messaging, either they're going to skip you, or you're going to create confusion, lower their confidence, or they can't buy and last time I looked, it's still there. But six out of 10 opportunities end in no deal, no action. You know, isn't that horrifying, all that time and energy and nothing, nothing changes, nothing moves. So, yeah, when it does happen, it's magic. And I've had that magic happen for me multiple times, luckily, where? But it's never it. I don't think it's ever happened where it's been overly pleasant, but I even, oh, I was out in London, Matt, I did a project for a company called concur, before they were acquired by SAP. And out in London, a group of marketers cornered me, and one of the VP said, Bruce, you have made our life miserable. And I'm like, going, oh my gosh, you know, Hey, I am sorry. I don't know what I did. And he goes, Well, Robson, who was their CMO, saw a sales narrative that I had made for their commercial sales team, and loved it, and then went back to his marketing organization. Said, Hey, stop our brand marketing. I want to redo everything around this, because he saw the value former Citrix cmo as well. But, you know, very seasoned CMO, he saw the value in that alignment that they did not have, you know, marketing was talking about stuff that, you know, sellers would certainly not be talking about in their selling conversations. So just complete misalignment. He wanted to solve for that problem. CETA. Matt, I don't know if I've told you about a company called CETA, but I worked with them across the world and their Chief Commercial Officer, thank goodness. I met him in Dubai at one of their global sales kickoffs. And that morning, he had asked all the sellers to enter, you know, introduce C to hit to him like he was an executive for the first time. And so they did a big, huge round robin. And at the end of it, he felt like hanging himself. He just, he just felt royally sick and distraught because CETA they'd been around since World War Two and and he was trying to rebrand CETA, rightfully, as an incredibly innovative organization and a pioneer that's still progressive and in the forefront. And the sellers were making it sound like, you know, Sita was old and stodgy and stuck in their ways. And, you know, just sending signals, brand signals that he absolutely didn't want. And so thank goodness I was there. Had lunch with him. He was really bummed, and I'm like, going, Hey, we need a unified narrative. And so we called it seed a story for sales, so the sellers could have a better narrative that they could use in their introductory selling conversations. But very interestingly, the narrative we built became the impetus for their whole organization's reorganization with their new CEO that they hired, you know, and that's the power of getting this right. And then, you know, when you have that, you know, that crossover executive, like this gentleman's name was author, the Chief Commercial Officer, he could make sure both sides of the house were speaking at, you know, a similar language and having a similar go to market narrative. So it's when it happens Jasmine, which isn't all that often. It's a beautiful thing. And of course, you know, the promise is, you know, more revenue growth when you do have that alignment, internally and externally, with CETA, for example, it was kind of funny. They have a whole analyst team, and they do their annual survey of the parent, transportation sector, and they come out with their big insights and point of views around those insights, and that becomes also a growth engine for them and part of their innovation that they bring to the market. So I got in touch with one of the analyst team and said, Hey, would you be interested in understanding the story that the sellers are telling? And he's like, going, you know, yeah, sounds kind of cool. And I go, Yeah, because if you understood that, you could probably help your sellers. And he's like, you know, trying to connect the dots. But I was like, going, Yeah, you know, based on the narrative that they're saying, you could help give them some gravity, some insights, some statistics that they could bring into their selling conversations and make them that much better, and then he's starting to get it. Oh, my God, that sounds awesome. And so what I did is, you know, we had built different narratives for their different solution domains, and I shared that with him, you know, the all up and the solution stories so that person could do a better job of designing their thought leadership questionnaires, their studies to get the right information to fuel these better selling conversations, as well as their marketing thought leadership platform.

Impact on Buyers and Retention

Speaker: Jasmine Gruia-Gray

Yeah, while I think a lot of companies recognize that the rift between sales and marketing is not healthy, and they sort of talk about how to fix it. I think what they most importantly forget is the impact on the future buyer. When you have that rift, well, you say future buyer. Weirdly enough, Jasmine is actually the existing buyer as well. You know, you wouldn't believe how many buyers buy and have remorse after they buy. You know, there's confusion, remorse, and then in this whole world of everything as a service, there's, there's also a big drop off rate where customers, you know, cancel subscriptions or cancel contracts they want to consolidate. You know, there's other reasons nobody's using it, and so these narratives can can also be incredibly clarifying and useful for existing buyers, giving them the reason to stay, funny enough and and customers that stay are, you know, way more profitable than acquiring new customers. Of course, we've already, we've all seen those economics. But in the in this day and age, especially with the tough economy that we're in right now, many organizations, you know, go to market teams are doubling down on existing customers, looking for white space. How do we upsell, cross, sell, grow with our existing customers? And that's where, you know, a compelling narrative, this contextual narrative, can be so helpful for them, and not only keeping the contract, but adding to that, or adding additional solutions to to the contract, as well as those future customers, as you say, Jasmine,

Becoming Bolder and Visualizing the Narrative

Speaker: Matt Wilkinson

keeping on this sort of sort of narrative story you've said that you know that we're talking about the stuff, and that one of the things in the book I really liked was that you would, you said an awful lot about trying to get people to be bolder. There's a, there's a bit of an old adage in the, you know, in the marketing world, that nobody gets fired for playing it safe. It's only, you know, it's only the people that take those big, bold risks that fail, that tend to get let go. But you kind of said the opposite, that playing it too safe is actually the far bigger risk. You know, we both work in a very, shall we say, conservative industry that tends to for years, everything was kind of pale blue and white. So how do you encourage companies to be more audacious with their narratives, and how do you get them to be bolder.

Speaker: Bruce Scheer

Oh, man, you really bring back an old story. I did some work for McKesson technologies, and I see, if I've got a visual in here, one of the big things that I work on is a narrative. We start with words, but then how can we visualize the narrative? And I don't know if you can remember that chapter, I spent a whole chapter on talking about the importance of that, just to a help customers truly understand what you're trying to speak to, the problem, the outcome, the solution that you're you're speaking around. But how can you help them visualize that as well? And if you can, memory goes way up. You know, the retention of that, but also, you know you probably heard the framing of a picture is worth 1000 words. You know, when you see the picture, you get a big aha moment. And a friend of mine loves to cite this. Have you seen Daniel Pink's some of his animate videos in the past? One was on Drive, you know, motivation. And this buddy of mine, you know, you know, had a degree in organizational development and loved the book, but was kind of confused. But he reads, reads the whole book, likes it, but he sees that video, and just bang, light bulbs go off, that that animation, that story. And so when you know you get a great narrative, you you help visualize it. But once that happens, this one project with McKesson technology, such a great narrative, beautiful visual. And the sellers that had that I was talking to one of the general managers there, and what he had witnessed was the sellers became so much more confident in their outreach to their prospects and in their selling conversations, because, you know, they just knew they were on it. There was big value in it, extremely clear story. The buyers could get it quite quickly. And, you know, the magic would happen. And so their confidence in selling their platform, instead of selling this complex platform, they had a very simplified narrative that people would buy into, and it elevated their confidence, their boldness, and they were so much more effective because of that. And you want your sellers super excited and convicted about what they're selling. And that's what this narrative was able to do, just kind of overnight, you know, introducing that new narrative, they're like, going, Yeah, this is gold. And I've had that happen again and again. When they are loaded right, with a great narrative, it's so empowering, both for them and also their buyers. Very often, I'll teach sellers to whiteboard the narrative in front of their client. And so all you know, when we design the visuals, we design for whiteboard first, and then you can create animations around it, marketing things, and, you know, update the website, etc. But first and foremost, we typically start with doodles, stick figures that sellers could truly draw on a whiteboard. And you wouldn't believe how many times that sellers have said, Hey, Bruce, I went back to my prospect. Oh my god, you wouldn't believe it. You know that whiteboard that I drew, it was still there, and I'm like, going not a surprise. I've heard it so many times that it wasn't your whiteboard anymore. It became their whiteboard and and they used it to retell the story. The CEO didn't want to erase it from the whiteboard because it was so compelling. So that that transfer of ownership of that narrative was is can be incredibly useful for that internal buying journey that the seller is typically not involved with

Speaker: Matt Wilkinson

fantastic I mean, that that's, it's incredibly powerful. I actually saw a stat from, I think one of our mutual friends, Dr Lisa Palmer, who published, posted something on LinkedIn recently about the the amount of revenue per employee. And McKesson leads with about 8.2 million US dollars per employee. And so if those value narratives are delivering that kind of return, how can anybody not take this up as an approach?

Speaker: Bruce Scheer

Yeah, well, and so many don't, but you'd be surprised. You just truly would be surprised in one of my workshops. Have you guys ever heard of a company called Razorfish? Yes, yeah, yeah. So Razorfish, it's the two founders of Razorfish, and 60 Minutes decides to interview them, because they're just so wildly successful. At the time, they were valued at $4 billion and so they're asking the co founders, hey, you know, could you explain just in a nutshell, like you did with me, Matt, what do you guys do? And they couldn't really answer the question, you know. They said, You know, we help business recontextualize themselves. And the 60 minute guy goes, recontextualize. That's kind of confusing, you know, Could you, could you make it simple? What do you do? You know? And they just couldn't, you know, but, but here they are, $4 billion company doing okay. So, you know, organizations exist and thrive without a narrative. But my argument is, if they do have an or if they can have one, which few do, especially, you know, especially few do that, put the buyers the hero. If they do have that you can see just, you know, wild success, one of my clients Tableau, I built their sales narrative, pre IPO, and they just exploded. Of course, the technology was awesome and the use cases were great, et cetera. But I'm telling you that organization, they took my narrative and they made a whole company training. I even have a story about a woman in training and development using it with a prospect down in Vegas and getting a deal. She wasn't even carrying it, you know, in a sales capacity. But, you know, they just believed everybody needs this story, this, this is it. And it became part of their whole company training from Chairman all the way down. So it, you know, when that, when people kind of line around that, you know, the the world can change much faster. And typically, you know, a lot of people might think that they have a narrative. And what I typically ask them, I go, Hey, that's great. I'd love to see it, you know, I don't see it. And I go, Yeah, yeah, I'd love to see it. And especially, show me a picture of the problem that you guys solve. And that's where, that's the total shakeout, where they're like, What do you mean? I'm like, going, Okay, let me repeat myself. Show me a picture of the problem you solve. And they can't, because they don't have a picture like that. They have a picture of their tech, but they don't have a picture, a picture of the customer world and how they're impacted and how that could change that visualizations like that typically don't exist.

Digital Buying, Remorse, and the Role of Sellers

Speaker: Jasmine Gruia-Gray

Yeah, I love that story, and I love that that question about show me a picture. I think a lot of product managers could also learn a lot from that way of thinking. But I wanted to pivot for a second. I was listening to your discussion with a lead judge on the Business Marketing podcast. Yeah, sure, and you quoted this, this metric that just blew my mind, and that was 59% of influence on a big B to B buying decision comes from the buying experience, and only 41% on the influence comes from the offering experience outweighs offering on a buying decision, but with most buyers getting At least 70% of the way through a buying decision before contacting a company rep representative, the experience is wildly dependent on a digital person free experience. Yeah, a little counter intuitive. What advice can you give commercial teams to mitigate this situation?

Speaker: Bruce Scheer

Yeah, it's, it's such an interesting dynamic now, because buyers don't want to engage with sellers, and they're, you know, many marketing organizations have started to kind of like, go, Okay, well, we get it. You know, we hate dealing with sales, too, makes sense, and I've even heard CMOS go and, yeah, our sellers are just order takers. They're useless. You know, you get into all this posturing stuff. But here's one of the other counter intuitive statistics, buyers that do all that self research and buy often have buyer buyers remorse at a huge percent, I think I'll have to fact check this, but roughly four out of 10 buyers who bought think they made the wrong decision after they bought and are unhappy with where they're at. And then the renewal rate, you know, you know, doesn't go and so there is a role to play for sellers, and I typically counter with this consumer based response, but I was working with a company called eBay Motors, part of eBay, and they sell 1000s and 1000s and 1000s of cars every year on eBay. And they, the team I was working with, they were going dealership to dealership, saying, Hey, if you got extra stock, throw it on eBay and you can sell it there and get more turnover, and it will improve your business. And so when I wanted to build those sellers a narrative, so they could go out to dealerships and tell that story. And so I asked eBay, who's your number one dealership in the world, throwing their stock up on eBay and driving a lot of sales, they go, Oh, that's easy, Lexus of Atlanta. So I arranged a meeting with the owners of that dealership, and said, hey, you know, tell me, why is this working for you? Etc. But then I out of curiosity, I asked them, hey, how many customers that come out there or buyers that already purchased their car online? How many of those buyers drive away with the car that they bought and again, they flew from their home state all the way to Atlanta, hopped on a train, took the train to the dealership that was right off the train line, went in there to pick up their keys so they could drive the car that they bought home. How many of those buyers drove their car home? Could you help me with that? Just take a guess. What percent of the buyers drove the car that they bought home, 50% 50 No, 80, 80% so four out of five of those buyers drove their car home. Why'd you say 50?

Speaker: Jasmine Gruia-Gray

My thinking was because the that's when the the seller got in there and started talking them up into a different solution. So built a different kind of relationship with them, and they were persuasive enough to convince them to buy something else half of the time. So I was giving them a lot of

Speaker: Bruce Scheer

you can call it, yeah, you can call it powers of persuasion. But you know, they were able to, I would argue, they were able to help one out of five buyers get into a better car, into a more suitable car. Hey, Bruce, you're here to pick up your Porsche. Oh, wow, that's a sexy Porsche. You're gonna look so cool. Tell me about your life. Are you single? No, no, I got a wife. Be not kids yet. No, no, no, I got three kids. Are they in school? Yeah, yeah. And do they have friends? Yeah. And how do you shuttle them around? I don't know. Hey, Bruce, instead of getting that Porsche, would you be interested? Interested in a minivan? I could make sure you get a sport addition so you still look cool, but, you know, or I use that as an extreme example, but they were able to help people find, you know, the right car, because a lot of people make assumptions, you know, I buy things online, and it shows up the house, and I go, why did I buy this? This isn't what I was expecting. That happens in these B to B deals as well. So there is a role for sales to play in these buyer journeys, you know. And so, you know, the world is the way it is, and sellers haven't earned earn that right. Many buyers, you know, actually want to run from sales again. 75% of B to B buyers prefer to have a sales free experience, according to Gartner, and so, you know, it's broken, but if the buyer tries to take it all the way on their own, you know, there's a huge risk of remorse and just making the wrong decision on behalf of their organization. So I don't know if that's helpful at all, but you know on the marketing side, you can still lead with that narrative. And my firm, we build value calculators, especially if you know it does require that executive check off the business cases is super duper helpful. In that regard, nine out of 10 deals often require business value justification, even by the procurement system. It's a form that they have to fill out. So you need to go there on many of these high consideration solutions, or bold solutions based on that buy in experience staff that you gave. So you can support that online, and we do that as well, you know, try to give the buyer information they need to do a better, you know, make a better buying decision. And so we're, you know, that's our mindset, to try to support that online and with the sellers.

Trends: AI and the Buyer’s “Agent”

Speaker: Matt Wilkinson

Makes sense. Thanks, brilliant. Thank you, yeah. Finally, Bruce, we've got one last question for you. And I'd like you to put your futurologists hat on, if you will. You know, I know I've had a chance to sort of check out that the sort of the business case calculator, I know it involves quite a bit of AI witness, and it is really cool. But what trains trends or changes should sales and marketing professionals in really be preparing for especially if they want to keep inspiring their buyers in the next five years.

Speaker: Speaker 1

Yeah, well, I enjoyed Mark Scheer latest book, how AI is changing your customers. Matt, you've probably read it as well, I'm guessing, or so I liked some of his points of view. I've witnessed that myself and even my own behavior very often. I was buying a pair of boots, and I was leveraging chatgpt to help me buy boots. And it was really hilarious. I already had a pair of boots, and I uploaded it into chatgpt, and I'm like, going, should I buy these boots and chat? GPT said, Well, you could. But if you want to be a progressive, you know, you know, contemporary, maybe a little bit older, but, but yet a little bit wiser, you know, CEO type person, I'd only recommend wearing those boots on the weekend, perhaps walking your dog. I already bought these things, and it just cracked me up. But I think I tell that story, but I believe a lot of people are starting to lean more more heavily on AI as part of that buying journey and getting points of view. So, you know, how can we show up better in AI to support, you know, the AI as buyer or the agent to the buyer? How do we support that agent and getting the right information to feed back into that buying committee? So I think that's one, one extremely hot topic, both today, for for kind of first movers, but for even for laggards, that's going to be a big issue moving forward in the world of consumer MD, to be sales and marketing. I think the other, the other exciting thing that I see is how AI is really helping sellers do a better job of understanding their customer and showing up, ready. The bigger concept behind that, I think, is Respect, respect for the buyer and service to the buyer. How can I do more on my end, to make life easier for my buyer. And I think AI really supports that, that philosophy. How can I, how can I create the best buying experience possible, to help them in their buying journaling internally, which is incredibly daunting. How can I help them do that? And then, how can I just, you know, make sure you know where the vendor of choice, if it's a good fit and and have that mindset, but AI can really help connect the dots in that way and for everybody, brilliant. So, Matt, did you have point of view on that as well? That was just mine. I know you think a lot about the future too, yes. So, I mean, I got a couple Jasmine

Speaker: Matt Wilkinson

yourself. I think the I love you, I love it. Sort of talking about coming up being more prepared. Our podcast is sponsored by humantic Ai, who sort of create both, sort of accounts intelligence and buyer intelligence. So one it can allow you to really go out and better understand the big trends that are impacting the business as a whole. So you can have that, you see, so you've got a better chance of tailoring your value narrative, actually to the business and and its problems that are happening right now and then, and then also being able to understand who's in the room and what kind of personality profiles you've got. So you're actually able to then look at all of those different buyers, you know, whether it's seven or 12, and be able to understand, especially if you can you know who you're meeting and who's in that group, being able to stand some of their personality types and then actually how to engage with them better. So that's one of the big things that I've really, really been espousing for recently. But I think it's all about connection. I think we connect the story and narrative. We connect by better knowing the the customers context, better, their problems better, and really being able to, as you said, sort of really, really respect them as much as part as possible and the problems that they have and and if you have the solution, then it's almost it's almost wrong if you don't manage to actually convey the value that you can give in the best possible way.

Speaker: Bruce Scheer

Yeah, yeah, no, I completely agree. I love your idea around connection as well. I've been speaking a lot about the charisma edge lately, and you and I have talked about that a bit. But you know, if I, if I get the right, or earn the right, to have a selling conversation with a potential buyer. You know, I've got a between a nanosecond and four seconds to show up with enough presence create a perception in their mind that this person is trustworthy and the person's, you know, smart, you know, they're going to help me. And how, you know, how can you do that so fast? And that's this charismatic effect that I'm actually trying to help. You know, B to B, sellers master more of and leaders who support them. How can they be more charismatic right out of the gate and then continue to earn that right based on the homework they did, the value that they can bring those subsequent conversations. But even out of the get go, how do we establish trust and credibility within four seconds? So I think, you know, funny enough, I think we, in the future, we need to upgrade our human operating system, is what I've been talking about. How can we how can we do a better job of connecting when we have these beautiful moments of truth? How can we connect better?

Speaker: Jasmine Gruia-Gray

Absolutely, I think that's that's so important. I don't think relationship building will ever go away. That you know that if you can be a futurist, that I think is the foundation of all of this, but I think what's what's going to change is how quickly you can build that relationship,

Speaker: Bruce Scheer

yeah, yeah. And the man, yeah, just the imperative for that, you bet. Well, bet. Well, find one.

Speaker: Matt Wilkinson

Thank you so much for today, Bruce, I've learned so much, and it's been, it's been brilliant having you on. Thank you so much for your time.

Speaker: Jasmine Gruia-Gray

Yeah, thank you sharing all your insights. We really appreciate it.

Speaker: Unknown Speaker

Yeah, thanks, Jasmine. My pleasure.

Speaker: Matt Wilkinson

Brilliant. Thank you so much. Bruce, that was that was awesome.

Q&A

How do I simplify a 20-product biotech portfolio into something a scientist can follow?

Group products into 3–4 solution categories that map to a scientist’s workflow (e.g., “Sample Prep,” “Assay Optimization,” “Data Confidence,” “Throughput & Scale”). Give each category a clear, named promise. Lead with the buyer problem each category solves. Use one diagram and a one-line outcome per category. Link individual SKUs only after the narrative lands.

Switching costs feel insurmountable. How do I make a credible business case fast?

Quantify the status quo: rework rates, turnaround times, failed runs, headcount hours. Estimate the financial impact with simple, buyer-provided inputs, then show a 6–12 month payback scenario. Package it in a one-pager: problem metric → outcome metric → assumptions → sensitivity table → next step (pilot). Invite the CFO early with a 30-minute review.

How can marketing and sales align around one shared narrative next week?

Run a 90-minute workshop: agree on one named problem, one named outcome, and a three-step solution path. Draft a whiteboard sketch and a three-slide POS deck. Sales uses it on three calls; marketing rewrites the homepage hero to match. Debrief on recorded calls to refine wording. Ship v1 in seven days, then iterate.

What’s a low-budget way to add emotion without losing scientific credibility?

Anchor emotion to operational stakes your buyer feels daily: “trapped in reruns,” “stalled validation,” “audit risk.” Pair each with one proof metric (e.g., “28% reruns last quarter”). Use a customer quote or anonymized lab note to ground it. One image or whiteboard beats stock art—draw the current-to-future state and photograph it.

How do we show up in AI-mediated buying journeys?

Create an AI-ready brief: a 300–500 word summary that states the named problem, desired outcome, proof metrics, FAQs, and a simple ROI example. Publish it on your site and as structured FAQ markup. Ensure consistency across your deck, page, and sales one-pager so AI tools echo the same narrative when buyers ask.

Shownotes and transcript

Shownotes

If you think performance marketing is the whole game, you’re leaving money on the table. This conversation shows why brand is the force-multiplier that makes performance work.
For biotech startup marketers, we define “performance branding,” show how to measure it across the buyer journey, and share scrappy plays to build trust, loyalty, and revenue—because branding eats performance for breakfast.

Who it’s for: Biotech startup marketers, life science PMMs, founders wearing the marketing hat.

What we cover:

• What “performance branding” really means (it’s brand, tied directly to revenue, win rate, margins, LTV—not fluffy veneer)
• Why scientists aren’t “immune to brand” (trust, community, credible support)
• How to measure memory + money (share of search, branded search, repeat traffic, win rates, price realization, pipeline velocity)
• Human-centric buyer journeys in an AI era (omnichannel webs, consistent claims + proof)
• Consistency as competitive advantage (repeat the strongest claim, then repeat it again)
• Shoestring plays for scrappy teams (one killer app note, head-to-head data, meet personas where they are)
• Where AI helps (deep research, RAG, prompt libraries) without going robotic

What you will learn:

• How to define performance branding and align it to revenue, win rate, and LTV
• A practical metric stack that blends memory (brand) and money (business outcomes)
• How to design consistent claims + proof points scientists actually trust
• A lightweight plan to pilot performance branding on a small budget
• Where to use AI for research and content without losing authenticity
• How to build community and advocacy to accelerate pipeline velocity

Chapters:

[00:02] Sponsor + intro
[00:48] Why performance branding now
[01:58] Definition: brand tied to business outcomes
[03:46] Aggregating across channels, not single-touch
[04:32] Search behavior & brand recognition in B2B
[06:12] Are scientists immune to branding? (No.)
[11:19] Measuring memory + money
[14:21] Human-centric buyer journey (AI era)
[16:34] Consistent claims → competitive advantage
[20:00] Misconceptions early marketers inherit
[23:19] Shoestring performance branding for startups
[27:04] Using AI without losing your soul
[32:45] Prompt libraries & marketer “style” models
[37:13] Planning season: make room for performance branding

 

Transcript

In this episode, Matt Wilkinson and Jasmine Gruia-Gray unpack “performance branding” for life science companies—why brand drives performance, how to measure it across the buyer journey, and how biotech startups can apply it next week on a small budget.

Intro & Sponsor

Speaker: Matt Wilkinson [00:02]

Hello and welcome to a splice of life science marketing. This episode is brought to you by Humantic AI. Imagine if you could close 22% more deals overnight using a buyer intelligence platform. Imagine slashing prep time by 70% and boosting close won deals by 37%. That's what customers the world over are achieving using Humantic AI's account and buyer intelligence system.

 

Speaker: Jasmine Gruia-Gray [00:16]

Imagine, right in front of the steps, and moving slowly, of you, right there, step by step. That's the front of the world I hope you've heard of.

Speaker: Matt Wilkinson [00:29]

The platform arms you with firmographic and psychographic insights at scale. So every outreach hits the mark. Deals move faster and revenue grows. Scan the QR code or click the link in your show notes to claim your 10% discount. Now let's get on with the show.

Why Performance Branding

Speaker: Jasmine Gruia-Gray [00:48]

Hi Matt! Good, good. I'm excited about this topic of performance branding.

Speaker: Matt Wilkinson [00:49]

Hi Jasmine, how are you doing today?

Speaker: Jasmine Gruia-Gray [00:59]

The way this came up is a couple of weeks ago, I inadvertently had written a blog post about performance marketing versus brand marketing. And my take was that it wasn't one or the other. It was an and. And then a few weeks later, you shared an article from MarketingProfs where they talked about performance branding, which is the perfect mashup from my blog post. So yeah, let's get on with it and have a chat about that.

Speaker: Matt Wilkinson [01:39]

So isn't it nice that MarketingProfs are reading your blogs? But I know that they talked about a paper from Kotler and Furch. And what do they actually mean by performance branding? And why isn't it just performance marketing with better manners?

Definition: Brand Tied to Business Outcomes

Speaker: Jasmine Gruia-Gray [01:58]

Right. It's really simple the way they describe it. It's branding that's tied to business outcomes. So think about the way we do branding today. Nothing changes in this definition, but it's tied to things like revenue, win rate, margins, lifetime value of a customer, and so on.

The real caution that I would overlay on this concept that they didn't touch on was that people have to think about the business metrics for performance branding across the entire buyer's journey. So let me take a step back. What do I mean by this? So typically, we tend to attribute a single business metric to a single activity. An easy one is you go to a conference and how do you measure success of that conference? By the number of leads. Well, when you think about performance branding, you don't want to just take that one conference and tie it to a business metric. You want to look at the performance branding in aggregate over a campaign or multiple campaigns that focus on the brand in a particular way across the whole buyer's journey.

Speaker: Matt Wilkinson [03:38]

That's really interesting. So how do they propose to do that? To be able to look at that in aggregate, because that's quite a challenge.

Speaker: Jasmine Gruia-Gray [03:46]

Yes and no. I think the concept is that there's no one thing that you can tie to revenue. It's an aggregate of your conferences, your application notes, obviously your sales interaction and relationship, your application scientists' interaction and relationships—all of that together results in the sale. All of that together results in building that pipeline and pipeline velocity. I think it's a mind shift that we all need to start getting our heads around.

Search Behavior & Brand Recognition

Speaker: Matt Wilkinson [04:32]

So it's really trying to actually look at things in an omni-channel holistic view. Kind of takes me back to something I wrote more than a decade ago, where I was looking at research that said 93% of the time B2B sales start with a Google search. And then it looked at what actually makes up those sales—the top 60 or 70 percent of clicks came from the top three links on the search engine results page. Those have changed dramatically over the past 10 years. But then what was really, really interesting is that when research was conducted into what drives people to select the links, it was actually: did people recognize the brand name?

Speaker: Jasmine Gruia-Gray [05:13]

I think that was really pretty.

Speaker: Matt Wilkinson [05:30]

So 87% of people would actually only click on links of companies that they already recognized. And you could argue that Google search in some ways—in its purest form—was a kind of performance marketing because at that point it was still very much the old black hat, before updates improved the search experience. So it really was a performance marketing approach back then. And that really did lead us to a point where brand’s still important.

Speaker: Jasmine Gruia-Gray [05:57]

I'm reading it.

Speaker: Matt Wilkinson [06:01]

In fact, brand is incredibly important, even in a space where people can choose their own adventure. So I thought that was really, really interesting.

Are Scientists Immune to Branding?

Speaker: Jasmine Gruia-Gray [06:12]

That is interesting and it sort of ties to my next question. I was always raised under the belief that scientists don't fall for branding. That's consumer world fluff and scientists are immune to branding. But then how do you explain brands like New England Biolabs or Oxford Nanopore or Cytiva, the former Pharmacia and GE Healthcare?

Speaker: Matt Wilkinson [06:52]

I really disagree that scientists are immune to branding. When you're pushing at the sum of all knowledge, you have to believe some things to be true. Being able to trust and believe in your results is an element of belief, because you're believing in your results. And we do that by believing we can trust results that come from certain brands.

Speaker: Jasmine Gruia-Gray [07:09]

—

Speaker: Matt Wilkinson [07:21]

Branding is in some ways more important. I've seen arguments between proponents of Agilent and Waters for HPLC-MS systems that are more impassioned than Apple vs. Android debates. This week I wrote a blog on using AI to study what people were saying about brands—doing deep research across the likes of ResearchGate and Reddit and places where scientists talk. You can get an awful lot of information about what people are talking about. Word of mouth marketing—certainly word of mouth marketing—

Speaker: Jasmine Gruia-Gray [07:22]

Enough. Okay.

Speaker: Jasmine Gruia-Gray [07:43]

Thank you.

Speaker: Jasmine Gruia-Gray [07:49]

Thank you.

Speaker: Matt Wilkinson [07:50]

—in digital space now, using AI, that you couldn't do before. You can see why they really like those brands. Not only sentiment analysis, but thematic analysis of what's being said, breaking it down into discrete quotes. For example, NEB’s following just loves human, credible contact—when they pick up the phone with a question, there's a scientist who understands them. Oxford Nanopore has built a whole field-support system online, a community support model. While maybe they don't rank as highly as NEB for phone support, they've built a community where people problem-solve together.

Speaker: Matt Wilkinson [08:18]

It becomes a way of asking: how do we build memorable experiences that people talk about—or dominate some online spaces? Cytiva has taken a different approach in some ways, using a rebrand to signal their focus after different shifts in ownership and brands. They’ve focused on branding in their culture as a risk-reduction strategy—signaling trust. It's not just pretty logos and iconography—those are important because they're what we tie emotions to—but they’ve understood that emotion is a core part of what scientists and human beings need to identify with a brand.

Measuring Memory + Money

Speaker: Jasmine Gruia-Gray [10:19]

What I like in the examples is the tie between emotions and creating community. You can't have a community without brand awareness and trust. That common purpose is part of performance branding.

Speaker: Matt Wilkinson [10:59]

If performance branding is designed to strengthen relationships, loyalty and trust, what should we measure to see if we’re moving the needle?

Speaker: Jasmine Gruia-Gray [11:19]

We need to measure a combination of two words: memory and money (or business). All of these companies are in business to be profitable. How do you go about measuring these two things? Branded search, share of search, and share of voice are part of memory. Repeat traffic on your website from target accounts starts joining the dots between memory and money. Then more concrete business metrics like pipeline velocity, win rates where there was prior brand exposure, and price realization versus discounting trends—especially with repeat customers. Those are things you can measure in a coming quarter to start aligning to performance branding.

Speaker: Matt Wilkinson [12:56]

Doesn't that get complicated across so many channels?

Speaker: Jasmine Gruia-Gray [13:09]

It can be. You need a plan and sales-marketing-finance alignment. I'm a big fan of starting small. Think big but start small. Maybe pipeline velocity is too complex right now—go to something more concrete like win rate, then work toward pipeline velocity.

Human-Centric Buyer Journey (AI Era)

Speaker: Matt Wilkinson [14:09]

What does human-centric look like in a lab buyer's journey?

Speaker: Matt Wilkinson [14:21]

It's changing all the time thanks to AI. McKinsey’s data showed buyers want to interact everywhere, any time. Funnels have become webs of interaction, and buyers jump around as they move through their journey—often starting in those webs before they're even in a buying journey.

Speaker: Matt Wilkinson [15:16]

We have to make really consistent claims with compelling proof across every touch—app notes, webinars, calculators, trials. Scientists are the most skeptical audience in the world. They test hypotheses: “If I go down this route, can I improve X by Y percent?” So we need consistent claims and proof points—and ensure there’s a line to a human they can trust. Keep a human in the loop across those interactions.

Speaker: Matt Wilkinson [16:15]

What’s the fastest path from brand awareness to competitive advantage?

Speaker: Jasmine Gruia-Gray [16:34]

Pull on “consistent claims.” Pick your strongest claim versus competitors—e.g., “My technology detects 30% lower-abundance proteins without any extra steps.” Repeat that claim across channels with the strongest data and evidence. Consistency plus proof builds the path from share of mind to share of wallet and competitive advantage.

Speaker: Matt Wilkinson [18:23]

So brand is linked to clarity of message and credibility that’s consistent across communications.

Speaker: Jasmine Gruia-Gray [18:38]

We get tired of our own messages and switch too soon. The audience isn’t tired. Studies show you need to repeat a message up to seven times before someone internalizes it. Don’t be afraid of repetition—it’s your friend and part of performance branding.

Misconceptions & 360° Experience

Speaker: Jasmine Gruia-Gray [19:46]

In your experience, what misconceptions do early career marketers inherit about brand?

Speaker: Matt Wilkinson [20:00]

Thinking branding is “one and done”—logos, color palettes, tone of voice. Strong brands like NEB, ONT, Illumina have identifiable, consistent elements you can hang emotions on, but they also build a culture of engagement. Products must be great. And the true test is when something goes wrong—do they make it easy, do you feel heard? Early marketers often miss that brand is the entire customer experience. As Warren Buffett says, it takes years to build a reputation and seconds to destroy it.

Speaker: Jasmine Gruia-Gray [22:13]

So important and so undervalued. Every employee is a brand ambassador—front of house or back of house—part of the customer experience. We could go down the rabbit hole on CX—maybe for a separate podcast.

Shoestring Performance Branding

Speaker: Matt Wilkinson [22:53]

How would a scrappy startup run performance branding on a shoestring?

Speaker: Jasmine Gruia-Gray [23:19]

Think in three questions. Who am I talking to (persona and ICP)? What are you going to say (consistent claim)? Where will you meet them (channels they actually use—LinkedIn, ASHG, etc.)? Start small: one application note—e.g., head-to-head comparison data versus a competitor. Describe setup, outcome, and most importantly what the outcome means. With something like that (gated or not), you tug on memory by repeating a narrative, show proof to the skeptical audience, and pull on empathy because the experiment resonates with your target.

Speaker: Matt Wilkinson [25:43]

Find that killer application that appeals to enough of your ICP to move the needle across a segment.

Speaker: Jasmine Gruia-Gray [26:04]

That goes back to product management and PMM early in development—build applications as the product is built. We are not an industry of “we will build it and they will come.”

Using AI Without Losing Your Soul

Speaker: Jasmine Gruia-Gray [26:40]

AI can feel like a field of dreams. How do we use AI without making our brand feel robotic or unauthentic?

Speaker: Matt Wilkinson [27:04]

As models improve, generic outputs stay generic—unless we give specific inputs and guidance. Andy Crestodina says AI can be “average information.” But AI gives phenomenal abilities to research customers. Once we identify ICPs, AI helps expand lists, build better personas, and do deep research—what are they actually talking about online? Forums beyond the obvious. What are they saying about my brand? What problems are they highlighting?

Speaker: Matt Wilkinson [28:22]

Use it to spot risks and enable advocates. Then use RAG systems to guide outputs—what to say and what to emphasize. AI connects datasets the human brain struggles to connect. Keep the human at the heart and solve real human problems. Creation still works best as collaboration: human tasks, augmented tasks, and agentic automated tasks—aim for symbiosis in the augmented zone.

Speaker: Jasmine Gruia-Gray [30:05]

I often get soulless responses. I’ll reprompt for more empathy—for the user’s struggle in an experiment. How do you approach it when you get a soulless response?

Speaker: Matt Wilkinson [30:50]

Exactly that—guidance in prompting and reprompting. If it’s a task you do a lot, once you get a good output, ask the AI: “Based on this, how should I have prompted you to start?” Save that in a prompt library. If it’s frequent, create a custom GPT or Claude project. I’m a fan of Mark Schaefer’s “Markbot”—discussing ideas there often makes outputs more empathetic. It’s like collaborating with a synthetic version of a marketing hero.

Speaker: Jasmine Gruia-Gray [32:45]

Two take-homes: build a prompt library—even an Excel file with tabs (performance branding, competitor analysis, etc.). And pick a marketer whose style resonates; prompt the AI to use that style or debate in that style. It helps you grow faster than doing it alone.

Speaker: Matt Wilkinson [34:02]

That prompt library suggestion is gold. Mine grew from scattered notes to a big Google Doc—one of the documents I’d be most upset to lose.

Lessons from Leading Brands & Close

Speaker: Matt Wilkinson [34:44]

What can early career marketers learn from brands that are really talked about—NEB, Oxford Nanopore, Cytiva?

Speaker: Jasmine Gruia-Gray [35:06]

Remember the feeling. Our audience is often scientists who pivoted to marketing. I remember using NEB products—detailed instructions, thoughtful human support when issues arose, not robotic answers. I remember events where speakers really understood my problems and helped connect the dots. Find a brand you respect and emulate how they created that feeling, loyalty, and trust.

Speaker: Matt Wilkinson [37:13]

I love that—branding is about the feeling. Great note to end on.

Speaker: Jasmine Gruia-Gray [37:23]

One encouragement: we're in strategic planning season. I strongly encourage you to think about how performance branding will fit into your business in 2026.

Speaker: Matt Wilkinson [37:50]

Excellent. Thank you so much.

Speaker: Jasmine Gruia-Gray [37:52]

Thank you. This was a fun discussion.

Speaker: Jasmine Gruia-Gray [37:58]

Looking forward to seeing you again on the next episode of a splice of life science marketing.

Q&A

How do I pilot performance branding next week with one scientist persona?

Pick one ICP role (e.g., proteomics core lead). Draft a single, strongest claim (e.g., “Detect 30% lower-abundance proteins without extra steps”). Create one proof asset: a one-page app note with setup, data, and what the outcome means. Post a plain-language teaser on LinkedIn, DM five relevant scientists, and email five existing contacts. Measure branded search, page views from target accounts, and replies over 7–10 days.

What metrics should I track first if I can only set up three?

Start with: (1) Branded search volume (memory proxy), (2) Win rate where prior brand touch is logged (money proxy), (3) Repeat traffic from target accounts (bridge metric). Instrument with Google Search Console, simple CRM campaign tags, and an account list in your analytics. Review weekly; log baselines and deltas to see if your claim + proof shift behavior.

How do I create consistent claims without sounding repetitive?

Keep the core claim identical, vary the wrapper. Use a 1×3 matrix: one claim, three proof formats (app note, short demo clip, customer quote). Rotate channels your persona actually uses (LinkedIn post, relevant community thread, conference follow-up email). Every asset ends with the same one-sentence claim and a single CTA to the app note or demo.

Where should I show up if my audience is small and niche?

Audit where your 25-account wishlist engages: LinkedIn posts, specific subreddits, ResearchGate questions, ASHG/SLAS sessions. Choose one digital venue and one event. Commit to a 2-week sprint: comment helpfully on five posts/threads, publish one practical tip tied to your claim, and offer the app note to those who engage. Quality beats volume in niche science.

How can I use AI without making content feel robotic?

Use AI for deep research and structuring, not final tone. Build a prompt library with your brand voice and persona pains. Feed AI your app note data and have it draft a summary; you add the human layer: context, empathy, and concrete next steps. Run outputs through a “scientist sanity check” list: does it cite data, acknowledge limitations, and offer a clear experiment-next step?

Shownotes and transcript

Shownotes

Most professionals think they need to post daily on LinkedIn to stay relevant, but they're burning out and seeing terrible engagement. There's a smarter way to build your professional presence without sounding like a walking sales pitch.

This episode is for life science marketers and scientists who want to grow their LinkedIn presence without the daily grind of content creation. Jasmine Gruia-Gray and Matt Wilkinson interview social media strategist Valentina Escobar-Gonzalez, who reveals why reposts only reach 1% of your audience, how strategic commenting can increase your profile views by 300-500%, and why posting multiple times per day kills your reach. The key insight: How do you actually grow on LinkedIn without posting daily or sounding salesy?

What you will learn:

• Why reposts and links dramatically reduce your LinkedIn reach
• The 3x3x5 commenting strategy that boosts profile views by 300-500%
• How to structure posts using "broetry" formatting for maximum engagement
• Why the first hour after posting is critical for algorithm success
• How to turn conference attendance into weeks of authentic content
• The notification bell strategy for staying top-of-mind with existing customers

Keywords: LinkedIn marketing, social media strategy, professional networking, content strategy, LinkedIn algorithm, biotech marketing, life science marketing, social media engagement, personal branding, LinkedIn growth, B2B marketing, scientific marketing

Ready to grow your LinkedIn presence without the daily posting pressure? Watch this episode, subscribe for more practical marketing strategies, and visit our website for additional LinkedIn resources and templates.

Transcript

In this episode of 'a splice of Life Science marketing', hosts Jasmine Gruia-Gray and Matt Wilkinson interview social media strategist Valentina Escobar-Gonzalez about LinkedIn growth strategies that don't require daily posting or salesy content. Valentina shares insights on algorithm behavior, strategic commenting, and how life science professionals can build authentic connections on the platform.

Introduction and LinkedIn Myths

Jasmine Gruia-Gray

Welcome to a splice of Life Science marketing. I am Jasmine Gruia-Gray, and today Matt Wilkinson and I have the great pleasure of chatting with our friend Valentina Escobar-Gonzalez. Welcome Valentina.

Valentina Escobar-Gonzalez

Everyone excited to join you guys today.

Jasmine Gruia-Gray

Just a little bit of an intro for Valentina. She's a seasoned marketer, social media strategist, serial learner, speaker and marketing educator. She specialises in helping individuals and their businesses build authentic connections to their social media channels to boost customer relations and create a strong, purposeful community online. She also deeply understands digital content, social media platforms, social listening practices and digital marketing strategies. With over 10 years of experience in the world of social media, she is a co-author of this fabulous book with a fabulous title, the most amazing marketing book ever. And she collaborated with over 35 fellow marketers to share their combined top strategies. It is a must read, and I encourage our audience to get that book. She's been featured in multiple podcasts, which are available on her website, and has won several prestigious awards throughout her marketing career. Wow, amazing, amazing background. We're so glad to have to be able to host you. Valentina, welcome again.

Valentina Escobar-Gonzalez

Thank you. And I might add that if you prefer the audible version, you have different countries represented, so you hear 10 different accents. So it's gonna be so much fun if you do listen to the book, your audible.

Jasmine Gruia-Gray

All right, so let's kick it off today with what are the three LinkedIn myths you wish people would stop repeating, and what actually matters in the first hour after posting?

Valentina Escobar-Gonzalez

Thank you for asking that question. I love the stuff that we're going to be talking today. It's going to be a very spicy discussion on LinkedIn for everyone that second guesses themselves and says, What am I doing wrong? I'm glad you asked that question. So one thing that I think a lot of people do that is probably wrong is reposting. I don't know if you know this Matt and Jasmine, but for every time you repost someone else's post, it only reaches 1% of your audience, and if you add your blurb or caption above the repost, it only gets 0.5% so here everyone's like, let me just repost someone else's stuff, or let me repost something from an organisation or association and that is a problem. No one's going to be seeing that. So that's one thing. The second thing is adding links. When you add a link, it reduces reach by 50% so if you're trying to tell people, hey, go to this link and I'm going to direct you outside of LinkedIn, that is going to reduce the reach. And finally, over posting, some of us might be a little bit guilty about this. We think that we have to post every day to be relevant. So those are the three things that I believe that might be hindering you with your LinkedIn strategy.

Jasmine Gruia-Gray

Wow. I had no idea that reposting reduces your reach that significantly.

Valentina Escobar-Gonzalez

Yeah. So a lot of people think that and I only do that if we're trying to be nice to someone or try to get brownie points. So if I repost your post, you'd be like, Oh my goodness, Valentina reposted my thing. That's so sweet of her, when I actually know that only 1% of my audience is going to see that post. You might not know that Jasmine, but you're like, Oh, she's so sweet to repost my post for the day.

Engagement Metrics and Dwell Time

Jasmine Gruia-Gray

So just as a follow up, how do saves or comments or likes or dwell time play from what you've seen?

Valentina Escobar-Gonzalez

So I want to focus on dwell time. So when you post something on LinkedIn, I try to put the juicy or the hook, something that's going to be about one liner at the very beginning of the post. So then it compels people to click on that button that says, See More. So this is something that I heard Richard Bliss discuss, and he's the expert. He knows all the stuff about LinkedIn. He talks something about broetry. So when you write something, you're putting something and it's one line, it's a hook, a good line, and then you put a space, and then you put another line, and then you do another space and another line. It kind of looks like a haiku. So you just go, space, line, space, and discuss something in detail. So that's why I think dwell time is something that you should think about putting something very good at the very beginning of the post, and people are compelled to press See More, and then you get certain points based on the algorithm, based on that.

Video Content and Content Variety

Matt Wilkinson

If LinkedIn hid likes for a month and reposts, what would you post and how would you drive success?

Valentina Escobar-Gonzalez

I'm not a good writer. I've always admitted that writing comes, it's hard for me to put my words into just putting it in a sentence or in a paragraph. To me, if I could just cheat my way through LinkedIn, I would do video. Video to me is so much more easier. I can just hop on something and just say what I need to say, and I'm done for the day. That's what I would do if I could. But I know video has got a lot of reduction in reach lately, and I think another thing that people do is they post. Someone was telling me yesterday, oh, I'm going to batch all these videos out. I'm going to have video, video, video. And I didn't tell her, I didn't want to seem too opinionated, but I didn't want to tell her. But if you do the same content, type of content all the time, so to say you do picture, picture, picture or link, link, link or video, video, video, it reduces reach by 30% so you're trying to switch up your strategy. So don't just always do videos, switch it up and do a link or a post or a picture or text only. Text only gets a lot of reach on LinkedIn.

Key Metrics to Watch

Matt Wilkinson

What two metrics would you really be watching on LinkedIn?

Valentina Escobar-Gonzalez

The big thing for me is the comments. If I can drive the comments, that would be ideal. I love to see what people are saying when you post something on LinkedIn, it gets 1/10 the reach. If you comment, it gets 1/3 the reach. So this is super important. So I'm always seeing if people are leaving me comments and then replying back to their comments in that first hour when you post something on LinkedIn, it gets 10% reach, so it's pushing it out there. So you don't want to post and go, so you don't want to be like, Okay, I'm going to post, I'm going to get up, I'm done for the day. You want to be close to your phone or your computer and monitor the comments. That's so important, so just keep that post up and relevant.

Jasmine Gruia-Gray

I love that no posting and going. I think that also speaks to the engagement that LinkedIn is focused on.

Strategy for Life Science Companies

Jasmine Gruia-Gray

You just joined a 10 person life science tools startup company. 90 days to create a pipeline from LinkedIn. What does that look like? What's your weekly plan across different personal profiles and the company page?

Valentina Escobar-Gonzalez

I want to go back and ask you more details about the audience. I want to understand what makes them tick. What are they looking into? What is a typical day in their life? To understand what type of posts or strategy I found for them. So give me a little bit more substance about the audience.

Jasmine Gruia-Gray

So the audience is typically scientists. They may post once a week, maybe even less frequently. The types of posts they may do is on their research itself. It could be a sharing of a recent publication, for example.

Valentina Escobar-Gonzalez

Most of the scientists that I know, they're always behind the scenes. They don't post. If they do post, they post maybe once a year, because they were forced to do it. So that's I always like to get that perspective. But when you're thinking about that, the strategy, I would say, just to make it simple and concise here, is maybe have the company page, post the publication and they share it. Because the thing is, these folks are so busy, they just need the quick fix. They just need to share something very quickly from the company page. So it looks official. I know from fact that the people that I know that are scientists don't like to toot their horn. So I don't know if that makes sense across the pond, but they don't like to talk about the amazing stuff that they're doing. So we need to be realistic. They are going to struggle posting about their publication. Right? Is that right? Jasmine, Matt, what do you think?

Matt Wilkinson

I think you're right. I think there's also the concern that if I say something that's just a little bit off or a little bit off company line, I might get embroiled in something. We know how toxic social media can get these days. So I think there's often a little bit of reluctance to promote themselves too much, because, honestly, they don't want or need the kickback.

Jasmine Gruia-Gray

I agree. I think that there's also another segment of the audience that may be earlier on in their career. They may be a technician type, or they may be in the process of getting a degree, and they're much more comfortable with social media. And they look to social media to stay on top of what companies are doing, new launches and that sort of thing.

Valentina Escobar-Gonzalez

And I want to pause and acknowledge those people that are doing that. That's great. But what about the Gen Xers? What about the elder millennials? I have a feeling that they need the nudge, and I think this would be a good time to pause and maybe Jasmine, you can say you need to do this. You need to do this for your career to be relevant in this age of AI, if you're not seeing yourself out there.

Jasmine Gruia-Gray

Couldn't agree with you more. Personal Branding doesn't have an age limit. Doesn't have a career limit. I actually recently wrote a blog about personal branding and how it changes over the course of your career and who you are and what you represent and what your values and ideals are, can change throughout the course of the year. And I think LinkedIn is a really strong professional social media platform to engage with.

Matt Wilkinson

I think there are other social media networks, like there's a lot of scientists to go on X or Blue Sky, but also places like Research Gate. So there are these other platforms where scientific discourse happens. But I think that HR, when you're going for that next role, they so often want to actually get to know who you are and what you're doing professionally via LinkedIn, rather than necessarily thinking about looking at ResearchGate or those other social media networks. So I think it really is important to have that presence there on LinkedIn, because it is more than just a glorified CV, but it becomes incredibly important for your personal brand.

The Power of Strategic Commenting

Matt Wilkinson

If you were banned from posting on social media for 90 days, how would you still grow and book meetings if you could only comment on other people's posts?

Valentina Escobar-Gonzalez

I kind of alluded to that earlier in our discussion. It's all about the comments. One of the things that Richard Bliss promotes is if you comment three times a day on three different people's posts for three to five days, it's going to increase your profile views by 300 to 500% increase. So let me repeat that again, because I'm from Miami and I talk fast. So you're gonna comment on people's posts three times a day. So breakfast, you go on, you have your cup of tea, your cup of coffee, and you're gonna look at LinkedIn. Then at lunchtime, you're gonna do this again. And then when you're doing your Netflix and chilling, you're gonna do this again. So you do this three times a day for three different people for five days, and that's going to increase your profile views from 300 to 500%.

And when we're thinking about comments, we're not going to be like, let's say you were at that event that you went to last week. I'm not going to just be like, Oh, that's so great that you were in the DC area. I am so happy that you were at this event. I actually attended that event two times in Boston when I was in college, and I got so much out of that event, and I only had access to the trade show ticket, so I like to get the people that are eavesdropping that comments showing up on their feed, they're gonna be like, Oh, that's so interesting. That Valentina went to that prestigious event when she was in college and she went to Boston. So there's so much substance behind that comment. You're not going to just say, hey, that's great that you went to that event. You're going to put some meaning.

Jasmine Gruia-Gray

So sharing the point of view, I think, is trying to say, to add value in that comment.

Valentina Escobar-Gonzalez

Yeah, because the comment might show up in a network outside of yours. So people are like, how does she know that person? So if you put that context, that is going to provide some input for that audience, and people eavesdropping the conversation by looking at the comments they have some substance behind that relationship that that person has with you.

High-Performing Content Strategy

Jasmine Gruia-Gray

Can you break down your highest performing post and what part of that post was the true driver? Earlier you spoke about the hook and sort of the formatting of the post, if you can sort of give us an insight into when Valentina is putting the posts together.

Valentina Escobar-Gonzalez

I love this because you both have been with me to the Mark Schaefer uprising event, and I've gone to several of the uprisings. This is one of the examples, my favourite example. I posted a recap of the uprising, and I put a carousel. So this is when you do a visual, you upload it as a PDF, and people are swiping through it. So for the visual people like myself, I put images of each of the speakers and a takeaway. One line takeaway. So I'm taking a selfie at the barbecue place. I'm taking a selfie right before they're about to speak, or when I'm at the breakfast, because that's what I do. I take advantage and I just sit with someone at breakfast. So I took a selfie then, and then I put a one liner saying what I learned from that person.

So that was the most viral post I've done. I put a blurb talking about what the uprising is, and then each visual was a selfie with that person and what I learned from that person. So the secret was, we had just attended Richard Bliss's presentation on LinkedIn, and we learned that if you tag someone, if that person does not respond, you get penalised if they don't respond quickly. So what I did is I tagged a couple of people, not everyone, and I secretly messaged them on LinkedIn, saying, Hey, I just posted, can you please like and comment? So I went out, I specifically was trying to be mindful of the fact, what is the best time to get the good comments, not the ones saying, hey, that's great that I saw you. When do people have the time to give me a good, juicy comment? On the weekends, when they're cycling, right before they go cycling, on Saturday mornings, when do they have more time to give me a really thoughtful comment? So that's what I did. I specifically posted that post on the weekend when someone could have more time to give me a good, substantial comment.

Conference Content Strategy

Matt Wilkinson

Conferences are content gold, as you've just mentioned, how would you turn a sales and marketing team present at the conference into 30 days of posts without sounding too salesy?

Valentina Escobar-Gonzalez

I love this question, and I was marinating on this question, and I was thinking about you, Matt, so let's take a couple of steps back. So when you post on LinkedIn, if you post every day, let's say you post multiple times in a day. So you post one time, let's say at nine o'clock in the morning. Let's say you post again at 11. You went to one session, then you went to another one. You found it very insightful, every time you post. So you post in the morning and you post maybe at lunch time, it hides subsequent posts by 99%. Let me repeat that. When you post on LinkedIn, if you post more than several times in a day, it hides the second posts 99%.

So when I'm posting on LinkedIn, I don't post every day. I post maybe once or twice a week. I'm not like you both, that you both are exceptional writers. I have that one post and I let it stay there, I let it marinate and let it cook in there a little longer. I try not to post too often, because I want that post that I did two days ago to get more traction before I decide to post again two days later. So when it comes to not being salesy, we have to go back to the psychology. The people that we're talking to today that are listening to this podcast, don't toot their horn as much. So you have to get past that imposter syndrome that you have to promote this event that you just attended, milk this conference. You have to take advantage of this conference and really amplify it once you get past that.

Let's talk the strategy. So we're going to share the fact that we're at this event where you take the picture with that awkward sign that says 2025, whatever conference you take that I've arrived, then you're going to attend a couple of events. I think that you shouldn't post every time you attend a session. I think people will appreciate what I do, which is the recap. You just post that one thing per day and you show off multiple speakers. People love that. Again, a lot of people are visual. So they're swiping and seeing who you saw, what you saw, what you learned. 30 days of content. That's 30 posts from you, Matt, I don't know if I want to see 30 days worth of content.

Jasmine Gruia-Gray

I think also people want the quick, salient facts and then they've moved on, right? To a degree, we all have a little bit of ADHD. So give me everything all in one go or in two goes, and then move on. What else have you got to say?

Customer Retention Through Social Media

Valentina Escobar-Gonzalez

So this is something for the sales people, because I feel like I do marketing, but I'm a salesperson at heart. This is something that I've read from Forbes a couple years ago, it basically gave me like a panic attack. It says that 50 to 60% your business is from current customers, while 5 to 12% are new people. So here we are trying to engage all these new people. We just need to cultivate our current customers or current relationships.

So what does that mean? How do you translate that to social media? So what I do is, if a person becomes a customer, how do I not miss anything? I turn on the notifications. So on the person's profile, once you're connected to them, there's a little bell icon, turn on the bell icon, and that way I get notified immediately if that person posts. So right now, as I'm talking to you, I'm looking that way, and it's notifying me on my home screen, on my phone, that so and so has posted something on LinkedIn. So I have to, after this call, I'm going to jump in and start engaging with their posts. What does that mean? That means that makes me top of mind. So when they post, I see it, I comment, and they're like, at one point, my customers know this trick already. And they're like, Valentina, why haven't you commented? Because I'm on a podcast or I'm in a meeting with someone else. I can't stop what I'm doing to attend your post, but they expect it immediately.

Optimal Posting Times and Strategies

Valentina Escobar-Gonzalez

When I publish my LinkedIn newsletters, I try to do it on Sunday because I feel like people are just relaxing. They're having that nice brunch with their family, and they're just checking up on their phone. So my favourite day to post is Sundays for that reason.

Jasmine Gruia-Gray

And any time of day.

Valentina Escobar-Gonzalez

So according to Richard, he says Tuesdays and Thursdays, 8 to 11am your local time is probably the best time to post something on LinkedIn. But I feel like when is the best time that people actually have time to comment so that I kind of reverse the question back to you, it makes you think about that as well.

Matt Wilkinson

It's really interesting, especially when you work across multiple time zones. So I tend to try and get the late lunch crew. So people when they're coming back from lunch in the UK, try to hit them then, which means it should be around coffee break time on the east coast, but people are maybe getting up in the West Coast. You're trying to hit a convenient time zone for everybody, but it's really hard.

Valentina Escobar-Gonzalez

So you can repost your own post, those people like you that have the multiple time zones. So let's say you post at eight o'clock your time, and then you cater to the people in the other time zone, four hours away, or whatever it is. And then you can also repost your posts two days later to get even more reach. So all you need to do is just press the repost button, and two days later you can get even more reach by doing that, because maybe earlier in the week it was busy.

Matt Wilkinson

And you don't get any penalty for reposting your own posts.

Valentina Escobar-Gonzalez

Not that I know of. And one more thing, reposts don't count against your daily post. So when you repost something from someone else, it doesn't count as your daily post. Remember, we were talking about the fact that if you post more than several times a day, it hides subsequent posts by 99% the only thing is, reposts don't count towards your daily post.

Key Takeaways

Jasmine Gruia-Gray

So as we sort of wind down this amazing discussion, what are the three take homes you'd like to leave our audience with?

Valentina Escobar-Gonzalez

I feel like we went in so many directions today, right? I feel like we were all over the place today. If anyone was tuning in today, I really hope you had that cup of coffee or tea before jumping in. But the thing is, I want people to, I really want to harp on this. I really want you to put yourself out there. I want you to be intentional about what you're posting. And if you don't like posting, maybe you're going to be more comfortable commenting and you say, Okay, I'm going to stop being a lurker. I'm going to stop looking at people's posts and actually take the time to comment. Because I heard about the value of commenting. I think that's one. Put yourself out there, stop lurking and start commenting.

Jasmine Gruia-Gray

And if people want to get in touch with you, Valentina, how can they reach you?

Valentina Escobar-Gonzalez

The best way to find me is going to my website, beyond hyphen engagement.com, as you can see, I'm glad to talk. I talk a lot. I'm glad to help you and push you if you need that push, or someone that give you that perspective that no one else can give you. I'm here for you. I'm here for my clients.

Jasmine Gruia-Gray

Oh my gosh. Valentina, thank you so much for sharing your marketing wisdom, your personal branding wisdom, and certainly the LinkedIn experiences. Most of all, thanks for helping to keep things spicy, as you like to say, and being part of a splice of life, science marketing community.

Valentina Escobar-Gonzalez

I'm excited to be here. And again, you guys, whoever is listening to this, I believe in you. I know you're doing great things, and just need to put yourself out there. You need to stand out and put yourself out there. And even if it's just commenting. You don't have to post, but just commenting and just putting yourself out there, that's so helpful right now.

Jasmine Gruia-Gray

Thank you so much.

Matt Wilkinson

Thank you.

Q&A

How can I grow my LinkedIn presence without posting every day?

Focus on strategic commenting using the 3x3x5 method: comment on three different people's posts, three times per day (morning, lunch, evening), for five days straight. According to Valentina's experience, this can increase your profile views by 300-500%. Make your comments substantive by adding personal context and insights that provide value to others reading the conversation.

What should I avoid doing on LinkedIn that kills my reach?

Stop reposting others' content (reaches only 1% of your audience), adding direct links (reduces reach by 50%), and posting multiple times per day (subsequent posts get 99% less reach). Also avoid posting the same content type repeatedly - vary between text, images, videos, and links to maintain algorithm favour and audience interest.

How do I make my posts more engaging for scientists who don't typically engage much?

Use the "broetry" format: start with a compelling one-liner hook, then space out your content line by line to encourage people to click "See More." Focus on sharing insights and learnings rather than self-promotion. Consider having your company page post publications and research, then share from there to make it feel more official and less like personal bragging.

What's the most important thing to do in the first hour after posting?

Stay near your phone or computer and actively monitor comments. Posts get 10% of their total reach in the first hour, so this is critical time. Respond to every comment quickly and meaningfully. If you're tagging people, message them beforehand asking them to engage quickly, as LinkedIn penalises posts where tagged people don't respond promptly.

How can I turn one conference into weeks of content without being salesy?

Create recap posts with carousel formats showing selfies with speakers and one-line key takeaways from each. Post once or twice per week maximum, letting each post "marinate" for several days. Focus on what you learned rather than promoting your company. Use the weekend for posting when people have more time to give thoughtful comments, and avoid posting every session attendance in real-time.

Shownotes and transcript

Shownotes

Most sales teams are drowning prospects in generic emails and cold calls, contributing to what Rohit calls "the Dust Bowl effect" of over-farming buyer attention. There's a smarter way to sell that focuses on buyer experience rather than seller productivity.

This episode is for biotech startup marketers and sales leaders who want to move beyond spray and pray tactics to build genuine connections with prospects. Matt Wilkinson and Jasmine Gruia-Gray interview Rohit Veerajapa, Chief of Staff at humantic AI, about how buyer intelligence and account research can transform your outreach strategy. The key insight: move beyond spray and pray outreach with account and buyer intelligence to truly connect with customers and prospects.

What you will learn:

• Why buyer mindshare, not seller productivity, is the real bottleneck in B2B sales
• How personality intelligence helps you "speak Japanese" to native Japanese buyers
• The buying committee magic quadrant: friendlies, sceptics, crusaders, and neutrals
• Why AI-powered account intelligence saves 30 hours of research in 30 seconds
• How to move from vanity metrics (volume) to value metrics (personalisation)
• Real customer stories showing 49% to 151% pipeline improvements

Keywords: buyer intelligence, account intelligence, B2B sales, personality AI, sales personalisation, humantic AI, biotech sales, life science marketing, buyer experience, sales enablement, buying committee analysis, prospect research

Ready to transform your outreach from spray and pray to strategic intelligence? Watch this episode, subscribe for more B2B sales insights, and visit our website for additional resources on buyer intelligence tools and strategies.

Resources & Links

• Humantic AI: www.humantic.ai

• Connect with Rohit:   / rohithveerajappa  
• Connect with Matt   / mattwilkinson  
• Connect with Jasmine   / jasmine-gruia-gray-3a656a   

In this episode of 'a splice of Life Science marketing', hosts Matt Wilkinson and Jasmine Gruia-Gray interview Rohit Veerajapa, Chief of Staff at humantic AI, about moving beyond volume-based outreach to intelligent buyer engagement. Rohit shares insights on personality intelligence, account research, and the "buying committee magic quadrant" that helps sales teams understand group dynamics and build authentic connections with prospects.

Introductions and Background

Matt Wilkinson

Hi, I'm Matt Wilkinson, and welcome to a splice of Life Science marketing. Jasmine and I are here today with Rohit Veerajapa, the Chief of Staff of humantic AI. Now humantic AI is a personality, buyer, intelligence tool, and I've been a big fan of their work for the last few years. Every time before every meeting, I receive an email with a personality assessment of who I'm going to be speaking with. And so just before we jumped on this call today, I received an email from humantic about Rohit, and apparently, he's a thorough evaluator. He's rigorous and demanding and he's precise and practical. He's less concerned about the product and more about its potential impact, and he puts a lot of effort into ensuring personal success. So I'm really excited to welcome Rohit onto the show today.

Rohit Veerajapa

Thank you so much, Matt. In life, I'm a far more friendly person, but humantic does not recognise that part of you. But we'll get to that in a bit.

Matt Wilkinson

I'm looking forward to that bit, and thank you for coming on the show. So really, just like to understand a little bit about who you are, your role and maybe the key performance indicator you're most focused on right now.

Rohit Veerajapa

Okay, so maybe a bit of a background, had my education in computer science, then started my career as a software developer, right? But I realised I had bigger ambitions. So in 2011 I started up, I ran a company called Bob labs for the next nine years. And then fate had it in a way, where I ended up working for an accelerator for the next four and a half years, an accelerator slash fund. It is the Y Combinator equivalent in India. It's called Upekkha. So of course, there I played the role of coach and a VC, but now I'm back in the playground. So right now I'm here at humantic as a chief of staff, but my primary role is to take care of the customer success and partnership teams. Right? Matt, you asked, what is the KPI that I'm really obsessing about right now that would be delivering value to customers? Right? So, of course, there are lagging indicators like NPS and CSAT that you can measure them with, or leading indicators like product adoption and usage itself, right? So that's what I'm currently obsessing about. I know humantic is a great product. It can really help people achieve great results. So I'm extremely passionate about helping people realise the value of the same. So that's what I'm trying to do right now.

The Problem Humantic Solves

Jasmine Gruia-Gray

Thank you. Hi, Rohit, and welcome as well. I love that you said that you're back in the playground. Maybe you can help describe, at a high level, the problem that humantic solves and how you're positioning humantic today.

Rohit Veerajapa

Okay, thank you so much Jasmine for asking me that question. Well, if I have to say, humantic helps sellers understand their buyers better, that's what we do now, usually I ask people in the current world, right? What is the bottleneck? Is it the seller's productivity? Is it the buyer's mindshare? Do we have really a dearth of sellers hours, the number of hours that they can work, or is it the mind share of the buyer, primarily because of the number of emails, calls, messages that they receive, right? And the overwhelming response I get is the buyer's mindshare. So that is what we help solve. We help solve the buyer's mindshare. We are a buyer experience tool, right? So we believe that there are two parts to every sale, right? One part is what the buyer wants. Second part is what the organisation needs, primarily because we're a B2B sales tool, right?

To understand the buyers wants, I again ask a simple question. I usually ask people that if you have a client who the native Japanese speaking client, and you had the ability to speak Japanese, would you not sell to them in Japanese? And overwhelming answer again, is yes, we would. And my counter question to them would be, then, why do you not sell in their personality style, but still in your personality style, right? And that is the aha moment that people realise, yeah, maybe they've left a lot on the table. So that's what we do as one part of a product, which we call people intelligence. We help understand the wants of the buyer, how they want to buy the product.

And of course, the second part, which is the needs of the organisation. There we have an account intelligence tool. It's typically wherein you spend about 30 seconds of your effort, and it saves you 30 hours of effort, if not 30 days of effort in researching the account that you're selling to. It typically understands the goals, the needs, the challenges, and the impact of not achieving those goals for that organisation, and then it marries it with your own solution and how you can help them. So this is what we do. We on one hand, have the people, intelligence part of the product. On the other hand, have the account intelligence part of the product, and together, we call this the complete buyer intelligence, and that's what we do in humantic.

Company Origins and Evolution

Matt Wilkinson

I was fortunate enough to meet Amarpreet, the founder and CEO of humantic, and it was three years ago at a Sandler sales and Leadership Summit in Florida. And I was curious how humantic started, because I've only ever experienced humantic as a sales tool. But if I'm not mistaken, the company started with a slightly different focus, or at least the technology did. So can you tell us a little bit about how the company was founded, and maybe about a little bit more about that shift and how you've ended up helping sales people?

Rohit Veerajapa

Well, that's quite a bit of a story, right? The story begins with this one mad man that we could call Amarpreet, and this mad obsession to humanise the internet, right? And one sliver of it is what you see today, which is trying to humanise sales. But of course, Matt, you got us that we did not start with the sales use case. Initially we started with the HR use case, right? We felt that the people intelligence part of the product that we had, then could be very useful in a hiring use case, because it can help the recruiters understand their candidates better and hire the right people. So happy that sometimes you don't choose the market. The market chooses you. So while somebody asked us for sales use case, and we had that also available, we just realised that the buyers mindshare or the buyer experience was a bigger and more acute problem, and we've definitely seen more traction there. That is why we've pivoted away from the HR use case, even though we do have a few customers from the early days that we yet serve. But as a company, we've shifted focus from the HR use case to the sales use case.

People Intelligence vs Account Intelligence

Jasmine Gruia-Gray

I'd like to go back a sec to the two components you described, the buyer, intelligence and account intelligence components. Is it fair to think about this a persona perspective and an ideal customer profile perspective.

Rohit Veerajapa

Well, if we have to break it down, I would break it down into the people perspective and the company perspective, right? Because in every single sale, there are two components, right? You're selling to an individual or a bunch of individuals, it's very important to understand what makes them tick, right, because there's a famous saying that says everybody buys irrationally and justifies rationally. So everybody have their own set biases. They operate a certain way, like when Matt read out my profile at the starting of the show, it said that, I really care about the impact that the product can cause. Because in humantic parlance, which is the DISC personality, I am somebody who is a speedy type of a personality, so I'm pretty dominant. I really care about the impact that my work causes. So that is where it is. Whenever I'm in a buying equation, I look at what impact the product can cause, and not just, who is the seller? I really don't care so much about relationships in a selling persona, in a selling equation context, but in real life, I really care about relationships and everything. So that is where every individual is different. Every individual in a selling context is different. That's why we have the people intelligence to understand the individual that they're dealing with and how to sell to them in their personality style, if we go back to the Japanese analogy, in their language versus our own language, right?

And of course, the other part, the other part, being the company itself, that is where Why is this person or individual buying? Of course, their wants are going to be addressed to the people, intelligence part of the product. But why are they buying? That is because there's a mandate from the organisation. So the organisation itself is an entity, and it has its own needs and challenges. That is what we cover with the account intelligence part of the product. So that is where to answer your question. In short, it is the individual and organisation that we typically try to cater to.

The Buying Committee Feature

Matt Wilkinson

Is there a feature that you think first time users of humantic, perhaps overlook, but could really help them in their day to day job as a salesperson?

Rohit Veerajapa

Yes. So typically, we have a bunch of features, right? We, of course, show the buyer insights. We tell them how to personalise their cold calls. We tell how to personalise their emails, as long as you put in a template or the pain points, we go ahead and produce an email in the way that they want to read, right? And, of course, we help them personalise LinkedIn connection requests and mails, etc, etc, right? These are features that get very easily used. But one feature that anybody who uses, but doesn't get used because it's a slightly different use case, is what we call the buying committee.

Now, so till now, we were talking about we personalise it for an individual, right? But more often than not, especially in a mid market and above sales use case you are selling to not an individual but a set of users that are buyers, right? That is where it is very important to understand the group dynamics, not an individual persona, right? That is why we go ahead and we build something called the buying committee maps, because it said that the best deals are lost in rooms that you don't get to enter. How do you win these rooms that you don't get to enter? By understanding the people there and building your crusaders who will fight your battles for you in your absence.

So we typically in the buying committee map, we go ahead and divide the buying committee or the people into four quadrants. We call them the friendlies, the sceptics, the crusaders and the neutrals. Neutrals are people who may or may not sway the deal towards you or away from you. Sceptics are the ones who have the ability to kill your deal. Crusaders are the people who will fight for you, and friendlies are the people who are most response friendly, right? We tell them, start with the friendlies. Use them to get to the sceptics and to the crusaders. Don't try to make a sceptic your crusader. They're never going to do that. So keep their scepticism at bay by constantly giving them data. That is the kind of personality they are. And with the crusaders, they are extremely demanding and rigorous. Once they believe in your product, they're going to fight your battles for you.

So we call that a Magic Quadrant, because it is borderline magic. We've had our users come back and tell us stories. I have one user who told us that she got a deal, a million dollar deal, back from the dead because she understood the buying committee and started taking the right approach with the right person. So that is where it's an extremely powerful feature. But of course, it comes later in the sales cycle, and not when you're prospecting, etc, etc. So it sometimes gets used. It's extremely effective. And anybody who uses it swears by it.

Matt Wilkinson

I have to say, it's definitely one of my favourite features, especially when you're selling online. Because one, it's great to know who you're selling to, but if you're on a zoom call, like we are now, and especially if people don't put their cameras on, or it's a room where you can't really see everybody actually in the room, because maybe it's a conference room, I find that it's incredibly helpful to then be able to direct bits of information to different people based on where they might sit, and being able to get that insight as to how are they going to want to receive information and where do they sit in that group? So it's definitely been one of my favourite tools since I've started using it. So I definitely recommend anybody that's getting to play in the playground, as you said earlier, that they can go and explore that.

Rohit Veerajapa

Thank you so much for the kind words, Matt. Whenever customers speak well about the product, it's always music to my ears. I'm really glad that it's adding value to you. Thank you so much.

Competitive Advantage and Personalisation

Jasmine Gruia-Gray

So continuing on that thread, as we're all well aware, sales and marketing folks, when they're looking to adopt a new tool, are all about how this is going to give them the competitive edge. So maybe I would ask you, how can learning to use a tool like humantic AI give someone an edge compared to their peers?

Rohit Veerajapa

That's a pretty straightforward answer in my mind, right? But for the larger audience, everybody today are using AI tools, right? What are AI tools like GPT, Claude, Gemini, etc, etc, right? And everybody are using these tools to write emails. Why not? Because it's writing better emails. Of course, it is writing better emails than majority of the individuals that they themselves would, but they're not using it for that reason. They're using it because it's easier to do it. So everybody are doing it. How are you going to differentiate is your competitive edge? First of all, if you're not even using ChatGPT to write your email. Okay, then God help you, right? But if you are, then everybody else is also doing that. How are you going to get the competitive edge? The competitive edge comes by understanding your buyer better, not by selling more. It is by helping them buy more, right? So that is where humantic comes into play. And by using humantic, you can understand their needs, their wants, how they like to behave, what are their biases? What moves their needle? Using these you can personalise your sales techniques, of course, in all channels, right, be it social, be it calls, or be it emails, so that you have the competitive edge others may not.

Jasmine Gruia-Gray

I think my interpretation of what you're saying comes down to one of Matt and my marketing heroes, Mark Schaefer, who says, gotta cut through the pandemic of dull and really, the value proposition between the buyer, the people intelligence and the account intelligence gives you that background to cut through that dullness.

Rohit Veerajapa

Absolutely like, if I were to give an example, right? We have a client called Rohit, and I was showcasing the product to him, and then he was like, Hey, why don't you write a cold email to me using your humantic? So we pulled out the product, and I just went ahead and gave it a template where I'm giving my selling points and usual pain points that a client would have. And what humantic did was it went ahead and it picked up his latest LinkedIn post was the fact that he had started this leadership position in this company, and then it congratulated him for that. Then went ahead and understood what their CEO has said in public, or the problems they are facing, or the goals for 2025 and married it with our offering, and of course, listed all our solutions, and then said, Hey, can we go ahead and book a 15 minute video meeting?

And I asked Rohit, is this the email you would read? And he said, absolutely yes, because it shows that somebody has taken time to craft that. But let's be honest, if you were to do all of this work, it would easily take you about 10-15 minutes even, even if you're very fast, it would take you 10-15 minutes to research and write this email. And nobody has the time or patience to do that. That's where we come in, and we help you cut through the dullness that you spoke about right where we have this pitch slapping contest that's going on, right where every email is the pitch, every call is the pitch, every LinkedIn request is a pitch. How do you differentiate yourself from all the pitch slapping that's happening by going ahead and personalising and it's just not personalising based on the data that's available in the internet. It's also personalising based on their personality, right?

Like Rohit, if it's a D type you directly want to get into the meat of the email. Would not like fluff in the email. Would like the email to be extremely crisp. Let's take an I type personality, that type of personality might be willing to read a long email. They might want the email to start with a greeting, hey. How are you? Hope this email finds you well. But a D type personality may not need that. D type person is like, get to the point that's where every personality has a different way of reading the email, and humantic goes ahead and it does that end to end email personalisation for you, so that's where you cut through the dullness. You cater it to the way that they want to receive it.

Customer Success Stories

Matt Wilkinson

You've already talked about a couple of customer examples and proof points that have shown real impact. Do you have any other favourite stories of where humantic really shown massive impact?

Rohit Veerajapa

Oh, that there are way too many. So for anybody who's interested, we have a YouTube channel over there. We have a playlist with, at this point, I think, about 32 videos where we have CEOs of different organisations talking about the impact that humantic caused for them. A few that if I may say, is we work with a publicly listed company in DevSecOps, publicly listed company, and while we work with them, they initially said that they wanted a 30% pipeline improvement, right? But we ended up delivering 49% for one group and 151% for the other group. Right? So that is where sometimes we say that these numbers sound so too good to be true, right? Because, of course, on paper, it looks outlandish, right? But that's where we have all of this data. We have these people claiming it themselves, so I really encourage any of your listeners to just go ahead and check that out.

And we also work with another publicly listed company called Domo right, and their CMO Mohammed, measured and reported that they saw a 37% improvement in their win rates by using humantic. Right. So there are, I think Jeevan Fox, who's the CEO of Adstudio, said that the conversion rate was around 15% but after using humantic, it moved to 42 to 50% right? So that's like more than a 3x so I have stories after stories that I can share as to how it's been positively impacting people. Anybody who's interested in further understanding this should just go to the YouTube channel and look at the playlist. They'll get all the data points.

Working with Technical Audiences

Jasmine Gruia-Gray

Yeah, maybe we can pull on that thread a little bit. The audience for our podcast is mostly focused on what has been called the most sceptical audience on the planet, the scientists and the clinicians. Do you see any unique challenges in applying personality AI to technical audiences like that.

Rohit Veerajapa

In short, the answer is no right, primarily because the only limitations that we may face is with respect to data, and how much publicly available data is there for us to go ahead and predict whatever we are predicting, right? Usually I get this question. Another variant of this question would be, hey, does this work well for big companies? Does it not work well for small companies? So my answer is that, hey, we are agnostic, right? We are agnostic with company size, with people, etc, etc, because we are an AI company. And what does an AI model need? We have a proprietary machine learning model. What does our model need? It needs data. As long as there is data we can predict, and as long as we can predict, somebody can use that and apply it in their line of work. Now that is why, in short, the answer is no, because we are agnostic their profession.

Jasmine Gruia-Gray

And can one of those data sources be their publications? So PubMed, for example.

Rohit Veerajapa

Absolutely right. So we typically, right now, do not pull PubMed data. But if, let's say, they publish something and they want to use that for analysis, we have options where they can go ahead and add additional text, we will go through and, refine our predictions. Or if there's no other data available, just use that to make the prediction.

Privacy, Ethics and Compliance

Matt Wilkinson

So when I've shown a few people humantic AI, they sort of thought it's a bit magic. And also, there's been a few people that have sort of thought, wow, where are you getting this data? What concerns about privacy, ethics and sort of compliance are there, and so, yeah, sort of curious. Your stance on sort of that, unpacking that whole sort of AI, privacy, ethics and compliance and humantic's stance on that, because obviously, you're analysing people in a way that maybe is, could be uncomfortable.

Rohit Veerajapa

Totally curious, right? And that is where we are extremely prudent about data privacy ethics, compliance, etc, etc, right? So currently, all our analysis is on publicly available data. If they have publicly put it out there we are using that to analyse whatever we are advising about an individual. So for any good reason, let us say that they are unhappy or uncomfortable with the analysis, right and they do not want us to analyse them. Then we have a simple opt out option where they can go to our website and put their LinkedIn URL or their email address or their name and say, hey, I want to opt out, and we will make sure that we do not provide that analysis to anybody else, because that is the right thing to do right and we really care about people and their privacy, so we want to do the right thing.

With respect to compliance. Of course, we are GDPR and SOC two compliant. That is a testimony to the fact that we truly care about our customers and their privacy. In a nutshell, we are very vigilant about this. Whenever we get feedback, we develop internally, try to think and work towards making sure that it's a safer place for everyone.

Future of Sales and AI

Jasmine Gruia-Gray

Thank you. So I'd like to take a page from your background and ask you to think about your spidey sense. See the future of sales in the next two to three years, and the role of AI in that.

Rohit Veerajapa

So the future that I see, or that we see as an organisation, right? The future that we are seeing is that nine out of 10 tools today are about seller's productivity, right? Helping you send more emails, helping you make more phone calls, helping you automate LinkedIn, email campaigns, etc, etc, but that is where we've taken the contrary view we've gone to the other side, and we are saying, Hey, we are going to solve the buyer's mindshare. We are going to humanise selling. We are going to ensure that we are not about selling more, but we are going to ensure that people buy more, right. So we are not the more people we are, the better people. So that is the future that we are betting on, that the general audience is going to get more done, but we want to get things done better, right? So that that's what our spidey senses tell us, and that's the future that we are trying to create, right? Because there's this thing I don't know who said it. They say, What's the best way of predicting the future? And the answer is, by creating it. So, we are trying to create the future. Let's see how that pans out.

Jasmine Gruia-Gray

So I think said a little bit differently, you're looking at lifetime value of a particular account.

Rohit Veerajapa

Absolutely right, not about transactions. That is where during a qualifying process, because we sell our product to someone else, right? So even we qualify. So when we are qualifying? One of the things that we see is whether they're doing velocity sales or long tail sales. If they're doing velocity sales, we tell them this is not a product for you, because in velocity sales, you have to play the volume game, you have to play the numbers game right? You have to send so many emails. You have to make so many calls. So wherever the sales cycle is longer, right, where it's typically mid market and above. Sales cycle is anywhere between three to six months can be a year. Here you don't know how you lost the deal, and when you lose the deal, there is where people do want to leave any stones unturned to ensure that they are doing everything in their power to make a sale happen. Those are the type of people who can appreciate this better. And those are the people who really care about the lifetime value. They really care about the churn, etc, etc. So those are the people that we try to sell to.

Jasmine Gruia-Gray

Yeah, so just wanted to interject for our listeners. Rohit is talking to all of you folks who have robotics types of products, who have high end scientific instrument type of products. So listen, listen up. I think this is a really valuable point for that sales and marketing cycle.

Metrics That Matter

Matt Wilkinson

And sticking with the sort of the Spider Man sort of themed questions. This is a tool that with great power comes great responsibility. And so if I was a chief revenue officer or a head of sales, what is the one metric you think that I should retire based on when I implement humantic AI, and what's the one that they should adopt instead?

Rohit Veerajapa

Oh, well, maybe I wouldn't limit to a single metric, but I would continue the overarching theme that I've been speaking about over here, right? I would tell them to stop measuring volume, because, unfortunately, the nature of the beast today is such that SDRs are measured on how many contacts are they reaching out to, how many emails are they sending? How many calls are they making? Right very similarly, with AEs, how many accounts are they handling? What is the entire pipeline coverage, etc, etc. That's where I would request CROs to look at this slightly differently. I would request them to measure the number of personalised emails or personalised touch points that you're making, whether it could be a call or LinkedIn or email or even in a meeting, right whenever you're going into a meeting, how prepared are you? Do you know each of those individuals? Do you know collectively, how they behave? Do you know what is the needs of the entire organisation?

Gone are the days where you just Google something, or you go to their website and pull out two data points. Today, everybody are prepared, right? So go ahead, make sure that you stop those vanity metrics and start looking at these value metrics, because Amarpreet has a very good analogy out here, right? He usually speaks about something called the Dust Bowl effect, which happened in around the 1930s in parts of the US, where, typically, people resorted to over farming, and then that left the soil vulnerable to wind erosion. And today, with the incoming of AI, we could have that same dust bowl effect where people are sending too much, too many outreach and that could really make it a very worrying situation. So that's why I would request every CRO to move away from these vanity metrics and move towards value metrics and see how that works out for them.

Making the Case to Sceptics

Jasmine Gruia-Gray

I really like that analogy to the Dust Bowl. You're right. We're being inundated with lots of emails and lots of outreaches that are dull and not on point. So if I had to explain the value of humantic AI to a sceptical colleague, what's the most straightforward way to frame it so that they'll give it a fair shot?

Rohit Veerajapa

Well, in my humble opinion, what works best is my Japanese buyer analogy. Okay, nine out of 10 times people get it and they have an aha moment. Now, I would encourage you to go ahead and use the Japanese analogy, right? If we would speak somebody's language, let's be honest, we all sellers, are used to persona based selling, right? That we typically try and understand the persona. We are like, Hey, let's try and understand the culture, let's try and understand their demography, let's try and understand XYZ, right? Everybody have been doing that, but it's time to start selling, not only to the persona, but also to the person, because each individual is different, and with AI, you can do more. So I would tell people is to go ahead and try and sell to the person, to the person, and not only the persona. So maybe that is how you could explain it to them, right?

Where to Learn More

Matt Wilkinson

That was brilliant. Rohit, so many good questions that you've answered today. But my final question is possibly the easiest one of all, where should people go if they want to learn more about yourself and humantic?

Rohit Veerajapa

Oh, well, we are humantic.ai, h, u, M, a n, t, i c.ai, please find us on our website. We go by the same handle on all social media accounts so you could find us there.

Matt Wilkinson

Brilliant. Thank you so much. And thank you for the really fun discussion today. I've really enjoyed it.

Jasmine Gruia-Gray

Yeah, thanks for sharing the humantic AI advantage, as well as your spidey sense. We really appreciate it.

Rohit Veerajapa

Well, Jasmine and Matt, thank you so much for having me over. You've been great hosts, and I thoroughly enjoyed this conversation. Thank you so much.

Q&A

How do I start using buyer intelligence without a big budget?

Begin by researching your top 5 prospects manually using LinkedIn, company websites, and recent news. Look for personality indicators in their posts and communication style. Note whether they prefer direct, data-driven messages (D-type) or relationship-building approaches (I-type). Even basic personality awareness can improve your outreach effectiveness before investing in AI tools like humantic.

What's the Japanese buyer analogy Rohit mentioned?

If you could speak Japanese to a native Japanese buyer, wouldn't you? Most salespeople say yes. So why don't we "speak" in our prospect's personality style instead of our own? This means adapting your communication - D-types want crisp, direct messages while I-types prefer warmer, relationship-focused approaches. It's about matching their preferred communication style, not forcing your own.

How can I identify the buying committee without expensive tools?

Start by asking your champion who else will be involved in the decision. Map them into four categories: friendlies (responsive), sceptics (can kill deals), crusaders (will fight for you), and neutrals (won't influence much). Begin conversations with friendlies, provide data to sceptics, and focus on proving impact to potential crusaders. This framework works even with basic LinkedIn research.

What vanity metrics should I stop tracking first?

Stop obsessing over email volume, call quantities, and total outreach numbers. These create the "Dust Bowl effect" of over-farming prospects. Instead, track personalised touchpoints - how many emails reference specific company challenges, recent achievements, or personality-matched communication styles. Quality engagement metrics matter more than quantity in complex B2B sales cycles.

How do I personalise emails for scientific audiences specifically?

Research their recent publications, conference presentations, or LinkedIn posts about research challenges. Reference specific technical problems they're facing, but adapt your communication style to their personality. D-type scientists want direct impact statements, while C-type researchers prefer detailed methodology. Always lead with how your solution advances their specific research goals, not generic benefits.

Shownotes and transcript

Shownotes

Most marketing personas are beautiful documents that get created once and forgotten forever. They focus on demographics like age and hobbies instead of what actually drives buying decisions.

This episode is for biotech startup marketers who want to create personas that actually get used by sales, marketing, and customer service teams. Matt Wilkinson and Jasmine Gruia-Gray reveal how to transform static persona documents into interactive AI tools that help you practice sales calls, test messaging, and understand buyer motivations. The key insight: how to craft ICPs and personas that work - and that they ARE NOT the same thing.

What you will learn:

• Why traditional persona templates focused on demographics miss the mark completely
• How to use AI deep research tools to build detailed personas in 15-20 minutes
• The five rings of buying insight that actually matter for B2B decisions
• How to create "persona AI" for ongoing role-playing and message testing
• Why personas should focus on jobs to be done, not personal characteristics
• How to keep personas alive by adding customer interview transcripts to AI models

Keywords: buyer personas, customer personas, jobs to be done, AI marketing tools, persona development, B2B personas, life science marketing, biotech marketing, customer research, buying behaviour, persona AI, custom GPT, sales enablement

Transform your dusty persona documents into powerful AI tools that your entire team actually uses. Watch this episode, subscribe for more actionable life science marketing tactics, and visit our website for persona templates and AI tools.

From Static Documents to Persona AI: Making Customer Personas Actually Work

In this episode of 'a splice of Life Science marketing', hosts Matt Wilkinson and Jasmine Gruia-Gray explore how to create personas that go beyond static documents to become living, interactive tools. They discuss the evolution from demographic-focused templates to AI-powered personas that can help with role-playing, message testing, and understanding the jobs customers need done.

What Makes an Effective Persona

Matt Wilkinson

And welcome to a splice of Life Science marketing. I'm Matt Wilkinson and

Jasmine Gruia-Gray

and I'm Jasmine Gruia-Gray. Hi, Matt. How you doing today?

Matt Wilkinson

Good, good. It's a little bit steamy here in Northern Virginia,

Jasmine Gruia-Gray

quite warm here in the UK for once, which is nice. Today we're going to talk about persona. And that's probably quite an interesting way to start really is because you, if you create a persona of an English person, you might actually say that one of the things that they're always talking about is the weather, and you might say that they're always complaining about the rain. But before we delve too deeply into persona, it's probably worth starting with, well, what actually do we mean by and a persona is a really helpful way to represent a group of our customers. The way I like to view them is that within our ideal customer profile, we have a buying group, and within that buying group, we typically have people that are our primary buyer, and then we have users, and then we have those that are also involved in the buying decision. So that could be procurement, finance, health and safety, quality, whoever it is there's often quite a few other people that are involved in that buying group itself, and we can create across our different groups of ideal customer profiles, we can create persona. And those persona are these aggregated views of who they are. And so we can start off by thinking about trying to humanise our messaging around the roles that they play in the jobs that they do.

So for example, you might be looking at a lab, so a bench scientist in the lab, and so you know that first thing in the morning they come in, there's a certain number of tasks. Maybe they're checking their email, they're doing a few things, but then they're going into the lab, they're putting on their lab coats, they're putting on their safety specs, and there may be going on, there's certain tasks that they're getting on with. And so it's about understanding what their day looks like, what keeps them awake at night, and really, what gets them excited and what motivates them. And if we can understand that across a group of customers, we can speak more closely to their needs, wants and the jobs that they need to be done.

From Templates to Jobs to Be Done

Jasmine Gruia-Gray

Yeah, just sort of pulling on that thread some more on the jobs to be done. I think that's where persona development has really taken off in the last three, four years. And the analogy I like to use actually works really well from what you and I are wearing, I think in the before times when people thought about personas, it was very much a template. It was very black and white, and it was some cutesy name for the persona, what their age was, what their hobby was, all stuff that really, by and large, wasn't relevant for how they make decisions, what motivates them and what the jobs are to be done. Today, we're living in colour. We're living in a world where anything goes, where the templates are okay. I mean, I absolutely have my playbook of templates, but we think about the template of a persona from a much more human perspective, not only jobs to be done, but behaviours. What are the steps they take in making a decision? What are how do they think about what they really need, what they don't need, what they're fearful of, what makes them successful?

Matt Wilkinson

I absolutely agree. I think there's another thing that's really important about persona is that once we've created them, they can be a real pillar of coherence between sales, marketing, customer support, about around how are we going to ensure that our interactions with this group of people that we're tying up together as a persona within a single person, that we're going to make sure that we're trying to address their needs and wants and what they're trying to accomplish, and that we're able to really be able to provide better customer experience overall. I think that's the big thing. Because if we try to, if as human beings, we try to address every single person as an individual, we just can't cope with that. I mean, research shows that we can have maybe 100-150 friends before we sort of have to move out and sort of just sort of calling them acquaintances. There's a reason why communities kind of top out at the size they do. That's why organisational structures don't get too big before having bigger structures in place, because as humans, we just can't cope with that many connections. And so this is really a way to help us simplify that, but also keep it very human.

Jasmine Gruia-Gray

Yeah, I think there's a really important point you made at the beginning, which is that personas, and I'd add also that ideal customer profiles, the bricks and mortar, are really the unifying elements across a commercial organisation, and it's really critical that sales, marketing, customer experience service, FAS are all on the same page regarding the ideal customer profiles and the personas, so that you're all going to be targeting the same segments, the same potential buyers with a very similar message, and a message that you've tested, that you know is going to resonate with them.

Understanding the Complete Buying Group

Matt Wilkinson

Absolutely, I think that's why it's always so important to try and get those people around a table to agree on not just who are ideal customer profiles and what are they, but then who are personas and what do they look like. And I think that there can be huge benefit gained from going through that process, because you get that unifying kind of coherence around who's important to speak with, who do we speak with, who do we know, and also maybe getting insight about who don't we know, who do we need to spend more time getting to know you know, the number of times that you speak to clients, and the sales team, are doing really well. They're great at getting into conversations. They're great at putting in proposals. But then once it hits procurement, things go quiet. And so then maybe there's a conversation that we need to look at. What is it we need to do to make sure that the procurement like us as well. And so then, by creating persona around procurement, understand what is it that we're not doing that's not communicating with them. Maybe we need to get them in the room as part of our buying process. Maybe it's actually we just need some really simple sales enablement messaging that allows us to continue to have messaging that resonates with that persona.

So I think that's a really important thing to do, because we understand who's in our buying group, we can better understand what jobs, what's important to each of them, what jobs are they measured on? And so there's some really interesting ways about taking that forward, rather than just taking the very vacuous kind of approach of, well, they're going to be this old and be married with kids, or they're going to have these sort of demographic traits, but actually understand what are they measured on? And because each of the people in that buying group are measured in different ways, and I think that's a really important thing that we have to look at. So we have to try and create coherence within our buying group that helps make them that helps the buying group as a whole come to a decision.

How Many Personas Do You Need?

Jasmine Gruia-Gray

So in your experience, how do you think about this? Is there an ideal number of personas that a company should have, or a product group should have.

Matt Wilkinson

So that's a really nuanced question. I think, in the days before AI, or at least before Gen AI, I would have said that trying to understand, in detail, who your key people you need to influence, and really digging into making sure that we're creating, we've got messaging for maybe, typically, there are five roles with any buying group, we sort of listed them out earlier, sort of the initiator, the user, the initiator is kind of the problem that recognises the problem. You have a champion, who's there that's also part of that similar group, but is there and sort of willing to take the drive to really solve the problem. You have end users, you have procurement, and you have finance, so you sort of have these interested parties in completing sale. And of course, those roles will change. They will be different depending on who you're selling to what the organisations are like. But I would have said, start off with any particular type of sale, with having about five persona within a segment, within an ICP group, however you're creating that segmentation.

The problem I've always found with persona is that very often what will happen is that agencies will go away. You'll have a fantastic meeting where you'll get everybody around the room, great workshop. Everybody's really enthused, and they're agreeing on things, getting these, the notes that come back sort of half formed. People then go away do some desk research, spend hours kind of trying to fill in the gaps. And then once you've created these, they get turned into beautiful documents that then just rust away on a hard drive somewhere. They get used for the initial piece of work, and then people forget them.

Using AI for Deep Persona Research

Matt Wilkinson

And so a deep research came out. I realised that rather than doing the manual work yourself of going through LinkedIn profiles job descriptions, you could actually create a way of using the deep research tools themselves to go off and analyse and think about a group of profiles that are on LinkedIn that fit your persona, looking at their job descriptions and then really trying to enrich what the conversations that you'd already had with genuine data that could really help to help you better understand who you're speaking with. And so that's something that I've been using now for, I guess, the best part of a year.

And then on top of that, rather than just having these sort of persona documents that are having to analyse and read through and go right, what was their key questions that they asked as part of their buying journey, what matters most to them? We could start creating custom GPT or AI assistants where we're actually uploading those into as a knowledge base within the AI itself, and then using that to not only be a queryable kind of persona that we could actually interact with and ask questions of rather than having to us ask the questions of the document. We can just go into the AI and ask the chat bot ourselves, but we can take that a step further and then actually interact with them and use that as kind of as a an in silico version of our persona themselves to do everything from brainstorming, testing messaging, creating messaging, the whole gamut of the sorts of things where you can almost have a virtual customer at your desk all the time. And I think for me, that's been one of the real eye opening ways that I've certainly changed my workflows, and I think that we can really look at making these personas so much more useful and helpful and shareable.

Creating Unified Commercial Teams

Jasmine Gruia-Gray

I can't say enough about this. I think this point is so important, and links back to what we were talking about earlier, about creating unification across a commercial team, because once you can agree on the jobs to be done and on, as Adele Revella calls it the five rings of buying insight, priority initiatives, success factors, perceived barriers, buyer's journey and decision criteria. And then you operationalise that to an extent, as you say, in silico with a custom GPT or a gem, or whatever your favourite LLM is, you can now use that to create sales scripts, to create sales presentations, to really evaluate your website, to evaluate your marketing tools and your sales tools to helping your customer experience team, because now you're all working on this using the same platform to think through what that outbound messaging is going to be like, and what that experience is going to be like from a proxy persona.

Matt Wilkinson

Yeah, absolutely. And I think that especially when you've got the ability to share these now, having everybody using the same data source or the same AI and silico version allows you to have consistency across the silos. And I think that's then really important. Now, of course, one of the criticisms has always been a persona has always been that there are one and done. I don't think we're quite there yet, but you can imagine a future where you build these persona into an AI that sits within your website, you can imagine that then you're learning not just on what does what we're telling it do within a website, but what actions do they actually take when we can recognise a specific persona? And then, if we're at that point, can we use data collection to then enrich them? I'm sure that there's, there are ways to do that. I, clever ways. I don't think there's a product out there that does that just yet. And I'm sure that HubSpot and Salesforce are probably working on this as I speak. But I'm sure there's ways to do that.

Role-Playing and Voice Coaching with AI Personas

Matt Wilkinson

I think there's also another thing I've seen is that there are now these speech coaching tools that companies like use it usually, and a few others, where you are able to not only understand to look at your customers and understand them better, but you can build those persona into a voice, into a conversational AI, and then you can set up scenarios. So let's just imagine that we've built our AI, we built our persona for a lab scientist. If I'm a salesperson, I'm going into a call with that lab scientist. Maybe I take that same person and build it into a conversational AI, and within that I can then actually practice a specific scenario. Let's just say that I'm going in and meeting them for the first time and I'm trying to introduce a new product range to them. I can actually practice that conversation. I can practice that cold call. And so there's a whole wide range of ways that, once we've got this, these data sets, that we can start using them to not just create better and more, but also to be better and more with them, to actually be able to practice with them. And I think that's just one of the really exciting things that I'm seeing.

There's also ways to look so get buyer insights around their personalities. And so you could imagine that there are different DISC profiles or Big Five personality traits. And you could look at those and go, okay, so if we've got these different types of personality, because we know that people aren't all the same, even if they're doing the same job. Well, we can then look at what would it be like if that person is more of a D rather than an I in their DISC profile, and be able to practice those same conversations with different personality traits. And I think that then just takes things to a whole different level, where all of a sudden we can be more prepared for the conversations that we might want to have.

Jasmine Gruia-Gray

Yeah, the word that comes to mind when you're describing this use case is informed, so it will help. It's the classic role playing that we used to do at sales meetings, right where somebody in marketing would play the role of a customer, and somebody in sales would have to practice their pitch, but the scenario you're describing with an AI helps you actually build something that's much more informed, much more real world and more scalable, so that it doesn't just live in the sales role playing, it can live in the customer experience role playing and how they deal with different scenarios and being on the front line as they often are.

The Importance of Real Customer Interviews

Jasmine Gruia-Gray

I think the other point I want to pull on is that just because we can train LLMs to be a very good proxy for customers and for personas, that doesn't negate the need to actually do the hard work and do some of the interviewing. And I really want to encourage our marketing colleagues and listeners to get out there and get that experience and roll up your sleeves and really understand face to face what that buyer's mindset is, what that buyers context is and how your company and products and services can be useful to that buyer firsthand.

Matt Wilkinson

There's nothing better than that firsthand experience of what it's like to walk in the buyer shoes for even if it's just a few metres or a few miles. But I think there's something else we can do as part of that, if we're given permission, if we can record those interviews, or we can at least take notes and add to them, we can add those verbatim quotes, those transcripts, to the data set as well that we're going to add in. So rather than just having a very polished persona, the AI doesn't really care how much data we give it. So we can then go through and actually add transcripts after transcript to these that will then actually only enrich the data. So we can constantly update these by simply by adding extra data to them, whether you're updating the same file or just adding extra files to say, hey, here's a couple of transcripts from interviews with this persona. You can do all of that and really start to build out a wealth of real customer voice, and have that shining through in actually, the way that they might speak, in the way that they might want to be spoken with. And so I think that's a really powerful thing.

Another reason to go out and speak with customers, not just because it's great for each of us individually to get that sense of that humanity, but because we can add that in, and we can then share those insights across the entire organisation through these in silico versions of our customers.

Jasmine Gruia-Gray

Right? And it's not only your customers that you can interview. You can also interview folks that didn't go with your solution, that chose your competitors solution, or chose some internal solution.

Matt Wilkinson

Yeah, absolutely. I mean, I use the word customer, but I think it's target, or potential customer, prospect, whatever we want to call them. I think that there's absolutely that it's really important that we do get that sense of, well, who's buying from us, who isn't, and what are the differences? Is it just because of brand, or is there something inherently different about the way that we appeal to certain groups of people and not others? As you know, when we're selling things, some decisions are made purely based on the procurement level, where you have a single supplier and or you have a group of suppliers, and if you're not part of that supplier list, you're not in the game. And so then it's a case of, well, how do we get in the game? But being able to just even understand that, I think, can be an incredibly powerful part of this sort of conversation.

Common Persona Mistakes

Jasmine Gruia-Gray

Right? So just before we wrap up as a take home message, what do you think are the two biggest mistakes that marketers make when they create buyer personas?

Matt Wilkinson

So I think there's so many mistakes we're about creating and then, but I think the biggest mistake is that often personas get created and then they used and forgotten about. So I think the first one is that they get forgotten about that, I've seen some beautiful persona being created, and then people aren't even using them when you're having to remind people to go back to the persona, because the information is in them, so they're not using them as a source of truth. So that's the very first thing to remember, is to use them. And I think that by turning them into an AI representation of those personas, actually makes them so much more useful, you just get far greater ROI for those efforts. Anyway. So that's number one.

And I think the other is, by being a bit too shallow with the questions that you're asking, I think that the five rings of buying insight so powerful understanding what jobs to be done, any of that insight that we can really get included? Yeah, there's things that are important about what channels they want to communicate through, but the more important thing is, is actually, how do we move them emotionally? How do we actually meet what their needs, the jobs that they need to be done? How do we actually help them achieve what they're looking to achieve? And if we can answer those questions within our persona, all of our messaging will then be just so much stronger, more focused, and will resonate so much better. And that's really, I think the big thing that I'd focus on is make it so that's usable. And it's not just pretty fluffy pictures. It's actually something that really can be used by marketing, by sales, by service, and anybody else that's interacting with our customers and prospects.

Starting with Personas in Campaign Development

Jasmine Gruia-Gray

To sort of add a finer point on everything you've said, and I agree with is I sort of appeal to product marketers and marketing managers when you're creating your next campaign, start with the persona. Who is it that this campaign is appealing to, what are their jobs to be done? What are their motivators? What is their buying context? And then put the campaign together, and then after the campaign is executed, and you look at the data, who has responded to your campaign, not only from a persona perspective, but also for an ICP perspective. Are there any surprises there? Are there any outliers from the trends that help you to then loop back to your ICP and to your personas and tweak them and lift them up even further?

Matt Wilkinson

Yeah, or even find that you've got a segment that you didn't realise that you were actually, that you were able to serve, that needs to be treated differently. Maybe you've hit an outlier that actually should be a separate segment, and we can do even more with that with those outliers. So I think you're absolutely right. I think being able to treat everything a little bit like a an experiment and a hypothesis. And knowing that very much like in science and in chemistry, when I went to do my first year of chemistry, basically they told us everything that we'd learned up to that point was wrong and that the models we'd use were all too basic. And as you go through doing organic chemistry and inorganic chemistry and physical chemistry, all you're doing is applying a model to serve a certain to solve a specific problem. If you try to use the most complicated model, the kept scientific model here, I'm not talking about large language models, but the most complicated scientific model to solve a simple problem, you'll be just wasting too much time, but you'll get the same result 90% of the time, 99% of the time. So what you're really looking for is finding the appropriate model.

And I think this is really the trick here with persona, is that they are an appropriate model for a whole range of communications and helping us get communicate better with a group of our customers or prospects. But they're not perfect, and of course, we do need to layer on top of this personality and individuals and the individual context of every customer. If we were taking to this to Account Based Marketing and a key account, we might create individual personas within a market of one company. So I think we can, and with AI, the way it is now we absolutely have the ability to do that. We can scale these things far quicker, and then we can use these in a way that actually makes that make sense. Whereas if we were doing this on a basis where we're looking at hundreds of persona potentially, but you're having to do the analysis, and kind of, you or I are having to try and understand the difference between somebody in the same job role in two different companies. We wouldn't be able to necessarily do that. I think the subtlety of that's different. But within the AI, maybe actually within some of our most important accounts, it's worth us doing that. It might not be, it's a hypothesis that we can test. Does it make sense to do that? But if we treat everything like an experiment, where we start off with a hypothesis that this is a good this is going to be good enough to make a difference, and for us to be even just 1% better than if we don't use those, that's where I think it's just so important.

Jasmine Gruia-Gray

Yeah, I think that's fantastic, and I think that's a great message to end on. This has been tons of fun. Thank you for sharing and helping us all learn, Matt,

Matt Wilkinson

and thank you, Jasmine, it's been great fun as always.

Jasmine Gruia-Gray

And thank all of you for joining us on a splice of Life Science marketing. Bye, for now.

Matt Wilkinson

Bye.

Q&A:

How do I create "persona AI" using tools like ChatGPT?

Start by gathering LinkedIn profiles and job descriptions of people who match your target persona. Use AI deep research tools to analyse these profiles and create a comprehensive persona document. Then upload this data to a custom GPT or AI assistant, training it to respond as that persona. You can then ask it questions, test messaging, or even practice sales conversations with this AI version of your customer.

What are the five rings of buying insight I should focus on?

According to Adele Revella, focus on priority initiatives (what triggered them to look for solutions), success factors (what outcomes they need), perceived barriers (what concerns they have), buyer's journey (their decision-making process), and decision criteria (how they evaluate options). These insights matter far more than demographic data like age or hobbies when creating B2B personas.

How do I prevent my personas from becoming forgotten documents on a hard drive?

Transform them into interactive AI tools that your team can actually use daily. Create custom GPTs trained on your persona data for message testing, role-playing, and strategy discussions. Make personas part of campaign planning by starting every campaign with "which persona is this for?" Use them in sales training for conversation practice and objection handling scenarios.

Should I interview people who didn't choose my solution when building personas?

Absolutely. Interview prospects who chose competitors, went with internal solutions, or decided not to buy at all. This reveals crucial insights about your positioning gaps, unmet needs, and decision criteria you might be missing. Add these interview transcripts to your AI persona models to create more realistic and comprehensive customer representations that account for different buying scenarios.

How many personas should I create for my biotech startup?

Start with about five personas per ideal customer profile segment, covering key buying group roles: the problem recogniser/initiator, champion, end users, procurement, and finance. With AI tools, you can create more personas efficiently, but focus on quality over quantity. Make sure each persona represents a genuinely different set of needs, motivations, and decision criteria that require distinct messaging approaches.

Shownotes and transcript

Shownotes

Most life science marketers confuse ideal customer profiles with personas, costing them 37% of their marketing budget according to Gartner. These are fundamentally different tools that serve different purposes.

This episode is for biotech startup marketers who want to stop wasting budget on the wrong audiences and start targeting with precision. Matt Wilkinson and Jasmine Gruia-Gray break down the critical difference between ICPs and personas, then share a practical five-step process for crafting both effectively. The key insight: how to craft ICPs and personas that work - and that they ARE NOT the same thing.

What you will learn:

• Why ICPs are about companies and personas are about people within those companies
• Philip Kotler's five requirements for effective market segmentation
• How to use AI tools for deep persona research in 15-20 minutes
• The iterative process between segmentation, ICPs, and persona development
• Why this must be a cross-functional team effort involving sales, marketing, and field applications
• How to create "persona AI" for ongoing message validation and strategy

Keywords: ideal customer profile, customer personas, market segmentation, biotech marketing, life science marketing, customer research, account based marketing, buying groups, firmographic data, persona development, customer targeting, marketing segmentation

Stop targeting the wrong customers and start maximising your marketing ROI. Watch this episode, subscribe for more life science marketing strategies, and visit our website for additional segmentation resources.

ICPs vs Personas: The Critical Difference Every Life Science Marketer Must Know

In this episode of 'a splice of Life Science marketing', hosts Matt Wilkinson and Jasmine Gruia-Gray tackle one of the most misunderstood concepts in B2B marketing: the difference between ideal customer profiles and personas. They explore Philip Kotler's segmentation requirements, share a practical five-step process for crafting effective personas, and demonstrate how AI tools can revolutionise customer research in minutes rather than hours.

Philip Kotler on Market Segmentation

Jasmine Gruia-Gray

Welcome everybody. I'm Jasmine Gruia-Gray,

Matt Wilkinson

and I'm Matt Wilkinson, and today we're going to be talking about segmentation, ideal customer profiles, and how these are not personas,

Jasmine Gruia-Gray

right? So I'd like to kick off with Matt is a quote from Philip Kotler, the esteemed marketer. He said, All customers are important, but some are more important than others, which sort of comes to the fine point of segmentation, and even further from that to ideal customer profile. And he said, If markets are to be segmented and cultivated, they must meet certain requirements. Segments must be measurable, sustainable, accessible, differentiable and actionable.

Matt Wilkinson

I'd fully agree. And I think you can take that even further to looking at actually down to how do you, once you've got an idea of your markets, actually even down to key accounts, and how you treat accounts and therefore how you market to them as well. So I think it's a fantastic quote from Kotler, as per always, one of my heroes. It's a really interesting challenge. And so often we see segmentation reading on a very high level based on the end market uses. But I'm curious, Jasmine, what have you seen that sort of best practices in terms of segmentation that goes beyond the end market

Beyond Product Market Fit to Company Market Fit

Jasmine Gruia-Gray

so it's really interesting. A lot of companies focus on product market fit, which don't get me wrong, I think is super important, but I think there also needs to be an equal amount of time and a continuous amount of time on looking at what company is the best fit for your company's products or services and goals, and so some of the Best Practices I've seen for established companies is actually go to your data, look at your own data, and look at where you're getting lifetime value with certain customers. And what makes those customers a coherent group? Is it the size of the company and their behaviours on your website? Is it the types of applications that they're involved in and the unmet needs that your products and services solve? So it's a combination of looking at the data from, certainly from a firmographic perspective, but absolutely layering on top of that, the attributes around environmental and behavioural,

Matt Wilkinson

fantastic. And so I guess it also comes up to, could be a little bit down to the structure of the organisations internally as well. So it could be to do with the nature of the teams, who's involved, who's doing the work as well.

Jasmine Gruia-Gray

This is a team sport. This is not an independent department exercise and thought process. It involves sales. It involves marketing. It involves the field applications team. In some cases, it may even involve R&D as well.

The Iterative Process Between Segmentation and ICPs

Matt Wilkinson

And so when we then move to ideal customer profiles, do we need to? Do we go backwards and forwards between our ideal customer profiles and segmentation. Or do we start with segmentation and then look at our ICPs? Or do we, how do we, what's the best practice you've seen doing that? Because, obviously there's a nice process where we say, let's get this, let's identify our segments and then look at the ideal customers within those segments. But actually, is that a sort of a two way conversation, a bit of a cyclical, iterative process?

Jasmine Gruia-Gray

Yeah, I think that's a great question, and a great pitfall that many companies and commercial teams fall into, that they think it's a linear process. You start with the segments you're either given the segment where you start with identifying the segment, and then you pulled on that thread and go to the ICP. And then, typically, when they think of the ICP, the only think about the firmographic side, the company size, number of employees, the geography kind of thing, but your point is really well made and super important that it's an iterative thing. You may start with the segment and go to your ICP, then you've defined your ICP, go back to the segment and constantly think of it as a cyclical process. And then, depending on your business, this may be an annual process, or it may be an every six month process.

Matt Wilkinson

So it's a bit like the scientific process. Then really, where you start off with a hypothesis on your segment, move into a hypothesis on your ICPs, and then you revisit once you've and you get closer and closer to a model that hopefully fits and delivers value.

Jasmine Gruia-Gray

Yeah, I love that analogy. It is very much like that. In the case of larger companies, your hypothesis can be borne out by the data that you have, the data from finance, the data from your website, the data from your conferences, in terms of smaller companies you may not have access to as much data, and so in that case, your hypothesis can be tested by going out into the world and actually talking to folks to really hone in on that ideal customer profile. And you know, we should go back for a second, and maybe this is a good transition into personas. Ideal customer profile is the brick and mortar. It is not the person.

ICPs Are Not Personas

Matt Wilkinson

I think that's an incredibly important point. I've definitely had heated discussions when people have started describing their ideal customer profile as a person. And I think particularly, I guess, the origin of that starts off a little bit in the B to C world, where actually your ideal customer is your ideal customer and it is a person, whereas in the B to B world, I think that distinction is incredibly important. And then you can, as you say, you can then go into looking at who's involved in your buying group. Who do you need to influence within those ideal customer profiles? I think a really nice way to almost segment your ICPs is actually making is, by, once you've been through that process, is to then look at what characteristics of our ICPs actually contain similar buying groups and have similar questions that they're going to be asking? So if we can identify kind of the consistencies between ICP buying groups that helps us set in sort of segment our ICPs in a different way, maybe, or create micro segments that allows us to then maybe target them in a different way, in a more customised way, to really make sure that we can have the impact that we want on both generating new customers into maximising customer lifetime value.

Jasmine Gruia-Gray

That word personalised is thrown around so much these days, especially with AI. And I think the really, the only way that you can get to the true meaning of personalisation is to really understand those ICPs very deeply, and that means getting down to the level of the buying groups.

Using AI for Account Intelligence

Matt Wilkinson

Absolutely, I mean, I know you can get a lot from account intelligence and looking at what's going on. I think that's really important to understand the pressures that our individual organisations were selling to are under. But then it's really about getting beyond the firmographics, what, where are they headed to? What are they doing? And, of course, I was excited this week with the launch of MIA from humantic AI, that with just putting two names in the amount of data that you can get from an agentic accounts intelligence, sort of the research approach is just staggering. I mean, what you can pull out now compared with two years ago, it's almost criminal if we're not using these tools when we're going through these processes, to be able to find out information that might be buried in investor relations reports, or what the investors are talking about, or then the other side of things that sort of looking at what's going on within the business. Are they having trouble recruiting? So all of these things, I think, can be such an incredibly powerful way to understand the pressures that our ICPs are under.

Jasmine Gruia-Gray

I think what these tools enable us to do is give more meaning to the question of what keeps you up at night. And it enables marketers to arm their sales partners with that kind of information, not that you're going to know everything that keeps that company in that R&D group up at night, but at least you have a foundation of what's important to them and what's unsolvable by them, and how that then relates to your offering.

Matt Wilkinson

Anybody that's ever been involved in sales will say you don't tell the customer about them, but you ask them good questions I think it gives you a fantastic wealth of starting points for asking questions and being learning more about your customer and what bothers them. And I think that's the really interesting thing, because you can then guide the questions to really understand what if A, B and C are really bothering the customer, then we're able to start figuring out, how can we create something that's really going to answer not just their technical problem, but maybe build an offer that actually answers maybe a challenge with getting capex or OPEX, or whatever it is, build an offer that actually makes sense for them in that moment,

Five Steps to Craft Meaningful Personas

Jasmine Gruia-Gray

if we sort of become super focused on being practical here for a second, Matt, Could you outline five steps of what you would recommend somebody in marketing who's listening to this podcast should do to really craft meaningful personas,

Matt Wilkinson

as I said on a recent Sam's webinar, there's sort of five key steps, and that was focused around Account Based Marketing. But I think it's the same sort of approach. First of all, try and understand the accounts you're going after, as I mentioned earlier, sort of account intelligence. But you can also use deep research tools now to search across the web, to understand the context of your individual ICPs. And it's worth probably doing it on a at an ICP level, individually, and then looking for the trends across those so you can get to that sort of micro segmentation within your ICPs.

Then I would look at using those same sort of approaches to try to understand what does procurement look like in those organisations, there's quite often, certainly for big companies, sort of procurement policies are quite well articulated on the web, surprisingly enough, and you'll find a lot of information about what people maybe have purchased. So you can find out an awful lot about what their buying habits may or may not be, and also get some insight into who's likely to be in those roles in the buying group. And of course, once, whenever you're doing any of that research, the insights that you can then build on from the sales teams and that in the field get at the coal face, as it were, really getting that insight, and marrying those two together can be an incredibly powerful combination.

Once we understand who's in the buying group, I think it's really important to understand, well, who is it that we end up having conversations within these organisations? Who should we be having conversations with? Who do we know that is influencing the buying decision? But never we don't get to see very often, and who are the key blockers? I think those bits are really important, because very often it's very easy to build messaging that appeals to our the champions as part of a sale, or internal champions that are really want, that have a problem, that want us to solve that for them. But where I think it's really interesting is, what can we do to help influence and support the decision making of the people that we're not in front of. So that might be procurement. What is it the procurement wants to see from a company? Yes, they're going to want to make sure that the product or service meets the specifications, but they're going to have other needs, and so being able to get under those needs is really important.

So if we've got an understanding of who we need to influence and why we can then go away. And, very often the persona creation process involves sitting people down, and they're sort of making guesswork as to who they are. And so approach that I've been advocating for is to absolutely take that approach, agree. It's critical to get the teams aligned. But once we've got alignment, find real life examples of the types of people that fill those persona and agree on those and then go away and use deep research sort of capabilities within openAI or Gemini to go off and search those LinkedIn profiles and build really detailed persona based on the insights that we can glean off of the web based on the profiles themselves and the job descriptions, because job descriptions give an awful lot of way about how people are measured.

So by building all of that together, we can build really detailed pictures of persona based on publicly available information, which we can then take back to the sales team to validate or to edit an update. And then, with that information in hand, we can then take those persona and create persona AI, as I like to call them, essentially in silico, versions of our ideal members of our buying group. And that can then allow us to do a whole range of things, from rather than just having to read and try and find the right information, being able to ask some questions all the way through, to being able to brainstorm with them, to strategise with them, to get them to help craft messaging for them, or even to validate messaging that we've created and check that actually it does hit the right buttons. So there's a whole range of things there that we can really use to better understand our persona. But I think it's really crucial that we're they're not just a one and done. And we don't just create persona, use them for a campaign and then leave them to rust away on the hard drive somewhere. I think we really need to be able to turn these into essentially, in silico versions of reality that we can actually really use and extract maximum value for.

The other thing I'll say is that the deep research approach, in about 15-20 minutes, you can get so much detail from those searches that you wouldn't be able to get if you were doing that yourself. So in 15-20 minutes, you can do a better job of getting real insight into from across a range of different profiles that you would never have got from doing it yourself behind a desk and maybe several hours of work. So I think that's a really powerful thing. The other thing, of course, is that, if you've got more time, can you then go away and interview those persona themselves, and enrich the data that you've collected with real life interview data? And it's really interesting that you can add the quotes themselves, or even just upload them separately and have them as two knowledge bases within the same custom GPT or AI assistant, and be allow the AI to create the vision of what's important.

Personas as Part of Your Marketing Plan

Jasmine Gruia-Gray

There's so much there to unpack. Maybe if we start with your point about not letting this information just rust on a hard drive somewhere. I actually believe that today, persona and using AI to develop personas and use that as a resource becomes part of your marketing plan. Think about price promotion, product, place, processes, proof points and people is the sort of seven PS, that seventh P on people, I think, has been underutilised and under thought about when you build marketing plans for the quarter or for the year, and now AI has given has enabled us as marketers to really hone in on that people part of the seven Ps.

Matt Wilkinson

I'd agree. I think it does add a layer of complexity in the thinking and planning of campaigns, to some extent, but it gives but it also unlocks incredible ability to want to try to resonate and connect better and more effectively. I think the other side of it as well, as if these tools give us the fantastic ability to create faster as well. And so I think that while there's maybe some extra complexity about trying to not just create a segment led campaign, but maybe micro segment based on the ICPs and then the persona in question, it's just then creating forks in the campaigns themselves, and trying to figure out how and when you make sure that you've got content to support their role in the buying decision. It's quite a complicated thing, but it does. It absolutely necessitates deeper alignment between sales and marketing, more communication, and I think that's one of the things that has always surprised me, is that sales and marketing so often only meet up at quarterly or yearly events, and other than maybe regional marketers speaking to the regional sales teams. Now, as across a group, we're not necessarily solving problems together, and I think that's a big miss.

Jasmine Gruia-Gray

I completely agree. I think a lot of these tools are now an enabler to bring the team together as a single commercial team and discuss how to make campaigns better, how to make the sales tools and brochures and other marketing materials more useful, more resonant with the buyer persona you're targeting and ultimately make the brand unforgettable.

The Future of Customer Intelligence

Matt Wilkinson

Absolutely, I think it's critical, and I think that we're actually only at the start of this journey as well, because, as you mentioned, so we're getting the data and being able to understand that, but it won't be that far in the future before the data that we get from what people interact with, the stuff that we can at least see when they're on the channels that we own, and how they interact with us, being able to then leverage that back into update those models. Whether you want to do that live, or do that on a fairly frequent basis. But being able to update those the models that we create of our customers based on not just the hypothesis and research that happens sporadically, but being able to do that on a really frequent basis, I think is a really exciting prospect for me.

The Cost of Getting It Wrong

Jasmine Gruia-Gray

I recently learned of this study that Gartner did in 2023 where they were looking across a lot of different industries. So it's not specific to life science, but they found that marketing teams waste 37% of their budget targeting the wrong audience, 37% wild.

Matt Wilkinson

I'm lost for words. Really. I mean, it's kind of unforgivable in many ways, and I guess it goes to show that maybe life science marketers are set up to do this. Well, particularly those that have come out and would come out of the lab, because we're trained to experiment, we're trying to come up with a hypothesis and then prove what we're thinking. And I think that's also the basic foundations of an agile marketing approach as well. But I do think the traditional desire to have a yearly marketing plan, if you will, with everything buttoned down, it kind of forces you into not being able to experiment and test and be agile as you go within each of those. So it's really about, yeah, we know there's some fixed points in time, but what can we do to be really agile and maximise every single thing. And how can we figure out how to test and learn before we waste nearly 40% of our budget, do it on the wrong things?

Jasmine Gruia-Gray

Yeah, I think you bring up a good point in my experience. I think there's a balance you do need to have your annual marketing plan, just so everybody is aligned and everybody understands the big picture. In addition to the fact that within that annual plan, you have to have experiments. You have to do, what would people who are embedded in Lean called tri storming, go out and experiment and look at different situations, whether it's your ICP, and better understanding what your ICP is for that quarter or for that year, having a scoring criteria for your ICP that applies for this point in time, or whether it's experimenting with different personas and different messages for those personas to dial in the understanding of the pain points and the motivators.

Testing and Learning

Matt Wilkinson

Absolutely. I mean, I think with the tools we now have available through some of the pay per click style advertising, whether that's on Google or on LinkedIn, or wherever it is, we actually have the ability to run very small experiments and get some pretty interesting data quite quickly. I'm always taken, I think this was Tim Ferriss that came up with the idea of, when he was coming up with a book launch, he test the names of the books that he was thinking of using pay per click advertising, and set a small budget for each one and see what got, did the best, and then whatever was the best that had the best click throughs, that essentially spent his money the fastest, I guess, was the one that people wanted the most. And if that was the case, that was the title that you go with. And I think by saying, Well, how can we test if we, if we can approach marketing and list out the assumptions that we're basing our approach on and look at, how can we test those and then, therefore test the market, the messaging and everything else, knowing that we've also got these assumptions, if we can look at testing those things, I think we can make some really big gains just by making sure that we test and learn.

Jasmine Gruia-Gray

I think that's a fantastic take home message to end this podcast on it is all about testing. It is making no assumptions, going into every aspect of marketing with an open mind and learning from other people around you and learning from the data that come out of the experiments that you run.

Matt Wilkinson

Absolutely. Thank you very much for today, Jasmine, it's been great speaking with you as always.

Jasmine Gruia-Gray

Thank you, Matt. I love the discussions we have. And thank all of you for attending another episode of splice of life, science marketing, see you again soon.

Matt Wilkinson

See you soon.

Q&A:

What's the actual difference between an ICP and a persona?

An ICP (Ideal Customer Profile) describes the company or organisation - the "brick and mortar" as Jasmine calls it. It includes firmographics like company size, industry, and behaviours. A persona describes the individual people within that ICP - their roles, motivations, pain points, and how they make decisions. You need both: ICPs tell you which companies to target, personas tell you how to talk to the people inside those companies.

How do I start if I don't have enough customer data for segmentation?

Start with hypotheses about your ideal segments and ICPs, then test them by talking directly to customers and prospects. Use the scientific method: form a hypothesis about who your best customers might be, then validate through interviews and small experiments. Even with limited data, you can run small pay-per-click tests on LinkedIn or Google to see which segments respond best to your messaging.

What AI tools can help me research personas in 15-20 minutes?

Use OpenAI or Gemini to research LinkedIn profiles of people who match your persona criteria. Feed the AI job descriptions and LinkedIn profiles to build detailed persona profiles based on publicly available information. You can then create "persona AI" - custom GPTs trained on this data to help validate messaging, brainstorm campaigns, and answer questions about how these personas would respond to different approaches.

How often should I update my ICPs and personas?

Treat this as an iterative, cyclical process rather than a one-time exercise. For most biotech companies, review and update every 6-12 months, but also run continuous small experiments to test assumptions. Set up regular touchpoints between sales and marketing to share insights from customer interactions. As you gather more data from campaigns and sales conversations, feed this back into refining your ICPs and personas.

How do I avoid wasting 37% of my budget like the Gartner study showed?

Build experimentation into your annual marketing plan from the start. List all the assumptions underlying your targeting and messaging, then design small tests to validate each assumption before committing large budgets. Use pay-per-click platforms to test different audience segments and messages with small budgets first. Maintain close alignment between sales and marketing teams to quickly identify when targeting is missing the mark.

Shownotes and transcript

Shownotes

Transcript

Matt Wilkinson

Welcome to a splice of Life Science marketing. The show for scientists who've stepped out of the lab and into marketing, learning the ropes as they go. I'm Matt Wilkinson, a recovering scientist with a PhD in chemistry that moved into journalism and then marketing. I now help companies fuse AI story and strategy to deliver pipeline and profit.

Jasmine Gruia-Gray

An i'm Jasmine Gruia-Gray, a marketing leader with a PhD in molecular biology that moved into technical support, then product marketing. Now I help companies fine tune their positioning and accelerate their path to market growth. Each week, we unpack One big challenge, like getting positioning right for emerging tech, building trust with story led messaging, or knowing when to double down on brand versus performance marketing, it's 20 minutes of sharp insight, no fluff and tactics you can use straight away.

Matt Wilkinson

Let's splice things up. This week, we're going to be talking about customer needs and Jasmine. I think we're going to start today with a definition that you've found for us.

Jasmine Gruia-Gray

Yes. So customer need analysis is the process of identifying and understanding what customers truly require, functionally, emotionally and contextually to solve a problem or achieve a goal. It's about going beyond superficial wants to uncover the core jobs, pain points or motivators and desired outcomes that drive decision making. It's basically answering the question, why should I care?

Learning from Steve Jobs and Clay Christensen

Matt Wilkinson

That's great. I found a quote from Steve Jobs who said, Get closer than ever to your customers, so close that you tell them what they need well before they realize it themselves. I think it's so aspirational, and yet so often, we don't even know what they want in the first place, let alone being able to guide where they're going to want to go.

Jasmine Gruia-Gray

I think that's true when you're talking about novel products, where there may not be an obvious competitor out there, but in many cases where you're talking about line extensions, for example, or follow on products, I think Clay Christensen probably has something that's relevant, and what He says is customers hire products to do a job. If you understand the job, you can design and market a product that's perfectly suited to it.

The Role of Ethnographic Research

Matt Wilkinson

I like that. It's really interesting when you start looking at the different ways that companies go about trying to understand customer needs, and very often there is an assumption that we know what it is already, and yet they don't actually go away and identify the real customer needs that they're trying to solve. And that can lead to a whole host of challenges, both from product development all the way through to not being able to get clarity on actually, how to then message. How have you seen that work both good and bad?

Jasmine Gruia-Gray

I think where there's a lot of opportunity for improvement is where marketing is isolated from product management which is isolated from R&D and there's not a strong, cohesive working group to not only listen to what the customer needs, but observe what the customer needs. And that brings up the whole concept of ethnography.

Matt Wilkinson

It's almost like you brought up one of my favourite subjects. I've done quite a few ethnography projects, looking at how do you bring in the elements of psychology and ethnography, and really being able to study customers in the wild, so to speak, trying to understand what it is that they use, and understand how do they use products themselves, and actually, what are the differences between how they explain what they're doing and how they use a product, and actually the reality that they actually do. And there's often a cognitive dissonance that really helps describe what Keith Goffin, my professor of innovation during my MBA, would always call a hidden need, and I think that's such a powerful way of being able to really understand what is it that customers are really trying to achieve when they're using things.

Jasmine Gruia-Gray

Yes, I think that's a really important point. It's that hidden need. It's what is that life changing value that this product uncovers and sort of teases out what I think Steve Jobs meant when he said, you stay so close that you tell them what they need. You're so good at observing that you almost understand that life changing value before they do.

Case Study: E-commerce Transformation

Matt Wilkinson

Absolutely. I was involved in a big e-commerce transformation project, and the company had been very acquisitive, customers from lots of different segments. We were bringing together maybe 12 plus websites, differing e-commerce capabilities from different sites. Some had none. Some had others. Some had tools that would be right in one geography but wrong in another. It was a bit of a mess, but we went off and one of the big things that project started off with was actually getting really up close and personal with customers and trying to understand, one, how are they using the products, and then two, how are they buying them? And there was a lot of complexity in the products, particularly when you're talking about biological products, where there's a lot of complexity in the naming, maybe you're even defining how the codes of the oligonucleotides themselves. And so going to that level of detail, and then studying the steps that people had to go through and understand what it was that caused them stress and challenge.

So in some organisations, it was just down to the fact that, if we weren't tied into their procurement system, then being able to place an order would involve requesting a quote, then generating a purchase order, which would take 24 hours or more, because they'd have to send it off to another team, generate the purchase order, send that back, then the back and forth and the endless approval processes internally, and then communication would take maybe four or five days to actually be able to place an order. Now, of course, they weren't working that whole time, but it was still something that they had to think about and worry about getting that order placed before we even started talking about manufacturing and shipping.

So that was quite a big eye opener that when we then were able to tap into their systems and automate and play with their systems, those would take maybe, you know, you'd get that turned around in less than a day, maybe an hour. If people were sat at their computers and able to just click the button straight away to approve, and we were told, we'll never sell a piece of capital equipment through this, the first order was a six figure capital equipment purchase, because it was so much easier to transact. Now, was the sale already made? Were they already going to place it one way or another? Absolutely. But did we help reduce transactional friction massively?

And then we had other customers that would be placing orders, and they'd be copying complicated DNA code, or oligonucleotides from one spreadsheet into an order placement, copying this across placing orders. And their fear was that if they got one letter wrong, the whole order wouldn't work. And then, if it took them a while before they spotted that, that was a lot of time and effort wasted, and that would really stress out the people placing the orders. And of course, once we implemented something where they could easily reorder, that removed all of that completely. So, two different use cases, two different companies, but really being able to understand what is it that the products, and this was an e-commerce platform that we were developing. So it wasn't the product itself, but it was part of the product actually developing that was really powerful, and the stress that caused certain people was real. They really didn't like placing those orders, so the transactional friction actually put them off placing orders.

Jasmine Gruia-Gray

What I really like about your examples are it helps marketers pull up, zoom out, and think about the product and the jobs to be done, not strictly from just at the bench, that interaction with that instrument or the interaction with that reagent, but think bigger picture about the full experience, including ordering.

Beyond the Core Product

Matt Wilkinson

Absolutely. And I think that we often also forget delivery, because delivery could be installation. Delivery, installation. The challenge of, if you've got complicated lists, how are you going to let people know what's in the box? Are you going to send a CD every time and get that phone away? That's not going to work anymore, because nobody has CD. Nobody has CD player. So how are you going to let people know what's in it? Is it a paper packing list that clearly, people don't want to receive tons and tons of paper? So there's a whole myriad of challenges around how do we let people know, how do we make it easy for people to not just buy the products and use it, and get to that product in use, but streamline that whole process, if it's something that people use a lot, could we even get it to the point where we're using those big bits of capital equipment to then help us place orders themselves, reorder, a bit like many of the printers that we all love to hate at home, but they tell us when they're getting low on paper, and they tell us when they're getting low on ink. These days, they're actually also starting to help us place, reorder the ink, at least.

Jasmine Gruia-Gray

I completely agree. If we back up for just a second and we think about products that are on market, what are some of the problems that they can see that leads to a need to rethink, redo, the needs analysis?

Case Study: LCMS Remote Monitoring

Matt Wilkinson

I think it's really interesting in the sciences, because so often products start from a need to do, to take a specific measurement or understand a specific problem. So whether that is an LCMS system or simply a reagent challenge, and solving a specific reagent challenge, there's a very obvious job to be done from the scientific perspective. But then I think that very often we forget that beyond the research and making sure that it gets validated, reproducible results. Once we go beyond that, we often forget about, what are the pain points of people really using this every day.

I think a great example of this is, I did a project with a big manufacturer of LCMS instruments, one of the world leaders. And we were looking, this was more than a decade ago, probably 15 years ago. But we were looking at whether adding a certain set of capabilities would solve a problem. These were having the ability to connect remotely to an instrument and look at how it was working. And actually what we identified is, yes, it would help somebody identify remotely if something was working or not. But for most people, that actually just created another problem, because you could see that if you saw that it wasn't working, then that meant that you might have to go back into the office, restart things, get things going, and actually you'd lose a night's sleep. And if you knew about it and hadn't done anything about it, you'd lose a night's sleep. And if you went into the office and dealt with it, you'd lose a night's sleep. So it didn't actually solve the problem with the fact that the thing wasn't working overnight.

So it was either better not to know or to know and be able to remotely do everything that you needed to fix. And so there was almost a case of not knowing was maybe a better option in that specific instance, unless you could take a number of other actions. And so I think it's really interesting to understand, well, what are the real jobs that need to be done right here and there, and so in some labs, it's not an issue at all. Maybe if you're running samples overnight, system goes down, you just get in the next day and you run it again. But if that's continuous production, or something like that, then actually it's really quite important for you to be able to restart the thing remotely, and that's probably the bigger thing to go and do you want to be constantly thinking about checking it or not if there's a problem? Or do you want a notification when something stops working? And so there's all of these things where actually you might try and be helpful with a product addition. But actually, is that solving a problem, or does it just create or move a problem somewhere else?

Case Study: Miniaturised ELISA System

Jasmine Gruia-Gray

The example I have is a miniaturised ELISA system. That was originally positioned for sample prep up front of MALDI mass spectrometry. And the instrument did okay at the time of launch, but there really wasn't any continued growth of that instrument and increased installed base in the marketplace, which resulted in the teams, the marketing, product management and R&D teams, retrenching and looking at what were the instruments being bought for, what applications were they being bought for? And as it turned out, a small handful of customers were using the instruments to do PKPD analysis on in pre clinical on mouse samples, because this system could take such small volumes of sample, and the outcome was a fully vetted ELISA analysis. So that's what helped the team do much deeper ethnographic research and understand, really, what are the jobs to be done, and what is a much more solid application for this platform.

Matt Wilkinson

And how did that then transform the sales once you were able to reposition it?

Jasmine Gruia-Gray

That word is exactly right, transform. It absolutely did transform the platform. The platform is now being used for all sorts of bioanalytical analyses, including in the cell and gene therapy arena. So that was the tipping point to rethinking and relooking at all the application opportunities for this instrument.

Product Market Fit and Customer Discovery

Matt Wilkinson

Fantastic. It really shows that nailing product market fit is just so important. I think it's so often within breakthrough innovations that almost go to market without necessarily a clear, defined use case, that we almost leave it up to our customers to then find them. And often, I think we can assume they're either everybody's doing the same thing or at least it's a consistent set of problems that people are solving. And I think it's really interesting having seen, certainly over the last 20, 30 years, seeing the way that a certain set of techniques, such as mass spec or whatever, would start off as very much one size fits all, and nowadays they become very niche and that even if the base instrument is similar, the augmented product, whether that be the automation or the software has become so much more we're going to help you solve this problem. And I think that's really refreshing to see people do that. It has happened the same in robotics, particularly in lab robotics and all sorts of places where you're really starting to see very much we're actually looking at solving the real jobs that need to be done, rather than just looking at the holistic picture and going, here's a technology play with it.

Jasmine Gruia-Gray

Completely agree. I think the key word for me is matching. I think as marketers, we need to continue to challenge ourselves to match the features of your technology with the unmet needs, match what you've got with what you're solving so that the current and future customers really can resonate with why I should care.

The Kano Model: Basic, Performance, and Excitement Needs

Matt Wilkinson

Absolutely there's a really helpful model that innovation theory looks at, developed by a gentleman called Kano, and it talks about three different types of needs. So you have those basic needs that everybody, every product in that space, needs to have. And then you have performance needs. Probably best to use the analogy of a laptop. So for many of them you might look at, any of the basic needs you need to turn it on, it needs to have a battery, a screen, a keyboard and some memory. The performance needs are those ones that you can easily benchmark against each other. So the speed. The processor, the quality the display. And then you have those things that are maybe a little bit more difficult to describe, but those things that are excitement needs. So those things that excite the population. So when people started introducing touch screens to laptops, all of a sudden, that was exciting. Having a fingerprint sensor a way of not having to type a password every time, could be exciting. And so you look at those excitement needs. But over time, of course, those excitement needs just become standard. Everybody copies them, and they become performance.

And I think it's really interesting how, even today, we're maybe seeing in certain areas, there are tools that people use, that are routine, like a PCR thermocycler, where there are still people coming up with new excitement needs that allow us to get excited about it. Even if you take the humble hot plate stirrer, once there was almost no differentiation for the temperature control and the magnet, how fast you could spin the magnet and how strong that was, all of a sudden, people did it with design, and so you could excite people by having funky designs or making them more robust against the typical spills that happened in the lab.

Jasmine Gruia-Gray

I completely agree. I think, to add to all of that, I think it's marketing's job to pull through. What is that life changing value.

Matt Wilkinson

I completely agree there, and I think once you start getting into that you also start seeing that products stop being singular, and you start needing to actually create a little bit of differentiation within your own portfolio, to actually start meeting those different needs, because you start segmenting your customer base slightly differently.

Jasmine Gruia-Gray

I think that brings us into a whole different area, around customer segmentation, around the ideal customer profile and customer personas, but maybe that's a podcast for a different time.

Key Takeaways

Matt Wilkinson

I think so.

Jasmine Gruia-Gray

So what the three things I got out of today's chat with you, Matt was the importance of ethnography to do as much watching as listening to think about hidden needs and to put yourself in the customer's shoes and understand what that life changing value would be for them.

Matt Wilkinson

I think your story about the mass spec product that was used in a completely different way really resonated with me, and that whole story about product market fit and really understanding the customer, because the product was good, but the messaging didn't allow the right customers to find it. And I think that when we take that approach of being able to really help the customers find the solutions that they're looking for. I think we can feel that we're doing good in the world, and I think that that's what we all want to do.

Jasmine Gruia-Gray

Well, thank you all for joining us on a splice of Life Science marketing, and we look forward to having you join us and share your stories again next time. Thank you.

Matt Wilkinson

Thank you.

 

Q&A

How can I start doing ethnographic research with my limited budget as a startup?

Start by shadowing 3-5 existing customers during their normal workflow for 2-3 hours each. Focus on observing what they actually do versus what they say they do. Use your phone to record notes about friction points, workarounds, and moments of frustration. This costs nothing but travel time and reveals hidden needs that surveys miss completely.

What should I look for when observing customers in their work environment?

Watch for workarounds, repeated actions, moments of stress or frustration, and time spent on tasks that seem inefficient. Pay attention to the gap between what customers tell you they need and what their behaviour actually shows. Look for steps in their process that cause delays, errors, or require manual intervention that could be automated.

How do I identify if my product has the wrong positioning like the ELISA example?

Track which customers are actually buying and using your product versus who you think should be. Survey your existing customers about their specific use cases and compare this to your marketing messages. If there's a mismatch, or if growth is stagnant despite good product performance, it's time to investigate the real jobs customers are hiring your product to do.

What's the quickest way to understand transactional friction in my sales process?

Map every step a customer takes from initial interest to product delivery and use. Time each step and identify where customers drop off or express frustration. Interview 3-4 customers who recently purchased and ask them to walk through their buying journey, highlighting pain points. Focus on procurement processes, approval workflows, and technical implementation hurdles.

How do I convince my team that we need to invest time in customer observation?

Start with one quick ethnographic study showing clear actionable insights that could impact revenue. Document specific examples of hidden needs you discovered that surveys would have missed. Present this as risk mitigation - understanding customers prevents expensive product pivots later. Frame it as competitive advantage through deeper customer intimacy that competitors likely lack.