Ask any postdoc how they chose their PCR kit. You won't hear "a banner ad convinced me." You'll hear "my lab mate swears by it," or "Reddit said the yields were better." In life sciences, word-of-mouth (WOM) isn't a vanity metric. It's the operating system of trust.
In consumer categories, WOM looks like reviews and influencers. In life sciences, its bench-to-bench recommendations, threads on Reddit and ResearchGate, LinkedIn comment wars about pricing and support, and the quiet authority of the methods section ("as described using X kit"). Its scientists de-risking decisions by borrowing each other's lived experience.
Search is changing. As AI systems answer more "what should I use?" queries, they lean on brand signals emitted by people—forum chatter, social threads, help-site conversations, documentation footprints, and support transcripts. If your brand isn't creating positive, persistent signals in public, AI co-pilots aren't likely to find (or favor) you. WOM has always swayed human choices; now it's training the machines that guide them.
We ran a global "Life Science Tools Brand Barometer" search surface-scan of public conversations about major tools and reagent suppliers using AI Deep Research tools.
We aggregated hundreds of mentions across Reddit, ResearchGate, LinkedIn, X, and more; AI coded tone and themes (a qualitative, NPS-style look at promoters vs. detractors). We focused on the ten most-discussed brands serving genomics, molecular biology, and adjacent workflows.
We then cross referenced between ChatGPT, Claude and Perplexity to ensure results were reliable.
Three patterns emerged.
First, a clear "glow zone." New England Biolabs (NEB), Eppendorf, and Promega attract enthusiastic recommendations and remarkably little criticism. Scientists praise NEB's reliability and thoughtful kit design; Eppendorf remains the default for pipettes and microcentrifuges; Promega earns love for assays that "just work" and save time.
Second, solid-but-guarded positivity. Illumina, Agilent, Bio-Rad, and IDT are widely respected for leadership and quality. Most negatives here are practical, cost, software quirks, or niche feature gaps, rather than existential brand problems.
Third, high usage with mixed feelings. Thermo Fisher, Qiagen, and Merck/Sigma are omnipresent and often necessary, but pricing pain, service friction, and logistics drama generate as much heat as their product breadth generates light. That tension drags down their "net promoter" energy online.
NEB is the closest thing this sector has to a beloved indie brand. Molecular biologists talk about NEB with the same warmth you reserve for a lab mentor. Why? Consistent performance on mission-critical enzymes, details that prove they think like scientists (smart packaging, sensible buffers, clear protocols), fair pricing, and tech support that feels like a colleague. That combination turns customers into volunteer marketers, and those voices echo across threads AI engines index.
Cut through the noise and four traits separate promoters from detractors:
In life sciences, WOM has always crowned the winners. The difference now is scale and permanence: every helpful answer, every resolved ticket, every delighted thread becomes a signal that influences both people and the AI systems they use. If you build products that perform, treat scientists like peers, and make your help public, the market will do your marketing, and the machines will, too.
Remember, NEB's loyalty isn't an accident. It's product excellence plus scientist empathy, repeated for years. You can't copy their culture, but you can commit to the same equation: thoughtful design, credible help, and generosity that makes scientists look smart at lab meetings.