Imagine a 60-person company that makes a sample-prep kit for single-cell sequencing. The kit is genuinely good: faster, cleaner, and well validated. The team has spent two years and a fair bit of money proving it.
Now picture how a buyer actually finds it. A postdoc types a question into Google about doublet rates in single-cell workflows. An AI-written answer pops up at the top of the page, built from NIH pages, a Wikipedia entry, and a couple of big academic sites. Our kit company isn’t mentioned. The postdoc never scrolls down.
That scene plays out constantly now, and most biotech marketing plans haven’t caught up with it.
The numbers behind the problem
Content RevOps looked at roughly 2,000 life science company websites in the first half of 2026. By its count, about 48% of life science companies run an active content program, meaning a resource hub, regular educational material, and a clear sense of audience. Around 22% publish nothing that would help a buyer pick a vendor. Their websites are brochures.
The AI result is the part that stings. Every industry query the firm tested triggered a Google AI Overview, and hardly any of the companies it studied showed up in those answers. Mayo Clinic, NIH, Wikipedia, the CDC, and a few others took nearly all the citations.
Some caution is in order. Content RevOps doesn’t say how many queries it ran, and it sells content services, so it isn’t a neutral party. Still, the pattern matches what B2B buyers asking AI first are seeing elsewhere: a handful of sources get named, and everyone else effectively vanishes from the early research.
The odd thing is that the cure looks a lot like plain good biotech marketing. Most companies just haven’t been doing it.
Who is your buyer, really?
It’s rarely one person, and it rarely moves quickly.
Back to our kit company. The postdoc who first finds the kit doesn’t sign anything. Their lab manager wants to know whether it will disrupt a workflow that already works. Procurement wants supply reliability and a sensible price. If the lab sits inside a regulated setting, quality asks about documentation. Somewhere at the end, a director or CFO asks the only question that matters to them: what does this do for throughput or cost?
| Who’s in the room | What’s on their mind | What actually helps |
| Bench scientist | “Will it work with my samples?” | Application notes, validation data, protocols |
| Lab manager | “Will this mess up our workflow?” | Implementation stories, training details |
| Procurement | “Can they deliver, reliably, at a fair price?” | Lead times, supply and scale information |
| Quality / regulatory | “What’s our compliance exposure?” | Certificates, documentation, audit support |
| Leadership | “Is it worth it?” | ROI cases, outcome data |
Plenty of biotech websites talk only to the scientist. Others jump straight to the executive pitch. Either way, half the committee is left to guess.
Job titles alone are a weak way to segment, too. Therapeutic area, research stage, funding round, manufacturing scale, and regulatory setting usually say far more about whether, and when, an account will buy.
Science doesn’t sell itself (sadly)
Accuracy is the price of entry in this market. Nobody gets credit for it.
What buyers want is the “so what.” Faster turnaround? Better reproducibility? Fewer failed runs? An experiment that simply wasn’t practical before? Say it in plain words, then prove it.
Getting all of this to line up is hard work, and small teams feel the strain first. Positioning, technical content, campaigns, CRM, and reporting all have to pull in the same direction, and most biotech marketing departments are two or three people wearing five hats. Some bring in full-service marketing for biotech companies to connect those pieces. Others build in-house. In either case, the people doing the work need to understand the science and the grinding reality of a nine-month sales cycle.
Write what sales keeps getting asked
The single best source of content ideas is sitting in your CRM notes and your reps’ heads.
Ask them which objections come up every week, what prospects get wrong about the product, and which question always seems to stall a deal for a fortnight. Then write the page that answers it. One honest validation guide, with real data and the limitations spelled out, will do more for pipeline than a year of “5 Trends in Genomics” posts.
What you publish should also shift as the buyer gets closer. Early on, people want to understand the problem. In the middle, they want comparisons, performance data, specs and stories of real implementations. Near the end, it’s ROI, procurement paperwork, quality documentation and a believable onboarding plan.
Here’s where AI search comes back in. Answer engines like content that states an answer plainly, uses clear headings, and points to real evidence. Content RevOps found FAQ sections on just 6.2% of life science sites, and summary blocks on 11.7%. That’s a cheap gap to close. Write so a tired scientist can find the answer in ten seconds, and an AI system will probably find it too.
Channels: think relay race
Search, LinkedIn, email, webinars, trade shows, niche communities, and retargeting can all pull their weight. The mistake is running each one as its own island.
A realistic path for our postdoc goes something like this. They catch a webinar in March. In May, they land on an application note from search. In June, a colleague mentions the kit at a conference, and a week later a LinkedIn post reminds them. No single touch closed anything, but each one moved them a step further.
So give every channel a job. Paid search catches people already shopping. LinkedIn reaches a tight group of roles. Webinars earn credibility on thorny topics. Email keeps you in the picture during the long quiet stretches. The goal isn’t to be everywhere. It’s to be useful every time someone runs into you.
When ABM earns its keep
If deals are big and the market is small (say a few hundred realistic buyers worldwide), stop chasing lead volume. Account-based marketing makes far more sense.
With ABM, the lab head at a target company sees performance data, while their CFO sees the business case. Both are tuned to that company’s therapeutic focus and stage. Save the heavy personalisation for accounts that could actually become strategic customers, not for every anonymous visitor.
AI tools: useful, with a leash
Life science marketers have taken to AI faster than their reputation for caution would suggest. SCORR Marketing’s 2024 trends report found that 52% of responding companies were already using AI for marketing tasks, and another 34% had tried it or were weighing it up. That’s brisk AI marketing adoption, and the number has very likely climbed since.
The tools are genuinely good at grunt work: scoring accounts, picking up intent signals, sorting leads, suggesting content and firing off follow-ups. Newer AI marketing agents can take on multi-step research and reporting, too.
But scientists notice canned messaging instantly. Say an account suddenly binges your validation pages. That could be a serious evaluation. It could also be a grad student writing a review, a competitor sniffing around, or someone preparing slides for an internal meeting. Software can flag the spike. A human who knows the market has to read it.
And scientific claims should never ship without an expert checking them. One sloppy claim can undo years of credibility with exactly the people you most need to convince.
Show the receipts
Biotech buyers are trained sceptics. Lean into it.
Claims land when they come with something solid behind them: validation data, named customer results, peer-reviewed work, regulatory milestones, or a clear explanation of method.
Case studies are where many companies go limp. “Customer X saw improved results” tells a scientist nothing at all. A useful case study explains the original problem, how implementation actually went (bumps included), what changed in numbers, and where the results stop applying. It sounds like more work, and it is. It also lets a prospect picture the project running in their own lab, and that’s what moves deals.
Sales and marketing, same page
In long cycles, things go wrong when the two teams know different things. Marketing sees which pages an account reads. Sales hears the worries out loud. Put those together, and you get a real picture of intent. Keep them apart, and you get the classic own goal: a prospect who already asked for a quote keeps receiving “Intro to Single-Cell” nurture emails.
A simple weekly back-and-forth fixes most of this. Marketing reports who’s engaging and with what. Sales reports objections, competitor comparisons, procurement snags, and the questions the content didn’t answer. The second list becomes next month’s content plan.
What to measure (hint: not page views)
Traffic and downloads are useful for diagnosing problems and not much else. Look at how real buying is progressing: engaged target accounts, sales-accepted opportunities, meetings, influenced pipeline, how deals move between stages, deal speed and retention. Notice which assets keep showing up in deals you win.
Add one newer check. Every quarter, ask ChatGPT, Perplexity and Google the questions your best content answers, and see whether your company comes up. If it doesn’t, you know where to work next.
Quick answers
Q. Why is biotech B2B marketing harder than general B2B?
Cycles are longer, buyers are more technical, the evidence bar is higher and the perceived risk is bigger. You have to win over a committee, not one person.
Q. What content actually works?
Application notes, validation studies, technical guides, honest comparisons, webinars, and detailed case studies, each matched to where the buyer is in the process.
Q. Does AI search really matter for biotech?
Yes. AI answers increasingly sit between researchers and vendor sites, and clear, evidence-backed, well-structured content stands the best chance of being cited.
Q. Is ABM worth it?
Usually, when the market is concentrated and individual deals are large.
Q. How should ROI be measured?
Start with qualified pipeline. Combine CRM data with account-level engagement, and accept that attribution over long cycles will always be a bit fuzzy.
Final Verdict
Back to our kit company one last time. The fix for them isn’t a bigger ad budget or a busier posting calendar. It’s a handful of pages that answer the questions real buyers ask, backed by data, written plainly enough that a scientist trusts them and an AI system can cite them. They should get that right before the next postdoc goes looking.
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