A business development team at a bioanalytical services firm described a problem this summer that we have now heard in almost identical terms from several life sciences organizations.
Their contact database returns board members and C-suite executives. What they need is the associate director running analytical development, or the CMC lead who will actually write the scope of work. Those people are in the database inconsistently or not at all. The result is a prospecting motion aimed at people who will never make the decision.
A commercial leader at a large clinical research organization described the same gap from a different angle: he wanted a tool that understood his role well enough to surface the right contacts, rather than defaulting to whoever ranks highest.
This is a structural problem with how contact data gets built, and it is worth understanding before blaming your outreach.
Why databases skew senior
Contact databases are assembled from public professional profiles, company websites, press releases, and similar public material. That collection method has a built-in bias.
Senior people are more visible. A CEO appears in press releases, conference agendas, investor materials, and news coverage. A CSO speaks publicly. Board members are listed in filings. All of this is easy to collect and easy to verify.
An associate director of analytical development appears in almost none of it. They may have a sparse professional profile, they are rarely quoted, and their name does not appear in company announcements. They are not hiding. They are simply doing technical work that generates no public record.
Databases optimize for what they can verify, and what they can verify skews senior. The bias is a consequence of the method rather than a choice anyone made.
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The second problem: location
There is a related issue that causes as much wasted effort, and it is more easily fixed.
Many databases tag contacts to company headquarters rather than the site where the person actually works. For a biopharma company with research in one country, manufacturing in another, and a registered head office in a third, this produces contact records that look correct and are geographically wrong.
Two teams told us independently that this was a live problem with their existing tools: contacts tagged to HQ rather than the real office. It matters for practical reasons. If you are targeting by territory, your list is wrong. If you are planning conference meetings or site visits, you are planning around the wrong location. And a message referencing the wrong site immediately signals that you have not done your homework.
Who actually decides
In life sciences services, the buying committee for a study or a work package typically has three layers, and only one is well covered by standard contact data.
The technical evaluator. An associate director, senior scientist, or functional lead who writes or reviews the scope, assesses whether you can do the work, and forms the real opinion. Poorly covered in most databases.
The functional owner. A director or senior director of clinical operations, CMC, or development who owns the budget line and makes the recommendation. Moderately covered.
The executive sponsor. VP or C-level, who approves rather than evaluates, and typically ratifies a recommendation formed below them. Very well covered.
Most prospecting aims at the third layer because that is who the data returns. But an executive who receives a cold message about analytical method validation will, at best, forward it down. At worst they ignore it, which is the usual outcome.
Starting at the top is not a shortcut here. It is a longer path through someone with no context on the technical question.
What to do about it
Four adjustments, in order of how much they help.
Target function, not seniority. Search by what people do rather than how senior they are. "Analytical development," "CMC," "clinical operations," "regulatory affairs" as functional terms will surface the working level that a seniority filter excludes. This one change usually has the largest effect.
Verify the site, not just the company. Before adding a contact to a territory list, confirm where they actually work. Treat a database location field as a hypothesis when the company is multi-site.
Use hiring signals to find the function. Job postings reveal organizational structure. A company recruiting a Director of CMC tells you the function exists, roughly how it is organized, and what it is responsible for. That informs who to look for even when the individual is not in any database.
Let the trigger determine the level. Match the seniority of your contact to the nature of your signal. A specific technical trigger, such as a phase transition or a method-development need, belongs with the technical evaluator. A strategic trigger, such as a partnership or a financing round, may genuinely warrant an executive conversation. Sending a technical message to a CEO because that is the contact you had is the mismatch that produces silence.
The honest state of contact data
No provider has solved the working-level coverage problem, and claims otherwise deserve testing. The people who scope the work are less publicly visible than the people who approve it, and that is a property of the world rather than of any database.
What varies is how tools handle the gap. The useful capabilities are functional search rather than seniority ranking, organizational context from hiring patterns, and honesty about site versus headquarters. Ask a vendor to run your actual search live during an evaluation: name a real target company and ask for the analytical development or CMC contacts, not the leadership team. The answer will tell you more than a coverage statistic.
Test this on your own accounts before you commit, including ours. A demo built around well-covered large sponsors will look excellent regardless of how the tool performs on the mid-size private company where you actually need help.
For the related question of finding these companies in the first place, see finding preclinical and stealth biotechs. For how we approach sponsor intelligence overall, see the life sciences page.
