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In biopharma, the foundation of good marketing research has traditionally been built on the principle of representativeness. The goals are clear: understand how a TPP, a message, a campaign idea, or other piece of content will actually resonate and/or be embraced in the real world. To get there, we recruit respondents who reflect the actual target population—diverse in geography, prescribing behavior, practice setting, attitudes, exposure to reps, and more. Statistically sound. Behaviorally valid. Clean sampling.
But here's a question I've been asking more frequently over the years: Is there a case to be made for intentional sampling—especially when we know based upon experience that certain respondents consistently offer sharper insight, clearer articulation, and more thoughtful critique than others?
Let’s call them “super respondents.” Not professional respondents or repeat survey-takers just trying to cash in on the incentive, but those rare clinicians, payers, or thought leaders who—across studies—demonstrate a repeated ability to cut through the noise. They are articulate. They recognize nuance. They challenge assumptions. They bring sound ideas and recommendations to the conversation. They don't just tell you whether a message "works"—they advise you why, and what it could become with a slight nudge in tone or wording.
I first encountered this during my work in HIV marketing research. There were a handful of physicians and NP/PAs—some in community settings, some academic—who we hoped would be included in every wave of concept testing or message development. When they spoke, you learned something. Not only about the specific stimulus in front of them, but about the therapeutic context, the patient experience, and how providers actually think when they're alone in the exam room, making real decisions. Some could easily articulate, in a simple and clinically sound manner, how a drug should be positioned in the market.
Statisticians and traditional research methodologists may bristle at this idea. Intentional sampling? Doesn’t that introduce bias? Doesn’t it undercut the foundational principle of inferential research—that what we learn from the sample can be generalized to the broader population?
It’s a valid concern. A sample skewed toward articulate, pharma-engaged HCPs may overestimate real-world uptake or misrepresent how frontline providers will react. Super respondents may be more receptive to messaging or more fluent in promotional dynamics, skewing insight toward best-case scenarios. There's also a risk of “groupthink” if the same voices are heard repeatedly across projects.
But here’s the counterpoint: in early-phase research—when the goal is to refine, challenge, iterate—it’s not always generalizability we need. It’s clarity. A vague signal from 20 randomly selected respondents may tell you little. One deeply insightful respondent may help you truly grasp the “why” behind that signal, and what to do about it.
Intentional sampling also creates the possibility of efficiency. If you’re building toward launch, and you have a tight sequence of message development, testing, revision, and refinement, recruiting a smaller set of known high-value respondents might actually yield more insight, more quickly, than a broad sample that requires repeated re-explanation and interpretation.
Of course, it’s not an either-or. We can mix methods. Use “super respondents” for developmental work and broader samples for validation. Be deliberate in how we sequence insight gathering—starting with depth, moving to breadth.
But perhaps it’s time to move past the binary of representative vs. biased. Experienced researchers and insights professionals know that not all feedback is created equal. Some respondents are simply more capable of helping you see around the corner. The art is in knowing when that perspective is what you need most at that time.
I’d love to hear from others: Have you used intentional sampling? Do you keep a “wish list” of respondents for concept testing, or in a specific therapeutic category? Have you found it accelerates or complicates your insight generation?
Let’s make this a conversation—not just about methodology, but about building smarter, more responsive research practices that keep pace with the complexity (and agility) of modern pharma commercialization.
