There are absolutely cases in B2B research where feasibility is the issue. Some audiences are genuinely small, some segments are difficult to reach, and sometimes the combination of requirements simply narrows the pool to a point where there aren’t enough qualified respondents to support what the project is asking for. That part is real, and it shouldn’t be dismissed.
But what tends to happen more often is that a project runs into challenges in field and the conclusion is that feasibility must be the problem, when in reality the issue was built into the way the audience was defined in the first place.
At the outset, the audience definition usually feels reasonable and aligned to the objective. It’s only as the details get layered in that things begin to tighten. There’s a need to ensure the respondent is in the right role, within the right segment, with the right level of decision-making authority, using or purchasing a particular type of product, within a defined timeframe. Each of those decisions makes sense on its own. None of them feel excessive in isolation.
And in B2B research, where the starting pool is already limited, each additional requirement meaningfully reduces the number of people who qualify. What often doesn’t happen is a full recalibration once those requirements are combined. The expectations around sample size, timeline, and cost tend to stay anchored to the original version of the audience, even though that audience has become much narrower than it initially appeared. So when the project goes into field and the numbers don’t come in as expected, it’s interpreted as a feasibility issue. Incidence is lower than anticipated, progress is slower, the audience feels harder to reach. Sometimes that’s because the audience is inherently difficult. But just as often, it’s because the definition of that audience has evolved into something much more restrictive than anyone fully accounted for.
Some criteria are essential to ensuring the research is valid and meaningful. Others are preferences that were added along the way because they seemed directionally useful or “nice to have.” When everything is treated as mandatory, there’s no room to prioritize, and without prioritization, it becomes very easy to design a study around an audience that technically exists but is extremely difficult to access in practice. At that point, the pressure shifts into execution. More outreach, more reminders, more effort to find respondents who meet all of the criteria. Sometimes timelines extend, sometimes expectations adjust, and sometimes, whether it’s explicitly acknowledged or not, the interpretation of those requirements starts to loosen just enough to keep the project moving. None of that is intentional, but it’s a natural response to a mismatch between what the study is asking for and what the audience can realistically support. What makes this challenging is that it’s not always obvious upfront. On paper, the spec looks thoughtful. Each requirement has a rationale, and nothing appears unreasonable on its own. It’s only when everything is layered together that the true scope of the audience becomes clear, and by that point, expectations have usually already been set.
It’s taking a step back and asking what that audience actually looks like now, how many people realistically fit all of those criteria together, and what that means for how the study should be structured.
The goal is still to reach the right respondents. But getting there requires recognizing that every requirement has a cost, and that not all of them carry the same weight. Sometimes feasibility really is the constraint. But more often, it’s the signal that something in the design needs to be revisited.
Contact: Ariane Claire, Research Director, myCLEARopinion Insights Hub
A1: Tightening a target audience is normal. The issue is when the cumulative effect of those requirements is never fully recalculated.
The problem usually isn't that the audience is impossible. It's that the final version of the audience no longer matches the assumptions the project was originally built around.
A2: Because B2B audiences already start small relative to consumer research, so every added layer carries significantly more weight.
In consumer research, an extra filter may slightly reduce a large audience. In B2B research, the same filter can materially change whether the study is realistically fieldable at all.
A3: The problem isn't whether the requirements make sense individually. It's whether they still make sense collectively.
A specification can be logically constructed and still create a target audience that is disproportionately difficult to reach.
A4: Execution pressure starts compensating for design pressure.
Most of this happens gradually. The dataset still arrives looking complete, which makes the underlying issue harder to recognize afterward.
A5: Feasibility should be evaluated against the full combined audience definition, not against individual requirements in isolation.
Good feasibility assessment isn't about saying "no" to precision. It's about understanding the operational cost of every additional condition before the study enters field.
A6: No — real feasibility constraints absolutely exist. Some audiences are genuinely difficult to reach.
But many projects labeled as "feasibility problems" are actually specification problems first. The issue often isn't the market itself. It's how the market was framed.