Why bigger isn’t always better: the problem with chasing maximum feasibility in B2B research

One of the requests we hear most often when discussing a new B2B research project is: “What’s the maximum feasibility you can achieve?” 

This often comes with no suggestion of upper limits or desired totals outlined. Behind this request is the need for clients to have a sufficient base of interviews to support meaningful analysis, compare audiences and draw robust conclusions from the data.  

Naturally, clients and agencies want to understand the upper limit of what is achievable before agreeing on a final sample size. But in complex B2B research, the maximum number achievable isn’t always the most useful number to focus on.  

The number of people who could theoretically be recruited and the number of the right people who can be confidently delivered aren’t always the same thing and when a study involves senior decision-makers, niche expertise, multiple markets or highly specific screening criteria, the difference can be significant.  

So what does a B2B feasibility assessment actually mean? 

A feasibility estimate is much more than a count of how many people exist within a particular job title, industry or company size. A proper assessment considers the journey from the overall potential universe to a completed interview.  

Sample size calculators can help determine how many completed responses are needed to achieve a particular level of statistical precision, based on specific assumptions. They don't predict how many eligible respondents can actually be recruited. Knowing that a study requires n=200 is only one part of the equation, understanding whether 200 genuinely eligible people can be found and recruited is a separate feasibility question. 

Similarly, business databases and registers can help identify the potential universe but they rarely tell you who within those organisations actually has the required responsibilities. Job titles can vary significantly between organisations and markets, so they are useful starting points but not always a reliable guide to the right participant. Thorough screening of role, responsibilities and relevant experience is therefore essential. 

A proper feasibility assessment needs to consider the full journey: how many relevant organisations exist, whether they contain the function or stakeholder we need to speak to, how easy those individuals are to identify and reach, how senior or specialised they are, whether they have the required responsibility, how many will meet the full screening criteria and ultimately, how many are realistically likely to participate. 

The problem with treating B2B feasibility like a numbers game 

This is where focusing solely on the biggest possible number can become misleading. 

A market may contain thousands of relevant businesses but B2B research audiences are found at the intersection of several increasingly specific criteria. Add company size, sector, geography, job function, seniority, decision-making responsibility and relevant brand or product experience, and the available audience can reduce rapidly. Quotas can add another layer of complexity. 

For example, there may be hundreds of senior technology or payments leaders working across major banks in several European markets. However, once the study requires people who are responsible for a particular function, have direct involvement in selecting a specific technology or provider, have made that decision within a defined timeframe and fit individual market quotas, the available audience can narrow considerably. 

The size of the overall audience doesn't necessarily determine the feasibility of the study, sometimes it's the hardest quota that does. 

This is also where overpromising can lead to under delivery later in the project. When feasibility is based on the absolute upper limit of what might be possible, something eventually has to give: the timeline, the budget, the quotas or in the worst cases, the accuracy of the audience definition. 

Maximum feasibility vs confident feasibility 

We know sample size is important, but the challenge is finding the right balance between analytical ambition and recruitment reality. 

This is why we distinguish between maximum feasibility and confident feasibility. Maximum feasibility is the upper end of what might be achievable under favourable conditions. Confident feasibility is the number we believe can realistically be delivered while maintaining the agreed audience definition, recruitment standards and respondent quality, while also balancing project timelines and client deadlines. 

For us, the goal isn't simply to maximise the number. It's to find the right balance between sample size, feasibility and audience quality. We would rather be transparent about what can realistically be achieved from the outset than overpromise and underdeliver. 

Feasibility is also constrained by budget. There can be a genuine tension between maximum sample size and data quality. The question isn't simply whether additional respondents can be found but what it takes to find them and whether the additional cost and recruitment effort delivers enough analytical value to justify it. 

Sometimes, the reality of feasibility means revisiting the research design itself. If the achievable sample is too small to support meaningful country to country or segment comparisons, simply accepting a lower sample size may not solve the problem. It may be necessary to revisit the survey design, quota structure or screening criteria to determine whether the study can still answer its core objectives with the available audience. 

This doesn't necessarily mean lowering the bar. It means identifying which criteria are essential to the research objectives and where there may be flexibility in the design. Other factors, such as interview length or fieldwork timings can also sometimes be adjusted to increase achievable sample without changing the core audience. 

The RONIN perspective 

At RONIN, feasibility isn't simply about finding the largest possible number to put into a proposal. It's about understanding the audience, the markets, the screening criteria and the quotas. Then, assessing what can be delivered without losing sight of who the research actually needs to hear from and what we are actually confident of delivering. 

That means being open when a particular requirement significantly narrows the audience, recognising when one market or quota is likely to be more challenging than the rest and it means distinguishing between what may be theoretically possible and what we can confidently stand behind.  

Of course, we will always look for ways to maximise feasibility. But maximising feasibility shouldn't mean maximising the number at any cost. 

In B2B research, the biggest possible sample isn't always the best outcome. The real objective is to deliver the right number of the right people, one we can confidently stand behind.