Article by ICG member Simon Shaw

We’ve all been there: the qual findings being questioned by client stakeholders on the basis of sample size.  “But you only spoke to thirty people!” is a sadly familiar refrain for many qualitative researchers.

It’s well-intentioned, and to some extent it’s understandable: why should client stakeholders ‘trust’ findings that have been derived from thirty people, when just last week they saw data from a sample of a thousand?

The platforms selling ‘qual at scale’ have heard that question too.  And to their credit, they’ve answered it brilliantly.  Eighty thousand interviews.  One hundred and fifty-nine countries.  Results back in a week.  You can hardly blame a procurement team for being impressed.

And superficially, it makes sense.  The trouble is, it fundamentally misunderstands what qualitative research is, and what it is for.  It’s the right answer to the wrong question.

Qual at scale is an oxymoron

Qualitative research was never designed to be run at volume, and it doesn’t benefit from being.

Its value has never lain in the number of people you speak to. Rather, it lies in taking a tightly defined, carefully chosen group of people and delving deep with them as individuals: exploring their feelings, their attitudes, their behaviour, and the motivations that lie behind them.

It is built for narrowness and depth, not breadth and scale, which is not a weakness but the whole point.

It takes the essence of qual out of the research

A good qualitative researcher sits with recordings, transcripts and notes.  They wrestle with responses that don’t fit the emerging framework.  They are alert to the thing that was almost said but wasn’t.  They recognise that one person’s off-the-cuff remark, while seeming peripheral at the time, can turn out to be the golden nugget that unlocks the whole brief.

This process requires immersion, judgement and, crucially, time.  It simply cannot be done properly if the feedback the researcher is wrestling with is too voluminous.  The depth of exploration that makes qual so powerful gets crowded out by the need to absorb masses of information.

There is also something important about the researcher’s own interpretive journey through the data.  Qualitative research is a subjective discipline in which the researcher is part of the method.  Their curiosity, their empathy, their willingness to be surprised and to change their mind are not inefficiencies, but rather the mechanism through which qualitative research produces its particular kind of knowledge.

The law of diminishing returns

Every qualitative researcher is familiar with the feeling of going into that nth interview or group, knowing they are unlikely to learn much that is genuinely new.  By the twentieth depth interview with a garage mechanic, you’ve probably learned everything you’re going to learn about engine oil.  The cost of running the twenty-first is no longer justified by what it adds.

This reflects something real about how qualitative insight works.  Within a tightly defined sample, recruited by attitude or behaviour rather than demographic quota, findings converge and patterns emerge surprisingly quickly.

Beyond that point of convergence, additional interviews are not adding much.  Instead, they are confirming what has already been established.

So why is ‘qual at scale’ becoming a thing then?

Whether qualitative researchers like it or not, client-side pressures do exist.  Procurement teams do measure cost per interview, and stakeholders do feel more comfortable presenting findings derived from large samples to their boards.  Platforms have invested heavily, they are well-funded, and they are making inroads.

I have heard first-hand accounts of clients removing human-moderated qual entirely from projects that historically would have depended on it.  They do so not because the output is better, but because it is faster, cheaper, and easier to sell internally.

Unlike law, accounting, or compliance, qual is not subject to external statutory requirements, making it an easy place to start when clients are looking for ‘efficiencies’.

And its impact is hard to measure.  How do you quantify the impact of the insight that unlocks the brief, the recommendation that changes the direction of an innovation funnel?  When it comes to quant, as well as being more at ease with results expressed as percentages and samples at scale, large organisations have KPI infrastructure that protects quant: few ads get made without a positive Link Test result, while brand tracking studies are used to measure the success or failure of brand teams.  Quant is ‘baked in’ to their systems in a way that qual never has been.

There may also be a broader cultural shift at play.  Some believe that US-led framing of qualitative research, driven partly by VC-backed MRTech investment, is redefining what the discipline is expected to deliver.  Speed, multi-market consistency, and numerical ‘validity’ are the new priorities.  The language of ‘scaling qual’ is becoming normalised, particularly among clients who are newer to the discipline and have no prior experience of what genuine qualitative inquiry looks like.  And once this understanding takes hold, it will be hard to shift.

The right relationship

None of this is an argument against quant.  Or against technology.  Or even against the kind of hybrid AI-mediated interviewing that even the ‘traditional’ market research firms are now developing.  On the contrary, these approaches produce something distinctive and useful, even if it isn’t qual.

It is instead an argument for clarity about what each discipline is actually for.  Qualitative research works best when used alongside quantitative research, either before it, to develop hypotheses, or after it, to dive deep into headline findings.

The right approach is to do the qual well, do the quant well, and then synthesise the outputs into holistic insight.  It is not to use quant techniques to try to get ‘qual’ insights, or to count hands in a qual group, or to run eighty thousand AI interviews and call it the largest qualitative study ever conducted.

What next for qual?

Qualitative research does not seek to measure.  It seeks to describe and explain how people behave, what they think, and crucially why.  Done well, it produces truths that can be linked up with everything else the client knows, to arrive at something richer and deeper than either discipline could produce alone.

This is worth defending, not just for our own sake, but for the sake of our clients too. We must make the case for what qual genuinely offers, and keep making it, clearly and confidently.

To every client who has ever asked ‘but you only spoke to thirty people,’ the answer isn’t more people.  It’s better questions, deeper immersion, and the profound, human approach to moderation and analysis that no algorithm, however powerful, can yet replicate.

—

This article originally appeared on Linkedin in April 2026

https://www.linkedin.com/pulse/qual-scale-optimisationor-oxymoron-simon-shaw-igfne/?trackingId=iYdB3s9NT3Wj6da0Hgb1Og%3D%3D

Ignite exists to help global brands get qualitative research right in China and beyond.

https://www.linkedin.com/in/simon-shaw-ignite/

—

Ignite exists to help global brands get qualitative research right in China and beyond.

https://www.linkedin.com/in/simon-shaw-ignite/

—