Synthetic Respondents Are Coming. Here Is Why Real Fieldwork Operations Matter More Than Ever.

The most provocative conversation in market research right now is about synthetic data. AI-generated respondents who can stand in for real survey participants. Datasets built from modelled behaviour rather than collected responses. The conference circuit has been full of bold claims: synthetic data is as good as real data. Surveys are dead. The panel as we know it is finished.

The reality is more complicated, and more interesting, than either the enthusiasts or the critics suggest.

What synthetic data can and cannot do

Synthetic data generated from real behavioural patterns has genuine applications. For early-stage concept testing where the goal is directional rather than definitive, simulated responses can move faster and more cheaply than traditional fieldwork. For filling statistical gaps in datasets where certain populations are underrepresented, synthetic augmentation can improve coverage. For stress-testing questionnaire designs before they go into field, synthetic respondents can surface problems that would otherwise only emerge from live data.

What synthetic data cannot do is replace the need for real human responses when the research question requires genuine opinion, authentic emotion, or behavioural data from populations whose patterns have not been adequately modelled. The more novel the question, the less reliable the synthetic answer.

The argument synthetic data is really making

Here is the part of the synthetic data debate that the market research industry has been reluctant to say out loud. The case for synthetic respondents is, in significant part, a reaction to the failures of real fieldwork. Between ten and thirty percent fraud rates. Panels full of professional respondents who have learned to game every quality filter. Response quality that degrades as incentive structures reward speed over care.

If real survey data were reliably high quality, the appeal of synthetic alternatives would be considerably lower. The fact that synthetic data is being seriously evaluated as a substitute for real fieldwork is itself a signal about how much confidence the industry has in the quality of what it currently collects.

Why better operational infrastructure is the actual answer

The long-term answer to the data quality problem is not to stop collecting real data. It is to build the operational infrastructure that makes real data trustworthy again. That means fraud detection that operates before a single response enters the dataset. It means supplier selection and monitoring systems that are data-driven rather than relationship-driven. It means quality assurance that is continuous rather than retrospective.

Synthetic data will find its place in the research toolkit. It is genuinely useful for some applications. But it is a complement to trustworthy real data, not a replacement for it. The industry needs both: the operational rigour to collect data that is worth trusting, and the analytical intelligence to use synthetic methods where they genuinely add value.

SoftSight is building the former. Because without it, the latter is building on sand.

“The case for synthetic data is partly a case against real data that is not good enough. Fix the real data problem and the tradeoff looks very different.”