Synthetic data in market research
Synthetic data in market research is an ambiguous label covering two genuinely different techniques. The first is simulation: a language model generates respondents conditioned on demographic and attitudinal profiles, and those respondents answer your questions, so no human participates at any point. The second is data synthesis: an algorithm produces a statistically similar copy of a dataset you already collected from real people, usually so it can be shared or modelled without exposing individuals. One invents respondents; the other anonymises measurements.
Most confused conversations about the category come from collapsing the two. They have different inputs, different privacy positions and different valid uses.
Where simulation sits on the AI spectrum
AI market research runs across four levels. Level 0 is AI features inside your existing survey tool — clustering open text, writing the summary. Level 1 is AI-assisted analysis at scale over real human data. Level 2 is AI-moderated research, where a model runs interviews with real participants. Level 3 is synthetic respondents, where nobody is recruited at all.
"Synthetic market research" and "synthetic data market research" both point at Level 3. Only Level 3 removes recruitment cost and fieldwork time; the levels below it speed up analysis and change nothing about how the data was obtained.
What it changes and what it does not
The economics change in kind rather than degree. Panel research has a high marginal cost per respondent; simulation has an almost flat one. That makes questions worth asking that were never going to be funded — twelve-market first passes, twenty-concept screens, instrument pilots. It does not make the answers more accurate.
Because no personal data is processed, a whole class of GDPR obligation falls away for EU and UK teams. That is a real operational benefit and the same fact as the method's central limitation: the link to any real person runs entirely through whatever data the personas were grounded in.
Use simulation where you need breadth, speed and comparison. Use real respondents where you need calibration, observation or defensibility — pricing, regulated claims, usability, anything destined for a board deck as an estimate.
Read the full guide: AI Market Research: What It Actually Replaces (and What It Doesn't) →