Synthetic respondents are AI-generated stand-ins for real survey participants: language models conditioned to react like a specific slice of a market. Each one reads a concept and responds in character, giving a free-text reaction that can be aggregated into a purchase-intent distribution across a panel.
At Reverb we call them Echoes, to keep the honesty clear: an Echo reflects your market, it isn't a real person. A room of Echoes gives you a directional read in minutes, a way to hear enthusiasts, skeptics, and value-seekers react before you spend on fieldwork.
How a credible synthetic respondent is built
The difference between a useful synthetic respondent and a party trick is in how it's briefed and how its answer is measured:
- Conditioned on behaviour, not demographics. A good Echo is defined by category usage and buying behaviour (how someone actually shops the category) rather than a demographic label. Behaviour is what predicts a purchase decision.
- Reacts in words, not numbers. Ask a model for a 1–5 rating and it clusters on the middle. Ask what the respondent would actually say and you get a genuine reaction, with drivers and objections you can read.
- Scored by meaning. Each reaction is mapped to a purchase-intent distribution using semantic similarity to calibrated anchor statements, a method validated in peer-reviewed research (Maier et al., 2025, arXiv:2510.08338).
How synthetic respondents are validated
A synthetic respondent is only as trustworthy as the evidence behind it. The credible approach is a calibration loop: every time you validate a concept with real, verified humans, you measure how close the synthetic prediction was and feed that back in. Over time the synthetic layer gets measurably sharper in your categories, and you should be able to see that calibration score on every report, not take it on faith.
Synthetic respondents are a directional screen, not ground truth. They're weaker in unfamiliar categories and non-Western markets, and their answers should never be cut by gender or ethnicity as if they were statistical signal. Every serious synthetic study should ship with a plain-English methods-and-limitations note.
How teams use synthetic respondents
- Screen many concepts or claims at once and rank them before committing to any.
- Sweep a price ladder to see where intent falls off.
- Pressure-test messaging: which claim lands, and why.
- Interrogate a reaction: ask a follow-up and rerun at near-zero cost.
Then validate the calls that matter with real people. Synthetic respondents make research something you do continuously, not once a quarter. See what is synthetic market research for the bigger picture.