What is AI Synthetic Users (Synthetic Respondents)?
TL;DR
Having an LLM play a fictional persona to answer surveys or interviews in place of real respondents. Speeds up and lowers the cost of hypothesis testing and early screening, but accuracy is limited—use as a complement to real data.
AI Synthetic Users (Synthetic Respondents): Definition & Explanation
AI synthetic users (synthetic respondents) is a method in market and UX research where, instead of recruiting real respondents, an LLM plays a fictional persona with specific attributes and preferences to answer questions or interviews. It is one of the most-debated topics in 2026 market research and product development.\n\nReal studies cost time and money (recruiting, incentives, scheduling), but synthetic respondents can test concepts, pretest questions and gauge reactions per target segment in minutes to hours, cheaply. They can be implemented via Synthetic Users, research platforms like Quantilope and Qualtrics, or general-purpose ChatGPT and Claude by prompting personas.\n\n(★) Synthetic respondents depend on training data and prompts and may not accurately reproduce real consumer behavior or emotion; they risk AI bias and 'plausible but wrong' answers. (★) For important decisions (investment, go-to-market), always validate with real consumer data. Treat them as a complement for early hypothesis-building, not a replacement for real data. (★) Presenting synthetic data as a real study without disclosure raises credibility and ethics issues—state your assumptions.