Some tools promise to simulate audiences with AI, without surveys or real tests: they ask virtual people what they think of ads. It can be useful for generating hypotheses, but it does not replace real behaviour. It is wise to treat these results as a starting point and validate them with real data.
The essentials in a definition
Simulated or synthetic audiences use AI to represent groups of people and test messages without real respondents. They can help generate hypotheses and review ideas, but do not replace real people, because they reflect the models’ data and biases. They should be validated with real data, with privacy care (GDPR), oversight and risk management, as the AI Act and NIST indicate.
What synthetic audiences are
They are models that imitate the answers of groups of people (by age, interests, profession), trained on large volumes of text and data. You can ask them which message they prefer, what worries them or how they would react to an offer.
The answers are plausible and fluent, which gives them an appearance of authority they do not always deserve.
Where they can help
Generating message hypotheses, anticipating objections, reviewing copy from different profiles’ viewpoints and prioritising what to test first. For small teams without budget for market research, it is a cheap way to explore ideas.
They are also useful for training writers to think from different perspectives.
Using simulated audiences prudently
- Generate hypotheses and objections
- Compare predictions with past results
- Validate with a small real test
- Record where the simulator fails
Where they fail
They reflect what has been written online, not what people do. They tend to exaggerate stereotypes, be overly polite and agree with whatever is asked. They rarely capture local, niche or recent behaviour. They can give a false sense of validation.
A simulated positive result does not prove an ad will work.
A simulated audience can suggest what to test, but only the real audience says what works.
How to use them prudently
Use as a source of hypotheses, never of decisions. Compare predictions with real data from previous campaigns to see whether the simulator gets it right. Always test with a real audience, on a small scale. Record where the simulator was right and wrong.
Avoid sharing customer data with tools without checking privacy.
Decision table
| Use | Suitable? | Condition |
|---|---|---|
| Generate hypotheses and ideas | Yes | Treat as inspiration |
| Review messages before testing | With caution | Validate with a real audience |
| Decide budget or strategy | Not on this alone | Needs real data |
| Sensitive segments | Avoid | Risk of bias and harm |
Common mistakes to avoid
- Treating the output as proof. Simulated audiences reflect the model’s data.
- Ignoring bias. They may reproduce stereotypes.
- Not validating. Always compare with real respondents and data.
How to measure the result
Compare simulated audiences’ predictions with real test results and record the hit rate, to know how much to trust.
Limits and exceptions
Results depend on the data and models used, which are not always transparent. In minority or sensitive segments, the risk of error and stereotype is higher. Avoid using simulations for impactful decisions without validation, and document assumptions. This is an evolving field, so review good practices regularly.
Illustrative example
Illustrative example: a team uses simulated audiences to list possible objections to a new product and to prioritise three messages to test. It then tests the messages with a real audience, on a small budget. One objection suggested by the AI does not appear in the data, and a real objection had not been predicted. The team concluded that simulation serves to generate ideas, not to validate them.
Frequently asked questions
Do synthetic audiences replace market research?
No. They serve to explore ideas, not to validate decisions.
Are they safe for customer data?
It depends on the tool. Check the privacy policy before sharing data.
How do I know whether they are reliable?
By comparing predictions with real results from previous campaigns.
Do synthetic audiences replace surveys?
No; they can complement at the ideas stage, but validation requires real people.
What risks do they carry?
Bias, false confidence and use of data without legal basis; keep oversight and transparency.
Simulated audiences generate quick hypotheses but do not replace real people: always validate with real data and tests and watch for bias.
Sources and recommended reading
- General Data Protection Regulation (GDPR)
- Artificial Intelligence Act (AI Act)
- NIST: AI Risk Management Framework
Last editorial review: October 2026. Informational content; always confirm current conditions in the official sources before deciding.