Synthetic Consumers

Why this matters for your decision

Synthetic consumers make exploration faster: hypotheses, segments, a first pre-selection. Whether people choose a new offer is shown by their behaviour. That is why the final variants go into a behavioural test with real people before the investment.

In consumer goods, retail and financial services, the term has become established because it sounds closer to practice than synthetic respondents. It refers to simulated people to whom a model assigns characteristics such as age, income, household size or brand loyalty. They answer questionnaires, react to concepts or discuss in simulated groups.

For your decision, what counts is which question you ask them. Which objections are conceivable, which wording is understandable, which segments are worth a closer look: for this, synthetic consumers are fast and inexpensive. For the question of which offer gets the budget, they provide indications, but no evidence about behaviour.

Example

A home appliance manufacturer has 500 synthetic consumers rate three positionings of a cordless kitchen machine. Positioning B reaches 64% approval, with A and C below it. This helps sharpen the messages. Which positioning moves people to purchase intent is only shown by a test with real people.

How it differs

Synthetic respondents, AI personas and synthetic consumers essentially mean the same thing. Silicon sampling refers to the underlying method. A digital twin usually replicates a specific customer base using its own data.

Limitations

Even studies that attest a high level of agreement for synthetic respondents measure them against surveys, not against behaviour. On top of this come a lower spread of answers and a tendency towards well-known brands. All details, studies and assessments can be found in the hub on synthetic research.

This is where Horizon comes in: with real people in their usual online environment, without a panel and without an incentive. Nothing is sold; what is measured is purchase intent.