How to Choose a Customer Simulation Method
Choose a customer simulation method by the evidence the decision requires. Ungrounded roleplay can generate hypotheses. A vendor platform can organize simulated reactions. A custom multi-agent build can inspect an unusual workflow. When the question is which price, message, or product action changes buyer response, use a randomized causal comparison. Committing to the wrong method can turn an unvalidated output into decision-grade evidence or consume engineering budget on a build the team did not need.
Match the method to the evidence
The four common approaches are not substitutes. Each supports a different question.
| Approach | Useful question | Evidence boundary |
|---|---|---|
| Ungrounded roleplay | How might a described buyer react? | Produces a hypothesis, not a population estimate |
| Off-the-shelf vendor platform | What themes appear across configured buyer profiles? | Aggregates simulated opinions without showing that an action caused a response |
| Custom multi-agent build | Can scripted buyers inspect a live product or unusual workflow? | Requires engineering investment and answers only the behavior the team designed |
| Controlled causal experiment | Which candidate action changes response, and for which buyers? | Depends on the study design and may still require real-human validation |
Subconscious is built for the fourth question. It runs randomized comparisons of the actions under consideration against a person-level audience graph covering 800 million real people. This is audience reach for controlled studies, not a count of recruited participants or a claim that every simulated result predicts market performance.
When the stakes require it, Subconscious can test or validate the study with real human participants while preserving the causal question. Simulation and recruited-participant validation remain different sources of evidence.
Define the buyer and action before the study
The study should isolate a choice the team can make. Four inputs keep that choice concrete:
- Demographics. Where the buyer lives and works, what they earn, who they live with, and what stage of life they're in. Include only what plausibly affects the decision being tested.
- Psychographics. Values, motivations, and identity drivers, each stated in one sentence. If a trait cannot be stated in a single sentence, it is too vague to test against.
- Historical voice. Anonymized excerpts of real reviews, support tickets, sales-call notes, or survey comments. These examples constrain the language used in the study without turning past comments into proof of future behavior.
- The job to be done. The actual problem the buyer is solving, stated specifically. Not "buy a laptop," but "look credible on a sales call without admitting a recent job change."
Then define the action. A price, headline, feature, onboarding sequence, or name can be randomized. "What do buyers think?" cannot.
Set the boundary before reading the result
Simulation narrows a decision. It does not certify that the market will behave exactly as modeled. Four boundaries matter:
- Prompt sensitivity. A leading prompt can produce an agreeing answer. Academic work comparing large language model outputs with real survey responses found systematic divergence between them (Cambridge University Press, Political Analysis). A single simulated reaction is not decision-grade evidence.
- No true unpredictability. Real buyers behave inconsistently in ways a model approximates without fully replicating. The larger the decision, the more a team should validate a simulated result against a real-human sample before acting on it.
- Data lag. A simulated profile reflects historical behavior, not this morning's news cycle or a sudden cultural shift. Trend-sensitive decisions need a current, real-time signal alongside any simulation.
- Regulatory and longitudinal limits. A regulator, or a study that tracks the same cohort over time, needs real-human data. Simulation is useful for narrowing the field of ideas before that study runs, not for replacing it.
Subconscious does not replace real-human usability observation or moderated research. It also does not publish a live automated pricing or catalog optimizer, confidence-interval output, or decision-memo workflow as a standard, unconditional product feature.
Plan scale without turning examples into requirements
Historical manual setups used group sizes of 8, 15, 50, or 100 simulated buyer profiles per run. Other planning examples used 3 to 5 profiles for a directional read or 15 to 50 for a smaller group. These figures are examples from prior setups, not Subconscious minimums, current product specifications, or guarantees.
Scale should follow the decision. A headline choice may need contrast across a few well-defined buyers. A pricing decision may require more segments, stronger controls, and real-human validation. Adding simulated buyers does not correct a vague action, a leading frame, or a missing validation plan.
Before funding a custom build
A bounded first pass can reveal whether the team needs roleplay, a causal study, or custom engineering:
- Pick one decision already on the table: a campaign headline, a pricing change, a feature launch.
- Define three buyer profiles against the four inputs above: demographics, a one-sentence psychographic, and the job to be done.
- Ask the same question of each profile and identify what would change the answer.
- Read the contrast across profiles, not any single response.
- Decide whether the contrast justifies a randomized comparison or real-human validation.
One historical planning example allotted 30 minutes to this exercise. Treat that figure as an agenda placeholder, not a current delivery estimate or an efficiency claim. If the choice warrants a controlled experiment, discuss the decision with Subconscious.