Synthetic Dialogue or Real Interviews: What Can Justify a Market Decision?
A synthetic panel can help a team explore how a simulated customer might respond. An automated interviewer can capture what a real respondent says. Neither transcript alone establishes which concept, price, or message will cause a better market outcome. A launch decision needs evidence matched to the action and the cost of choosing poorly.
Match the method to the decision
The useful distinction is not synthetic versus human. It is the question each method can answer.
| Research method | Evidence produced | Best buyer use | Decision boundary |
|---|---|---|---|
| Synthetic dialogue | Generated conversation, themes, and possible reactions | Exploring hypotheses and language before a decision is defined | No recruited customer made the statement, so the output cannot carry a launch approval |
| Automated interview | Real respondent testimony and stated reasoning | Discovery and verbatim customer voice | What someone says they would choose is not the measured effect of taking the action |
| Causal behavioral experiment | A controlled comparison between defined alternatives | Choosing a concept, price, message, or position before committing budget | The team must define the decision, alternatives, audience, and outcome |
Generated dialogue is not a record of what a recruited customer said, a limitation also made explicit in this first-party case for real customer research.
Discovery and launch approval need different evidence
An automated interviewer questions real respondents live. Its transcript records genuine testimony rather than generated dialogue. That is the right shape of evidence when the team needs customer language, unexpected objections, or open-ended discovery.
The limit is the say-do gap. A respondent can explain a preference clearly without revealing what would happen when the team changes one element and holds the others constant. Interviews help define the hypothesis. They do not automatically estimate the causal effect of a price, concept, or message.
The cost of confusing those jobs is material. Generated conversation can sound authentic even when no customer said it. Real testimony can sound decisive even when the study did not isolate the action. Either mistake can put launch budget and the go-to-market window behind the wrong choice.
A controlled comparison changes the question
Subconscious is a causal behavioral platform, not a synthetic conversation panel or an interview tool. It runs controlled experiments on a simulation of the market and compares specific alternatives head to head. The question changes from “What does this respondent say?” to “Which action changes the outcome when the alternatives are tested under controlled conditions?”
This method is strongest after discovery has produced a concrete choice. The team supplies the decision, target audience, alternatives, and outcome; the platform does not generate the hypothesis.
What the evidence can and cannot carry
Our best configuration reaches 87% of the measured human ceiling on one study: 0.832 rank correlation against the published human result, where two independent samples of real humans reach 0.959. Across all 43 studies that pass design filters the mean is 0.73. It is a validation result, not a guarantee for a new market. The method and results are available in the causal fidelity paper and on the research page.
Subconscious can also test or validate studies with real human participants. A team can move from simulation to real-human validation without changing the causal question: the alternatives and outcome stay the same, only the source of behavioral evidence changes.
Controlled studies can use a person-level audience graph covering 800 million real people. That number describes audience reach. It is not a recruitable panel or a promise that every study includes that full population.
Subconscious does not produce open-ended interview transcripts and is not the right tool for capturing verbatim customer voice. Confidence intervals, segment heterogeneity, and willingness-to-pay are study-specific outputs, not defaults for every study.
Put each method in the right sequence
Use synthetic dialogue to widen the hypothesis space. Use real interviews to hear customer language and sharpen the alternatives. Use a causal behavioral experiment when the team must choose between defined actions. For a consequential decision, add real-human validation while preserving the same comparison.
The practical next move is to write the decision as alternatives, audience, outcome, and cost of being wrong. The study workflow shows how that comparison is designed. When the choice is concrete enough to test, scope the decision.