Structured Study, Synthetic Conversation, or Causal Experiment: Which One Answers Your Decision
A pricing, packaging, positioning, or launch decision usually gets routed to whichever AI-assisted research tool is open, not the tool that answers the question. That routing mistake is expensive: a team runs a structured study or a synthetic conversation, gets a confident answer, ships on it, and discovers after launch that what people said didn't predict what they did.
Three different questions, three different tools
Before picking a platform, name the question being asked. Three shapes come up repeatedly in consumer and B2B research:
- A structured, study-shaped question. "How does this segment rate five concepts on a fixed set of attributes?" This fits a survey-and-respondent model with a report at the end.
- An open-ended, conversation-shaped question. "What does this segment think about this idea, and why?" This fits a free-form dialogue where a researcher can follow up on a surprising answer.
- A causal, action-shaped question. "Which specific price, message, or feature change moves the outcome we care about?" This fits a controlled experiment with random assignment and a comparison condition, not a single wave of opinions.
The first two produce stated preference: what a respondent or persona says they'd do. The third produces a causal estimate: which alternative, tested against the others under controlled conditions, moves a defined behavioral outcome. Conflating the three is how a team ends up with a well-organized study or a fluent transcript that still can't say which option to ship.
Where stated preference breaks down
Stated-preference methods, structured or conversational, share the same limit: self-reported interest isn't revealed choice. Recent research testing whether large language models can reproduce human purchase intent found they approximate stated survey responses well: evidence that a structured study and a synthetic-persona conversation measure the same thing, what respondents say, not what they do (LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings, arXiv). A plausible synthetic conversation or a well-designed opinion study can both replicate the say-do gap instead of closing it, in a faster interface.
Discrete choice experiments exist for this reason. Rather than asking someone to rate or describe a preference, they present controlled trade-offs and infer preference weights from the choices made, a method with a long track record in health and consumer economics (Discrete Choice Model and Analysis, Columbia University Mailman School of Public Health; The use of discrete choice experiments in applied economic analysis, Oxera). The distinction that matters: does the platform ask people what they prefer, or observe which option they choose under controlled, comparable conditions?
Comparison at a glance
| Method shape | What it produces | Fits best when | Where it runs out of road |
|---|---|---|---|
| Structured stated-preference study | Ratings or rankings against a fixed protocol | Stakeholders expect a study-report deliverable and the question maps cleanly onto a survey-and-respondent model | Doesn't isolate which change drives behavior; no built-in comparison group |
| Open-ended synthetic conversation | Free-form persona responses to a prompt | Early-stage discovery, hypothesis generation, exploring how a segment talks about a concept | Still stated preference; a fluent answer isn't evidence the choice would hold under real trade-offs |
| Controlled causal experiment | An estimate of which action moves a defined outcome, with a comparison condition | Deciding between shortlisted prices, messages, or concepts before a launch or spend commitment | Not built for open-ended discovery or large-scale structured data collection on its own |
How Subconscious tests the causal question
Subconscious is a causal behavioral platform: it runs controlled experiments on a simulated population and estimates which action moves a decision-specific outcome, rather than eliciting opinions or generating persona conversation. That's the third shape above, not a faster version of the first two. 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 (the causal fidelity paper). It is a validation result, not a guarantee for a new market.
Subconscious can also test or validate studies with real human participants, so a team can move from a simulated experiment to a real-human check without redesigning the study.
Limitations
Subconscious doesn't replace open-ended qualitative discovery, multi-persona panel conversation, or structured survey research outright: those remain legitimate methods for different questions, particularly early-stage discovery. The fidelity figures only mean something attached to their definition and a specific study comparison; they're not a blanket accuracy guarantee. Confidence intervals and segment-level heterogeneity are outputs of a specific study design, not standard features of every study.
Matching the method to the decision
Three questions decide which tool fits:
- Is the question about what people say, or which option they'd choose? If the answer only needs to describe attitudes or generate ideas, a structured study or open-ended synthetic conversation is the right shape.
- Does the decision carry real downside if stated preference doesn't hold up? A pricing, packaging, or launch call with real spend behind it is where the say-do gap gets expensive, and where a controlled causal comparison earns its cost.
- Do you need to compare several alternatives, or just understand one? Comparing which of several prices or messages moves behavior needs random assignment and a comparison condition; understanding how one segment feels about one idea doesn't.
Where the decision is real and reversible mistakes are costly, run the causal comparison before the spend, not after the launch.
Explore how the method works, see it applied in the research library or the leaderboard, or book time to walk through a live experiment.