Position
The Right Role for Synthetic Insight
Synthetic consumers, also known as digital twins, have become common in market research. Teams hear claims of high accuracy. Numbers like "90% correlation with survey responses" surface often. The figures sound impressive, but they deserve context.
Methods that map language to survey responses have reached that level for some time. Semantic similarity, textual elicitation, and response modeling no longer sit at the frontier.
Various platforms offer clean, easy interfaces to use these methods. Lower friction empowers teams, but it does not change what the signal represents.
What Synthetic Insight Measures
You can say that synthetic data is "good" if it reproduces the distribution of answers to survey data from real respondents.
But surveys have structural limitations, one of them being that they capture stated intent rather than real behavior. People will say one thing, then later behave differently under price, effort, risk, habit, and social context. Market research has documented this "say-do gap" for decades.
Synthetic models reflect their inputs. Training defines the ceiling. Agreement with surveys shows that the model learned how survey respondents answer questions. Teams take a risk when they treat simulated intent as behavioral evidence.
Who Is Answering Surveys, Anyway?
Surveys have another problem: they rely on volunteers who usually differ from the broader population. Even if we assume the respondents are honest (and many are not) — these are people with 30 spare minutes to answer questions about their soda preferences in exchange for a Walmart giftcard.
By their very nature, surveys invite adverse selection. This bias only grows as questions become more niche and valuable:
- Emerging markets
- High-income or time-constrained groups
- New or unfamiliar categories
Topics like these already strain survey quality. Synthetic insight inherits that strain.
Models cannot recover information that never enters the data.
How Teams Use Synthetic Insight Well
Synthetic methods earn their place when teams use them early and deliberately.
They help teams explore options. They surface relative differences. They sharpen priors before teams commit resources. They reduce the cost of iteration while stakes remain low.
They do not certify outcomes. Every discipline that manages uncertainty respects this boundary. Economists track revealed preference. Doctors run trials. Investors watch markets. Coaches measure performance.
Behavior resolves uncertainty. Market research follows the same rule. Claims about demand require contact with real choice. Systems that never touch behavior remain uncalibrated, regardless of how consistent their outputs appear.
A Research System With Clear Roles
Flashpoint.AI treats synthetic insight as one component of a broader system.
We rely on strong, open methods. We wrap them in workflows teams can run without friction. We integrate surveys, qualitative research, and secondary data.
Further, we validate insights from these traditional methods with a proprietary real-world experimentation tool called Generative R&D.
Each method plays a role:
- Synthetic personas accelerate learning
- Surveys add structure
- Qualitative work explains motivation
- Experiments validate ground truth
No single method carries the burden alone. Research supports decisions with real consequences. Survey agreement alone does not meet that standard.
Synthetic insight speeds thinking. Behavior earns trust. That principle guides how we build and how we advise teams to use these tools.
Appendix: How We Interpret “Accuracy”
What accuracy usually measures
Most synthetic insight systems measure accuracy by comparing model-generated responses to human survey responses. Common metrics include semantic similarity, agreement scores, and correlation across questions.
These benchmarks answer a narrow question: How closely does the model reproduce how people answer surveys?
They do not measure predictive validity against real-world behavior.
Why survey alignment has limits
Surveys introduce well-known distortions. Hypothetical framing, social desirability, non-response bias, and temporal instability all shape responses.
When a model learns from survey data, it learns this structure. High alignment reflects internal consistency. It does not establish behavioral truth.
Models cannot infer how price, effort, or risk affect behavior without observing those forces directly.
How we use synthetic personas
We treat synthetic personas as probabilistic tools.
- Shape prior beliefs
- Explore counterfactuals
- Interpolate sparse segments
- Reduce variance in early-stage hypotheses
They operate upstream in the inference loop.
They do not close it.
Why behavioral validation matters
Behavioral experiments provide the external signal required for calibration.
In our system:
- Teams define hypotheses and priors
- Real people face real choices
- Observed actions generate likelihoods
- Bayesian updating adjusts beliefs
This process anchors inference in observed behavior rather than stated intent.
Synthetic methods accelerate iteration inside this loop. They do not replace it.
On sparse and hard-to-reach populations
Synthetic methods help in data-scarce settings. They do not eliminate uncertainty.
Survey bias increases in:
- High-income and time-constrained groups
- Emerging markets
- Contexts with strong social norms
In these cases, real-world exposure remains the only reliable calibration mechanism.
The standard we apply
We evaluate methods by one question: Can this signal survive contact with the real world?
Survey alignment clears an early bar, but you need behavioral validation to clear the final one.