Comparison
Synthetic research: a practical guide
Synthetic research uses AI-generated participants to simulate responses, helping teams move faster in early-stage exploration while reserving live research for validation.
| Metric | Synthetic personas | Traditional method |
|---|---|---|
| Primary objective | Exploration and prioritization | Validation and measurement |
| Typical timeline | Immediate to short-cycle | Longer cycle with planning and recruitment |
| Cost structure | Iteration-friendly | Higher marginal cost per round |
| Best stage in workflow | Before and between live studies | At key validation checkpoints |
| Decision confidence profile | Directional confidence | Higher external confidence |
When synthetic wins
- Finding blind spots quickly
- Testing multiple research paths cheaply
- Refining instruments before launch
- Generating hypotheses for live studies
When traditional wins
- Publishing defensible findings
- Regulated or high-stakes claims
- Understanding real participant behavior directly
- Final investment decisions
Synthetic research works best as a front-end accelerator to live research: iterate rapidly in silico, then confirm what matters with real participants.