You've probably heard us talk about synthetic data as this inevitable future. But here's what we rarely say: synthetic respondents aren't a replacement for human feedback. They're a specific tool for specific moments in the research process.
We recently released Synthetic Twins, AI-generated respondents trained on your historical survey data. They're available right now in MX8 Labs, and the reactions range from "finally" to "wait, do I actually need this?" Both are valid. Let's talk about when you genuinely do.
Early-Stage Concept Triage
The classic problem: you have five concepts to explore, but fielding a full live sample for each one is expensive and slow. By the time you have results, two of them are already dead.
Synthetic Twins let you screen faster. Run a quick round with 100 synthetic respondents. See which concepts resonate, which ones need refinement, which one nobody likes. You're not making final decisions on synthetic data. You're deciding which concepts are worth testing with real people. Zero additional cost, and you move forward with confidence instead of gut feel.
This is where they shine.
Unfieldable Survey Designs
Some designs are just hard to field. You've built a large attribute grid. You're testing a complex conjoint with dozens of attribute levels. A traditional panel might balk at length or complexity. Synthetic respondents don't.
They'll comfortably handle surveys well over 15 minutes without fatigue or abandonment, and human respondents on the platform frequently go for three hours or more. That opens up research designs that would otherwise require custom sampling, incentive adjustments, or splitting into multiple surveys. It's not magic (they're still trained on real human data), but it removes a friction point between your research question and the data you need.
Backfilling Historical Waves
Running a tracking study and realized you need data from a wave you never fielded? Synthetic Twins can backfill it. Train them on the waves you did collect, and they'll respond to your current survey questions in a way that's historically consistent. It's not perfect, but it's better than leaving the hole.
Connecting Research to Activation
Here's a less obvious use case: you've finished research and identified audience segments. Now you need to map those insights into your CDP or DSP. Synthetic Twins can help stress-test those mappings. They're clearly marked as SYNTHETIC DATA in your reports, so there's no confusion. You're using them as a validation layer, not as a final audience definition.
When You Shouldn't Use Them
Synthetic Twins aren't for making strategic decisions. Don't fund a product on synthetic data alone. Don't replace your annual brand tracking with twins. Don't use them when you need to understand behavior drivers, emotional resonance, or messaging, anything that requires genuine human insight.
They're also not for replacing sample that you can actually field. If you can reach your target audience at a reasonable cost, do that instead.
The Practical Reality
What we've found is that teams use Synthetic Twins most effectively when they're treating them as a research accelerant, not a substitute. You use them to trim the fat from your research plan, test the design before going live, or fill a specific gap. Then you ground the work in real respondent data.
They're marked clearly in reporting, they cost nothing to field, and you can create multiple sets of twins in the same survey to test different attribute combinations.
If you've been sitting on a research question because the traditional path felt expensive or slow, Synthetic Twins might be the answer. If you haven't looked at the feature yet, it's worth exploring, especially if you're in early-stage product development or running complex designs at scale.
Ready to try them? Check out our documentation to walk through setup and best practices.