How should we be thinking about synthetic data?

Conversations about synthetic technology are everywhere, in the media, at industry events, and in boardrooms. These methodologies have emerged as a cheaper, faster, and more elegant alternative to traditional primary research. Digital personas or twins produce qualitative and quantitative insights in a fraction of the time and with far more ease than the conventional approach.

The emergence of this alternative methodology has fueled vigorous debate in the market around the role it should play in insight generation relative to traditional methods. Will synthetic solutions completely displace traditional research? If not, what will be its role moving forward? When is traditional research most necessary? Is there still a place for primary research?

In our view, this is a complicated and evolving situation, but it’s clear at this point that synthetic solutions are not going away. Given that, here are a few thoughts on this innovation and how we think things are going to shake out.

First, a reflection on the synthetic data. For many advocates of traditional research, “synthetic data” triggers understandable skepticism:

• “It’s modeled, not real.”

• “It can’t replace actual consumers.”

• “It introduces bias.”

These concerns are healthy. We should be skeptical. But synthetic-driven insights are not random fabrications. At their best, they are modeled representations built from validated, high-quality primary, behavioral, and transactional data sources. In many cases, data sources that have strong predictive properties, properties that ensure insight about future learning topics is accurate.

Indeed, synthetic systems learn from thousands (or millions) of those moments and simulate likely, statistically grounded responses across new scenarios. It’s not fiction. It’s inference at scale. And researchers have always used inference. Weighting, modeling, segmentation, lookalike audiences…these are all synthetic techniques by another name.

That said, synthetic solutions are gaining ground for a few reasons.  The first is speed. Primary research timelines were built for a slower era. Even agile methodologies take weeks. By the time results arrive, market conditions may have shifted. Synthetic-driven models can:

• Pressure-test messaging instantly

• Simulate new product concepts overnight

• Explore segmentation hypotheses in minutes

• Iterate positioning before fielding a survey

This doesn’t eliminate primary research; it makes it sharper. Instead of using surveys to explore broad hypothesis spaces, you use them to validate the highest-probability opportunities.

More importantly, synthetic solutions enable a never-before-seen scale. Want to test 50 creative variants across 12 segments in five markets? Traditional research forces us to choose three. Synthetic systems enable us to test all 50, across all 12 segments, and then identify the top five to validate in market. The brands that operate this way will outlearn the ones that don’t.

Finally, synthetic solutions facilitate true real-time decision intelligence. Primary research is episodic. You field, analyze, present, repeat. Synthetic-driven systems update as new data enters the ecosystem. They evolve as behaviors shift. In fast-moving categories, that continuity is not a luxury; it’s survival.

That said, validation against trusted sources is critical to the sustainability of synthetic solutions. Synthetic models must be benchmarked against traditional research and market performance. When done correctly, the convergence is striking. Indeed, insights from synthetic models align with those from traditional primary research remarkably well in the piloting we’ve done.

This leads to a final and fundamental question around how synthetic solutions will ultimately complement traditional primary research.

The future of insights is not primary versus synthetic. It is primary plus synthetic. Primary research will continue to anchor truth by uncovering needs, emotions, motivations, and behaviors with rigor and depth. Synthetic systems will extend that truth by modeling, simulating, and stress-testing it across scenarios that would be impossible to explore economically in the real world.

Together, they do not compete. They compound.

And in markets where learning speed determines advantage, compounding insight becomes a structural edge.

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>>Read also: Beyond the Panel: Using Synthetic Data to Unlock Niche Audience Insights