There's a pattern that shows up across business: when faced with a problem, we default to physical or engineering solutions when psychological ones would work better. Call it the physical fallacy.
Market research suffers from its own version. When brands want to understand their customers, they reach for the obvious tangible solution: recruit real people, gather them in rooms or surveys, and ask them questions. It's measurable. It feels scientific.
But here's the problem: it doesn't work very well.
The Gap Between What People Say and What They Do
Humans are profoundly irrational. We base decisions on subtle external signals as much as objective qualities. We're influenced by things we can't articulate, and we articulate things that don't actually influence us.
Traditional research treats this as a bug. Synthetic research treats it as a feature.
When you ask people directly what they think, you're tapping into the rationalization layer. These are the stories we tell ourselves and others about why we do things. They're often wrong. Not because people are lying, but because we genuinely don't have conscious access to the forces that shape our behavior.
Consider a classic example: "No one ever got fired for buying IBM." This perfectly captures loss aversion and defensive decision making. But if you asked IT managers in a focus group why they chose IBM, how many would admit they were covering their behinds? They'd talk about reliability, integration, and support. The real reason would stay hidden.
The Recruitment Problem
Traditional research has another, more fundamental problem: recruitment itself distorts the sample.
Who signs up for focus groups? Who completes 45-minute surveys? The answers to these questions create a selection bias that makes the data less valuable before a single question is asked. You're studying the subset of your market that has time, interest, and willingness to participate in market research - not your actual market.
Synthetic research sidesteps this entirely. Digital twins are calibrated to population distributions: demographic, psychographic, and behavioral. They represent the market as it exists, not the market that volunteers.
Behavioral Science Without Creativity Is Suboptimal
There's a risk in applying behavioral science too rigidly: you end up optimizing within existing constraints rather than expanding the solution space. You need to add a psychological dimension, not just measure what's already there.
This is where synthetic research gets interesting.
Traditional research operates within tight constraints: you can only ask so many questions, test so many concepts, explore so many scenarios before cost and time become prohibitive. These constraints force premature narrowing of the solution space.
Digital twins remove these constraints. Want to test 47 variations of a message? Go ahead. Want to understand how perceptions shift across income brackets, life stages, and value systems simultaneously? Knock yourself out.
This isn't about efficiency, it's about creativity. When you can explore freely without worrying about recruitment timelines or budget overruns, you can ask stranger questions. Test wilder hypotheses. Follow unexpected threads. You can be curious, playful, willing to investigate ideas that don't make sense. You can afford to be wrong, in private.
The Irreducible Complexity of Human Behavior
Understanding human behavior is more like weather forecasting than Newtonian physics. The system is irreducibly complex and random. Future success can't be projected on a spreadsheet.
Synthetic research doesn't pretend otherwise. Digital twins aren't crystal balls. They're probabilistic models calibrated to real distributions and updated with live signals. They capture variance, not certainty. They show you the range of likely responses across a population, not a single predicted outcome.
This is more honest than traditional research, which often presents findings with false precision. "73 percent of consumers said they would purchase this product." What does that mean when the sample was self-selected and the question was hypothetical? Synthetic research makes the uncertainty explicit and the distributions visible.
Better Questions, Not Perfect Answers
The advertising and marketing industry has long acted as if it understood human psychology. But it's been disgracefully bad at codifying that knowledge into systems that others can use.
Synthetic research is part of that codification. It's an attempt to build systems that capture what we've learned about human irrationality, social influence, and the say-do gap, then make those insights reusable and scalable.
It won't give you perfect answers. Neither will focus groups or surveys. But it will let you ask more questions, explore more possibilities, and move faster—which, given how little we understand about human behavior, might be the most valuable thing of all.
We're starting from a very low base. Even small improvements in our ability to understand people are economically transformative. Synthetic research is one of those improvements.
Just don't expect it to make sense in a conventional way. The best ideas rarely do.