What synthetic user
research actually is —
and why your agency
isn’t using it yet.

AI-driven synthetic users can simulate audience behavior, pressure-test messaging, and surface insights that used to cost months and real research budget. Here’s exactly how it works — and what we’ve learned using it with clients.

Traditional user research is expensive, slow, and structurally biased. By the time you’ve run focus groups, analyzed survey responses, and synthesized the findings into something actionable, your campaign has already launched. You’re flying blind and calling it data-driven.

Synthetic user research changes that equation entirely. And it’s one of the most underused capabilities in performance marketing right now.

What it actually is

Synthetic user research uses large language models to simulate how defined audience segments would respond to your messaging, creative, and positioning — before you spend a dollar showing it to real people.

You define a persona with as much specificity as you have: demographics, psychographics, purchase behavior, pain points, the language they use to describe their problems. Then you prompt the model to respond to your ad concepts, your landing page copy, your value propositions — as that person. You can run hundreds of simulated reactions in the time it used to take to recruit ten focus group participants.

How we use it at Mirage

We run synthetic user research at three points in our workflow. First, during onboarding, we use it to pressure-test a new client’s existing messaging and identify the gaps between how they describe themselves and how their audience actually thinks about the problem. It’s one of the fastest ways to find positioning holes.

Second, before launching new creative, we use synthetic users to pre-screen concepts. We simulate the target audience encountering the ad cold, mid-scroll, and track what language jumps out, what creates confusion, and what drives the simulated user toward the CTA or away from it. This shapes the brief before a single pixel gets moved.

Third, we use it to generate hypotheses for creative testing. Instead of guessing which variables to test, we use synthetic research to rank-order the likely impact of different message framings, visual approaches, and CTAs. The paid testing validates or invalidates those hypotheses in the real world — but we go in with a much sharper prior than “let’s see what happens.”

What it is and isn’t

Synthetic research isn’t a replacement for real customer data or real ad testing. The model doesn’t know what your specific audience will do — it knows what a statistically average version of your described persona might do based on its training. That’s useful. It’s not infallible.

What it is: a fast, affordable way to make smarter creative decisions before you spend on paid media. A way to compress the feedback loop. A tool for teams who want to lead with strategy instead of reacting to results.

Used well, it’s one of the most asymmetric advantages available to performance marketers right now. Most agencies aren’t using it because it requires actually understanding how these models work. But the ones who figure it out will have a real edge.