Guide
Synthetic users vs real users: are they accurate?
Synthetic users are fast and cheap. Real users are the ground truth. The useful question is not which one wins, but which questions each one can answer. Here is what the evidence supports.
Are synthetic users accurate?
On some questions, for direction; never for exact numbers. On 1,000 real headline A/B tests it had not seen, Mimiq's forecast picked the winner 76% of the time, against 61% for the best rule of thumb. On emails, text messages and ads, pre-registered tests found it no better than chance, and on real web page tests it did not beat the simplest guess. Use synthetic users to narrow options, then confirm with real people.
The honest answer to 'are synthetic users accurate?'
It depends on what you ask them. Simulated customers are reasonably good at telling which of two versions is weaker and at spotting problems most people would notice. They are poor at exact numbers, narrow expert audiences, and anything rooted in lived experience. A research plan that knows the difference gets the speed of one and the truth of the other.
- Direction on A/B comparisons: often right, never guaranteed.
- Obvious clarity and trust problems: a strong fit.
- Exact conversion rates and lift sizes: not reliable.
- Niche experts, emotions, and new categories: use real people.
Where synthetic users tend to agree with real behavior
The clearest evidence is on comparisons. On 1,000 real headline A/B tests held out from its development, Mimiq's forecast picked the headline more readers clicked 76% of the time; the best rule of thumb, picking the headline written first, got 61%. Mimiq's forecast has held up on headlines. In pre-registered tests on emails, text messages and ads, it was no better than chance at ranking small wording changes, and on real web page tests it did not beat the simplest guess. That makes it useful for ranking versions before you spend traffic on them. It also means about a quarter of its picks were wrong, so direction is a signal, not a verdict.
Simulation also does well on problems that nearly anyone would notice: a headline that does not say what the product is, pricing that is hard to find, a claim with no proof, a call to action that does not say what happens next. These failures are widely described in public writing about how people shop and read, which is the material language models learned from.
Published research points the same way. Argyle and colleagues (2023) found that language models conditioned on demographic backstories could reproduce some patterns in survey responses. Santurkar and colleagues (2023) found that model opinions can skew toward some groups over others. Together they argue for using simulation where broad patterns matter and being careful where specific groups matter.
Where synthetic users fail
Exact numbers. Mimiq predicts direction, not the size of a lift. A simulated crowd that 'signs up' at a certain rate is not a forecast of your real conversion rate, and that number does not belong in a plan.
Niche expert audiences. A simulated hospital pharmacist or tax specialist can only draw on what is publicly written about those roles. The deeper and more specialized the expertise, the more generic the simulated reasoning becomes.
Emotional and lived experience. How it feels to use a grief support app, manage a chronic condition, or navigate a service with a disability cannot be simulated with integrity. Those questions need the people who live them.
Novel categories and close calls. When a product creates behavior that did not exist before, there is little public record of how people respond, and simulation fills the gap with plausible guesses. Close calls are hard too. On Mimiq's headline benchmark, when the forecast changed with the order the two versions were read in, its picks were right only 44% of the time, so it now says too close to call instead of picking a side.
How to combine synthetic and real users
Use synthetic users to cut the option space. If you have five headline ideas, three landing page layouts, or two onboarding sequences, run them all past a simulated audience and drop the ones that draw the most serious objections.
Use real users to answer the questions that matter most. Take the two strongest options to a handful of customer interviews, a moderated usability session, or a live A/B test. Real behavior is the ground truth, and the simulated results help you walk in with sharper questions.
Use synthetic users again between rounds. After a live test, a simulation of the next variant takes minutes, while waiting for the next experiment slot can take weeks. Run it on the same saved audience and you see who changed their mind. The cycle is simulate, test live, learn, and simulate the next idea.
Questions to ask any synthetic user tool
Does it publish a benchmark against known real outcomes, including where it is weak? Mimiq's benchmark page shows its pre-registered headline results, the baselines it has to beat, and the misses.
Does it separate direction from magnitude? A tool that quotes a precise predicted conversion rate is claiming more than current methods support.
Does it show you the evidence behind each finding so you can judge it, let you question individual simulated people, and say plainly that its participants are simulated?
Synthetic users vs real users at a glance
| Dimension | Synthetic users | Real users |
|---|---|---|
| Speed | Minutes. A 25-person Mimiq web page test takes about 90 seconds. | Days to weeks for recruiting, scheduling, and sessions. Live A/B tests need enough traffic to reach a result. |
| Cost | Low per test. In Mimiq, one credit per simulated person, or 5 when they use your site in a flow, from $9 for 120. | Participant incentives, recruiting or panel fees, researcher time, and traffic for experiments. |
| What it is good for | Ranking versions, catching obvious clarity and trust problems, sharpening questions before research. | Ground truth on behavior, motivation, emotional context, expert workflows, and exact rates. |
| Known failure modes | Sycophancy if not built to disagree, generic reasoning for niche experts, no lived experience, unreliable exact numbers. | Small samples, recruiting bias, participants telling you what you want to hear, slow feedback loops. |
| Evidence type | Simulated. A hypothesis and a directional signal. | Observed or stated by people. The standard for high-stakes decisions. |
Mimiq figures come from its public benchmark. On 1,000 real headline A/B tests held out from its development, Mimiq's forecast picked the headline more readers clicked 76% of the time; the best rule of thumb, picking the headline written first, got 61%. Mimiq's forecast has held up on headlines. In pre-registered tests on emails, text messages and ads, it was no better than chance at ranking small wording changes, and on real web page tests it did not beat the simplest guess.
Questions
Are synthetic users accurate?
Accurate enough to be useful for direction and obvious problems, and not accurate enough for exact numbers. On 1,000 real headline A/B tests held out from its development, Mimiq's forecast picked the headline more readers clicked 76% of the time; the best rule of thumb, picking the headline written first, got 61%. Mimiq's forecast has held up on headlines. In pre-registered tests on emails, text messages and ads, it was no better than chance at ranking small wording changes, and on real web page tests it did not beat the simplest guess.
Can synthetic users replace user interviews?
No. They can make interviews better by helping you arrive with sharper questions and fewer obvious problems, but they cannot tell you what your actual customers experience.
Why do some synthetic user tools seem overly positive?
Language models are trained to be helpful and agreeable, so a naive simulated person tends to say yes. Useful tools build in skepticism and a real option to leave.
When should I trust synthetic users least?
When the audience is a narrow expert group, when the question is about feelings or lived experience, when the product category is new, and when two versions differ only slightly.
Are the people in the report real?
No. Everyone in a Mimiq report is simulated. Even when you import your own customers, each twin is a simulated person built from one row of your list, not the customer. They are not real people, customers, or a panel.
How can I check whether simulation works for my audience?
Take a page change you already tested live, run both versions through Mimiq, and see whether it picks the same direction. That tells you more about fit for your audience than any general benchmark.
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