Synthetic users: how they work and what they are for
Synthetic users are simulated customers that react to your product in character. Used well, they surface objections and confusion before launch. Used badly, they produce confident fiction. This page covers the difference.
Why people look for synthetic users
Teams want to pressure-test a page, a flow, or an idea before the first campaign dollar goes out. Recruiting real participants takes time and money, and asking one chatbot produces one polite opinion. Synthetic users promise many opinions, quickly. The question is how to get ones worth acting on.
- A cohort of audience-specific personas instead of one assistant voice.
- Built to disagree, hesitate, and leave, not to be agreeable.
- Available across Page, Flow, and Ask modes.
- Best used as directional signal before real research and live experiments.
How it works
- Step 1
Describe the audience
Paste a URL or type a question. Mimiq infers who the likely customer is. Adjust it if the inferred audience is too broad or simply wrong.
- Step 2
Run the cohort
Page and Ask tests run 25 synthetic users by default in about 2 minutes. Flow tests send 5 browser agents through the site in real browser sessions.
- Step 3
Question the results
Read the outcome counts and ranked objections, then click any persona to ask why it hesitated or what would have changed its mind.
What a synthetic user is
A synthetic user is a simulated person with a defined profile: who they are, what they need, what they already use, how much they can spend, and how patient they are. A language model plays that person while it reads your page, attempts a task, or answers a question.
One synthetic user is an anecdote. A cohort is more useful. When 25 personas with different profiles react to the same page, the pattern of who converts, who hesitates, and who leaves tells you more than any single reaction.
In Mimiq, personas are sampled from a 5.5M-profile population skeleton pool and shaped to the audience inferred from your page or question. You can edit that audience before running. The personas are simulations, not real people and not a recruited panel.
Useful synthetic users disagree
The main failure of naive synthetic users is sycophancy. Language models are trained to be helpful, so a persona asked 'would you sign up?' tends to say yes. That produces inflated enthusiasm and no useful objections.
Useful synthetic users need permission and pressure to say no. Mimiq's personas carry limited attention, competing priorities, and a default of skepticism. When a page is vague, they say so and leave. Their objections are ranked by how many personas raised them, with the persona evidence attached.
A second failure is sameness. If every persona sounds like the same writer, you are hearing one opinion 25 times. Varying role, budget, context, and motivation is what creates genuine disagreement between segments, and segment differences are part of every Mimiq brief.
Where synthetic users help most
They work best early: before ad spend, before a release, before a redesign ships, and before a message is locked in. They are good at catching problems most visitors would notice, like an unclear value proposition, missing pricing, weak proof, or a confusing next step.
They are also useful for comparison. On 23 published A/B tests, Mimiq picked the winning direction about 78% of the time. That supports using synthetic users to rank two versions of a page. It does not support using them to forecast an exact conversion rate or lift, which Mimiq is not reliable at.
They also make iteration cheap. Because a run takes minutes, you can fix one issue, rerun the same audience, and see whether the objection disappears, which is hard to do with recruited participants on a tight schedule.
Where synthetic users fall short
Synthetic users are weakest where the answer depends on knowledge or experience that is thin in public text: niche expert buyers, emotional and lived experiences, local context, and genuinely new product categories.
They also cannot tell you what your existing customers actually did. If you have analytics, support tickets, or interview notes, those are stronger evidence for what already happened. Synthetic users are for the questions you cannot yet answer with your own data.
The honest workflow is to use synthetic users to narrow the options and sharpen the questions, then take the survivors to real customers or a live experiment.
Questions
Are synthetic users accurate?
They are useful for direction, not precision. On 23 published A/B tests Mimiq picked the winning direction about 78% of the time, and it is not reliable at predicting exact conversion rates or lift sizes. The benchmark page shows the method and the known weaknesses.
Are synthetic users the same as real users?
No. They are simulations and should be treated as a fast first pass. Our guide on synthetic users vs real users covers where the two agree and where they diverge.
Can I ask follow-up questions to a synthetic user?
Yes. After a run, click any persona and ask why it hesitated, what it expected to see, or what would make it sign up. It answers in character based on its profile and what it saw.
How many synthetic users should I run?
The default is 25 for Page and Ask tests, which is enough to see which objections repeat across personas. Flow tests default to 5 browser agents because each one works through the real site.
Can I use synthetic users for B2B products?
Yes, with care. They handle common B2B buying concerns like pricing clarity, proof, and setup effort reasonably well. They are weaker for narrow expert roles, so treat those results as hypotheses to check with real buyers.
What do synthetic users cost in Mimiq?
Your first test is free with no account. After that, credit packs are one-time purchases: 500 credits for $29, 2,000 for $99, 5,000 for $199. One credit is one persona evaluation, and credits never expire.
Keep reading
See what 25 skeptical customers think of your page.
Paste a URL. In about 2 minutes you get their objections, the fixes that matter most, and a report you can share. Treat it as a fast first pass, then validate big bets with real users.
Last checked 2026-09-22.