AI focus group: ask 25 simulated customers anything

A traditional focus group takes weeks to organize and costs more than most early decisions are worth. So teams skip it and decide in a meeting. An AI focus group sits in between: a quick, structured way to hear how different customers might react to an idea, a name, or a choice, before anyone commits.

• No URL needed: ask about a concept, a feature, a name, or a trade-off.
• 25 simulated customers by default, or 10, 50, or 100.
• Answers broken out by segment: role, age, gender, and country.
• Ask the room a follow-up, or interview any one person.

Each participant is a simulated person with a profile matched to the audience you set, including a role, an age, and a country. Each one answers your question in character.

Unlike a real focus group, the people do not hear each other while they answer, so there is no groupthink and no loudest voice in the room. The report does the synthesis: a plain-language headline, counts with honest ranges, the objections ranked by how many people raised them, segments, and every person's own words.

Then keep the conversation going. Put a follow-up to the whole room and open answers come back grouped into themes, while a yes or no question comes back as a tally. Or pull one person aside for an interview. Both are free within a fair daily limit.

The audience matters more than the wording of the question. Mimiq infers one from your question, but an inferred audience is often broader than the people you sell to. Describe the role, company size, or situation of the buyer who actually decides.

If you sell to more than one group, say so in the audience description. The report breaks answers out by segment, so a split between, say, founders and procurement leads shows up clearly instead of averaging into a bland middle.

If you have a customer list, import it from a CSV as twins: one simulated person per row, with names, emails and phone numbers stripped before upload.

Resist describing the audience as already convinced. A room full of people who love the category will tell you what you want to hear. Include skeptics and people who currently solve the problem another way.

Concept checks. Describe a product or feature idea and ask whether it solves a problem the audience has, and what would stop them trying it.

Choices between options. Offer two or three names, positioning angles, or plan structures and ask which one each person would pick and why. Comparisons are where simulated feedback is most useful.

Objection mapping. Ask what would make them hesitate to switch from their current tool. The answers are a draft list of objections to handle on your page and in sales conversations.

Vocabulary. Ask how they would describe the problem in their own words. The phrases that come back often make better headlines than internal language.

It cannot tell you how many people will actually buy. Stated intent overstates behavior even with real people, and Mimiq predicts direction, not size. Treat 'yes, I would try it' as weak evidence and objections as strong leads.

It is weakest for narrow expert audiences, deeply personal or emotional topics, and brand-new categories where there is little public record of how people behave. For those, talk to real people.

It also cannot replace the texture of a real conversation: the pause before an answer, or the story someone tells about the last time the problem hit. Use the synthetic round to decide which questions deserve that time.

Once an idea survives the question round, test it where it will live. Put the winning message on a page and run a web page test, test the ad, or test the launch email. Each step turns an opinion into a reaction to something concrete.

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 supports using it to narrow options. It predicts direction, not the size of the lift, so the final call on anything important belongs to real customers or a live experiment.