Synthetic audience research, built on public data

Ask any audience anything.

userken builds synthetic audiences out of public data, then lets you put a question to them and read the answer in seconds, with the segment breakdown and the real quotes behind it. No panel to recruit, no survey data of your own needed to start.

511,891 public reviews indexed73 apps, 11 categories, growingWorks inside Claude
Audience preview · All News apps live
48,998 public reviews, clustered into weighted personas
5 stars
38%
4 stars
8%
3 stars
7%
2 stars
9%
1 star
37%
The Update-Betrayed LoyalistThe Betrayed Neutrality SeekerThe Privacy Line Drawer
"The most recent update made a well made, smooth, easily navigable app into a buggy frustrating mess."
Audiences indexed today
Google ChromeBraveFirefoxWhatsAppDuckDuckGoOperaChatGPTSpotifyand 65 more
How it works

Three steps, no fieldwork.

01

We build the audience from public data

Every public review we index gets embedded and clustered into personas with real weights: how many people actually sound like that. You pick an audience by category, by competitor, by app, or by filter. Nothing of yours is required to begin.

02

You ask, in the app or inside Claude

Single choice, multi select, Likert, ranking or open ended. Use the web app, or call the same tools over MCP so the question happens inside the Claude conversation you were already having.

03

Results in seconds, with the breakdown

A weighted distribution across the whole audience, the split by persona segment, the verbatims each segment gave, and the review ids that grounded the answer so you can go read the source.

Every source we use

Audiences

Start with ours. Build your own.

Browse all audiences
Category
All Browsers apps
168,295 reviews · 75 personas
Category
All Chat apps
56,951 reviews · 42 personas
Publication
Google Chrome
70,883 reviews · 7 personas
Publication
Brave
30,817 reviews · 10 personas

Describe an audience in a sentence and userken turns it into filters, previews it, and builds personas for it. "Android users of financial news apps who complain about login" is a working audience in under a minute.

Use cases

Real answers, not guesswork.

All use cases
For the teams that decide
Works inside Claude

The same tools, where you already work.

Every audience, survey and focus group is also an MCP tool. Connect userken to Claude with your API key and ask the audience mid conversation, or paste a focus group prompt and moderate the room yourself.

Read the docs
ask_audience(
  question="Would you pay $4.99 a month to remove ads?",
  options=["Yes", "No", "Not sure"],
  audience_ref="cat:news",
  samples_per_persona=5,
)
# weighted distribution, by_persona split,
# verbatims and grounding_review_ids
Accuracy

You can check our work.

We hold out recent reviews, ask the audience, and score the answer against what real people said. Two measures, published every run, next to the human ceiling and an ungrounded model.

NDAM
33.2%
stricter measure
1-MAE
87.8%
held out
Compared

Where it sits.

The full comparison
userkenGeneral LLMTraditional research
Time to an answer Seconds to minutes Seconds Weeks
Grounded in real people Public reviews, ids recorded No Yes, recruited
Segment breakdown Weighted personas One voice Yes, if sampled
Accuracy method published Yes, on a public page No Per study
Audience for a competitor Yes, the data is public Nothing specific Hard and costly
Representative of a population No, and we say so No Yes, when designed for it
Cost shape Free tier, then credits Your existing subscription Per respondent
Questions

Fair questions, straight answers.

All 20 in the FAQ

How is this different from just asking ChatGPT or Claude directly?
A general purpose model answers from its training data and gives you one voice with no weights, no segments, no sources and no error bar. userken builds the audience from a specific, inspectable corpus of public reviews, samples each persona several times, weights the result by how large that segment actually is, grounds every reply in real quoted text, and records the review ids used. We also measure the difference: our accuracy work reports an ungrounded LLM baseline, which is exactly the 'just ask the model' answer, next to ours.
What data is it built from?
Public app store reviews today: Apple App Store and Google Play, across the news and browser categories, with more categories being added. Reddit, Hacker News, Bluesky and your own CSV uploads are next. See /sources for live coverage numbers.
How accurate is it?
We publish it rather than claim it. /accuracy explains the two metrics we report (1-MAE and NDAM), how we hold data out by time, the baselines we score against including an ungrounded LLM, and the human ceiling from split half agreement. Until the first benchmark run completes, that page shows a clearly labelled pending state rather than a number.
Can I use my own data?
That is on the way. CSV upload of support tickets, survey verbatims or transcripts will let you build private audiences, mix your data with public sources, and run accuracy reports on your own audiences. Public data is what makes the product work without it.

Stop guessing. Start asking.

Free on the public audiences. Pro from $39 a month for your own.