A creator's own age tells you who they are. Their audience's age tells you who your campaign will actually reach. Audience Age shows the age distribution of the people who engage with a creator, in five brackets from Under 18 to 45+. It's measured on the engaged audience, and it only ships for creators where the sample behind it is large enough to be trustworthy.
Start From the Engaged Audience
For each creator, we analyze a sample of their engaged audience. These are the people a brand actually reaches, which makes them a better base for demographics than raw follower counts. Audience estimates are extrapolated from public engagement signals, and every audience statistic is an aggregate.
The Audience We Analyze
Every audience data point we publish is built on the people who actively interact with a creator's content, because they're the ones who see and respond to campaigns. The full reasoning behind this approach is on the Audience Analytics page.
Age Detection on Public Signals
The engaged audience goes through the same age detection that runs across the rest of our data. It works from public signals only, and where the signal isn't clear enough, that part of the audience is excluded rather than guessed.
Clear Signal Counts, Weak Signal Doesn't
Not all of an audience carries enough public signal for an age estimate. That part is simply left out of the calculation. The distribution is always computed over the share of the audience where the signal was clear, never padded with guesses.
Aggregate With Quality Gates
Ages are counted into brackets, and the result is only published when the sample is large enough to be trustworthy. If a creator's audience sample is too small, the field stays empty rather than showing a shaky number.
From Individual Ages to a Distribution
A distribution computed from a handful of people is noise, not data. We require the sample behind the field to cover at least 1,000 engagers with a clear age signal before publishing, and up to 10,000 for creators with larger audiences. Below that floor, Audience Age stays empty for that creator. An empty field is a quality signal, not a gap.
Illustrative Example
Two creators, two very different audiences. The same travel content can reach mostly students or mostly professionals, and this is the field that tells you which one you're buying.
Two Creators, Side by Side
A young-adult audience. 85% of the engaged audience is 18-34. Strong fit for a student travel card or budget booking app.
An older, flatter distribution with no single dominant bracket. The audience is spread across life stages, so bracket-specific offers will only reach part of it.
Both creators could appear in the same search. The audience age distribution is what separates them: NikolaTravels concentrates 85% of engaged people into the two youngest adult brackets, while BorisLifeLog spreads across four. Neither is better in general. One is better for your campaign.
Output Format
The exact fields this data point ships with, shown with example values, and how to use them in search.
The Actual Fields
Each value is a percentage of the analyzed sample. The five values sum to 100%.
Filter creators by the share of their audience in a bracket, for example "audience 18-24 above 40%" for a student-focused campaign, or combine two brackets to target 18-34. Because the field is a full distribution, you can require a minimum share in your target bracket.
Validation 
Audience Age is validated at both levels: the underlying age detection, and the aggregated distributions built from it. Both go through the same two-track process used for every data point.
Getting the underlying age analysis right is not enough. We also validate the final distributions, comparing published audience brackets against independently built reference distributions for the same creators. This catches errors that only show up in aggregate, and it is why the field ships with a sample floor instead of shipping every creator with any data at all.