Demographics tell you who the audience is. Interests tell you what they care about, which is what actually decides whether they respond to a product. Audience Interests shows the top interest categories of a creator's engaged audience with the share for each, expressed in the same niche categories we use to classify creators. Like every audience data point, 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 interest data 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.
Interests Come From Content Niches
Interests are read from the content the audience engages with, using the same niche classification that categorizes creators across our platform. One taxonomy covers both sides: the niches a creator posts in, and the interest categories their audience follows. Where the signal isn't clear enough, that part of the audience is excluded rather than guessed.
One Taxonomy, Both Sides
Because creator niches and audience interests use the same categories, you can line them up directly: find a fitness creator whose audience is actually into fitness, or spot the more interesting case, a creator whose audience cares about something their content isn't about yet. Tools that use two different category systems for these can't make that comparison.
Aggregate With Quality Gates
Interest categories are counted and ranked, and the top of the ranking ships with a share for each. The result is only published when the sample is large enough to be trustworthy; below that, the field stays empty rather than showing a shaky number. The minimum is 1,000 engagers, and up to 10,000 for creators with larger audiences.
From Signals to a Ranking
People aren't one-dimensional: the same part of an audience can be into travel and photography at once. Each share answers "how much of the audience follows this category", so the numbers overlap by design instead of being forced into a fake single-choice split.
Illustrative Example
Two creators whose audiences want different things, which is exactly what decides which brands fit.
Two Creators, Side by Side
An audience that engages with travel content and cares about photography and food. Booking apps, camera gear, and food-tour brands all have a real angle here.
No standout interest: the top category covers just 12% of the audience. Broad-appeal products can work, but a niche brand won't find its crowd here.
NikolaTravels posts travel content, and his audience does follow travel, but photography and food rank right behind it. The audience is broader than the content, and for a camera brand that difference is the whole pitch.
Output Format
The exact field this data point ships with, shown with example values, and how to use it in search.
The Actual Fields
A list of interest categories, each with the share of the analyzed sample following it. Shares overlap, so they don't sum to 100%.
Filter creators by what their audience follows, for example "audience interested in Fitness above 15%" for a supplement brand. The strongest use is the mismatch case: a creator whose audience over-indexes on your category even though the creator isn't posting in it yet.
Validation 
Audience Interests is validated at both levels: the underlying niche classification, and the aggregated rankings built from it. Both go through the same two-track process used for every data point.
Getting the underlying niche classification right is not enough. We also validate the final rankings, comparing published audience interests against independently built reference data 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.