Rights holders sell audiences — to sponsors, broadcasters and advertisers — but the digital half of that audience lives across official sites, fan forums, supporter blogs, fantasy platforms and statistics communities that panel measurement barely covers. The Cookieless Audience database pre-computes an audience profile for 102 million domains, with 51 sport-level interest codes, full demographics and ticket, merchandise and memorabilia purchase-intent segments. Value the whole ecosystem around a property — not just the flagship site — and pitch it with fixed, reproducible codes instead of estimates.
INT.sports.*A club, league or federation can report its own site analytics. What it cannot easily prove is the audience of everything around it: the independent supporter sites, match-report blogs, fantasy and statistics communities, transfer-rumour forums and podcast companion pages where fans actually spend their time. Sponsors increasingly ask for exactly that — the shape and value of the full digital fanbase, not one property’s pageviews.
Panel-based measurement thins out fast below the head of the web, and cookie-based audience verification misses a growing share of traffic outright: cookies remain on Chrome, but Safari, Firefox and iOS block third-party cookies by default, putting roughly 40%+ of traffic beyond identifier-based counting. Fan sites — mobile-heavy, long-tail — sit squarely in that blind spot.
Domain-level audience profiles cover the gap. Each site’s demographics, sport-level interests and purchase intent are computed from the property itself, on fixed v1.0 vocabularies aligned with IAB Audience Taxonomy 1.1 — so the numbers in your deck are reproducible by the sponsor’s own analysts.
INT.sports.soccerINT.television.sports_tvINT.sports.fantasy_sportsPI.arts_entertainment.ticket_servicesPI.collectables_antiques.sports_memorabilia_and_trading_cardsFilter the corpus on these values and the result is the measurable footprint of a fandom — every domain where it concentrates, with demographics attached. Per-URL granularity is available through the real-time API.
Every attribute comes from a fixed enumerated list — no free text, no vendor-specific segment names — documented in full on the audience segmentation taxonomy page.
51 codes under INT.sports.* — soccer, basketball, cricket, auto_racing, golf, ice_hockey — plus fantasy_sports, INT.television.sports_tv and INT.music_audio.sports_radio for the second screen.
8 age brackets, a 5-point gender skew, 6 income bands, 7 education levels and 14 life stages — the profile sponsors actually ask about when they ask “who is this fanbase?”.
283 purchase-intent segments including PI.arts_entertainment.ticket_services, PI.arts_entertainment.fantasy_sports, PI.apps.sports_apps and PI.sporting_goods.athletics_equipment.
Urbanicity, household composition, employment and home ownership round out the picture — useful when a sponsor’s category (autos, finance, travel) skews on exactly those lines.
1,667 named personas resolve the attribute combinations into labels a commercial deck can carry — the same inputs always produce the same persona.
Every attribute carries a low / medium / high confidence band, so a pitch can state its evidence standard explicitly — and hold up under a sponsor’s due diligence.
The same sequence works for a league selling a title sponsorship, an agency valuing a rights package, or a club building its commercial deck.
Pick the sport and second-screen codes that describe the property — e.g. INT.sports.soccer + fantasy_sports.
Filter the 102M-domain corpus to sites where those interests concentrate: official, fan-run, statistical, editorial.
Aggregate demographics and intent across the footprint — age, gender skew, income, ticket and merch intent.
Rank domains by audience fit and concentration; separate the property’s owned reach from its earned fan-site halo.
Drop the profile into sponsor decks, rate-card justifications and media-rights negotiations — with codes a buyer can re-run.
A club’s commercial team is renewing a shirt-sponsor conversation and wants to show the sponsor the digital fandom beyond the official site. Here is the ecosystem filter — codes on the left, what they read as on the right.
| Field | Filter value (code) | Reads as |
|---|---|---|
| interest | INT.sports.soccer | Soccer / football content |
| interest (layer 2) | INT.sports.fantasy_sports | Fantasy-game communities |
| age_bracket | 25_34 or 35_44 | Core commercial fan demographic |
| income_level | middle to high | Sponsor-relevant spending power |
| purchase intent | PI.arts_entertainment.ticket_services | Actively shopping for tickets |
| purchase intent (2) | PI.collectables_antiques.sports_memorabilia_and_trading_cards | Memorabilia & trading-card buyers |
| confidence | medium or high | Well-evidenced attributes only |
| Social follower counts | Panel-based measurement | Domain-level audience profiles (this dataset) | |
|---|---|---|---|
| Covers fan sites & long tail | No — platform accounts only | Only panelled sites; thin below the head | Yes — 102M domains, head to tail |
| Demographic depth | Platform-reported, coarse | Good on large sites, sparse elsewhere | Full demographic, interest and intent model per domain |
| Commercial-intent signal | None | Rarely available | 283 PI.* segments incl. tickets, merch, memorabilia |
| Reproducibility for a buyer | Screenshots | Panel weighting shifts over time | Fixed v1.0 codes — sponsor’s analysts can re-run the filter |
| Cookieless traffic | N/A | Partially modelled | Fully covered — profile never depended on identifiers |
Yes — that is the core use. The database describes the audience of any domain in the corpus from its content and audience characteristics, so a rights holder can quantify the independent supporter sites, forums and fantasy communities around a property without needing access to their analytics. No PII is involved: the profile describes a readership, never an individual fan.
That is the point of the fixed vocabularies. Every attribute comes from a versioned v1.0 enumerated list aligned with IAB Audience Taxonomy 1.1 — 51 sport interests, 283 intent segments, 8 age brackets and so on — with a banded confidence value per attribute. A sponsor who licenses the same file and runs the same codes gets the same list, which makes the deck auditable rather than anecdotal.
Identically to everyone else. Cookies remain on Chrome, but Safari, Firefox and iOS block third-party cookies by default — roughly 40%+ of traffic is cookieless, and sports fandom skews mobile. Because the audience profile is attached to the domain rather than to visitor identifiers, fan sites with heavily cookieless traffic are described with the same fields and confidence bands as any other property.
Most start with the top-1M domain file: $1,990 one-time or $590/quarter with instant checkout, full audience attributes included. The top-100k tier is $490 one-time ($190/quarter), and vertical or country slices — e.g. sport-heavy domains for one market — run $190–$490. Larger cuts up to the full 102M corpus, custom enrichment or recurring feeds are quoted individually. See pricing.
Score sponsorship opportunities by the audience they actually deliver.
Turn audience attributes into slides that survive buyer scrutiny.
Value a property by the audience it reaches, not just its traffic.
The same playbook for gaming content networks and esports properties.
Filter the live data in the demo dashboard, or download the top-1M file and have a quantified fan-ecosystem profile in your next sponsor deck.
Open the audience demo See database pricing