You know your customer better than any publisher does — your order data tells you exactly who converts. What you cannot see is which content sites, affiliates and newsletters reach that same person. Cookieless Audience closes the gap: a coded audience profile for any of 102 million domains — age brackets, income bands, life stages, 285 interests and 283 purchase-intent segments aligned with IAB Audience Taxonomy 1.1 — so you can compare every candidate partner against your buyer profile before signing the sponsorship, approving the affiliate or committing the budget.
E-commerce teams measure everything after the click — and almost nothing before the contract. A sponsorship gets signed because the site “feels on-brand.” An affiliate gets approved because the network category says “Home & Garden.” A newsletter placement gets bought on an audience one-pager the seller wrote about themselves. The money is committed at the moment you know the least, and flat-fee deals in particular — sponsorships, fixed placements, content partnerships — pay out in full whether or not the audience ever matched.
Performance data eventually reveals the mismatches, but slowly and expensively: a quarter of commission statements, a burned sponsorship budget, an affiliate program whose average conversion rate hides a wide spread between right-audience and wrong-audience partners. And post-click attribution is noisiest exactly where much of your audience lives — Safari, Firefox and iOS already block third-party cookies (roughly 40%+ of traffic; Chrome still supports them), so the feedback loop you are waiting on is itself degraded. The cheapest fix is upstream: select partners whose audience demonstrably matches your buyer before any of that spend happens.
That is a data problem with a direct solution: describe your buyer and every candidate site in the same coded vocabulary, then treat partner selection as a match query rather than a judgment call. The full vocabulary — demographics, interests (INT.*), purchase intent (PI.*), 1,667 personas, banded confidence — is documented on the taxonomy page.
Programmatic buys audiences impression by impression. These channels buy sites — which is exactly what domain-level profiles describe.
Score inbound applications and prospect lists against your buyer profile — approve on audience match, not on network category labels or traffic estimates.
Vet flat-fee deals — site sponsorships, newsletter slots, fixed banners — where all the risk is paid upfront and the seller's media kit is the only evidence on the table.
Choose co-marketing, gift-guide and review-content partners whose readership overlaps your customer — and bring coded evidence to the negotiation.
Profile the sites already sending you converting traffic, learn what they have in common, and recruit lookalike partners from the same coded cells.
Four steps. The only input you need from your side is what your analytics already tell you: who buys.
Translate your converting customer into vocabulary terms: age_bracket, income_level, life_stage, the INT.* interests they read about and the PI.* segments that map to your catalog.
Join applicant lists, prospect lists and current partners to the database — or check individual sites and their category sections with the real-time API when the placement lives in one corner of a bigger site.
Require your primary purchase-intent segment at medium-or-high confidence, then rank by demographic and interest overlap. The result is a shortlist with a stated, repeatable rationale.
Negotiate with evidence, spend where the match is proven, and re-score the roster each quarterly refresh — audiences drift, and yesterday's fit is not a permanent fact.
Illustrative scenario: a DTC outdoor-furniture brand is choosing between two sponsorship offers at similar prices. Buyer profile from its own order data: homeowners, 35–54, upper-middle income, suburban, entertaining at home. Coded: home_ownership: owner age_bracket: 35_44, 45_54 income_level: upper_middle PI.furniture.outdoor_furniture.
Sign. Primary intent segment (PI.furniture.outdoor_furniture) present at high confidence and every demographic leg of the buyer profile matches. Smaller audience, but it is your audience.
Pass, at this price. Six times the reach, but the audience is young urban renters (home_ownership: renter, urbanicity: urban) — people without patios. The intent segment that pays for the placement is present only at low confidence. Reach is not the constraint; fit is.
Without the profiles, Candidate B wins the meeting every time — bigger name, bigger number. With them, the decision inverts, and the reasoning is on paper: the same comparison your finance team can read, and the same rule you will apply to the next twenty candidates.
| Selection method | What it tells you | What it misses | Risk profile |
|---|---|---|---|
| Seller's media kit | The audience as the seller describes it | Independent verification of any of it | Full spend at risk on trust |
| Traffic / reach estimates | How many visitors | Whether any of them are your buyers | Pays for volume, not fit |
| Network category labels | Broad topic bucket | Owner vs. renter, premium vs. budget, buyer vs. browser | Coarse filter; mismatches pass through |
| Wait for performance data | Ground truth, eventually | Only after budget is spent; noisy in cookieless traffic | Pays tuition on every mistake |
| Coded audience match | Demographics, interests and purchase intent vs. your buyer profile, with confidence bands | Not a reach number — pair with volume data | Mismatches filtered before money moves |
Most brands start with the slice that matches their category: vertical slices run $190–$490, the top-100k file is $490, and the top-1M file is $1,990 with instant card checkout — enough to score every affiliate application, sponsorship offer and referrer you will see this year. Page-level checks via the API start at $99/month.
Take what your order and analytics data already show — typical age range, price sensitivity, homeowner vs. renter signals, product categories bought — and express each as a value from the fixed vocabulary: an age_bracket, an income_level, a life_stage, plus the INT.* and PI.* codes closest to your catalog. The taxonomy page lists every valid value. That coded profile is then directly comparable to any of the 102M domain profiles — no modeling step in between.
Yes — flat-fee deals are where it matters most, because the whole cost is committed before any performance signal exists. A newsletter or sponsorship pitch always arrives with the seller's own audience description; the domain's coded profile is the independent check. If the pitch says “affluent homeowners” and the profile reads income_level: middle, home_ownership: renter, you have a negotiating position — or an exit — before signing. See sponsorship evaluation for the full pattern.
Use both levels. The domain profile is the right screen for whole-site deals and bulk scoring; for a placement inside a specific section — the garden vertical of a general magazine, a gift guide, a review hub — profile the actual section URLs with the real-time API (plans from $99/month for 10,000 credits). Sections can over- or under-index the site average substantially, and the section is what your buyers actually see.
Performance data is the ground truth, but it arrives late, costs real budget to generate, and only covers partners you already signed. It is also noisier than it used to be: roughly 40%+ of traffic (Safari, Firefox, iOS) already blocks third-party cookies, so post-click attribution undercounts exactly the conversions you are trying to learn from. Audience matching front-loads the decision — it filters the obvious mismatches for free, and once performance data exists, the coded profiles explain the spread and point at lookalikes worth recruiting next.
The same buyer-profile matching, applied across neighboring channels.
Paste the candidate site into the live demo and read its coded audience profile next to your buyer — or license your category slice and score every partner in one afternoon.