Two domains with identical traffic can differ enormously in value — because value lives in who the visitors are. Cookieless Audience provides pre-computed audience attributes for 102M domains, covering income, firmographics, intent and personas. Every attribute is drawn from fixed, versioned vocabularies aligned with IAB Audience Taxonomy 1.1.
Audience-based valuation assesses a domain or publisher portfolio by the audience its content attracts — demographics, interests, purchase intent and firmographics — rather than by traffic volume or keyword rankings alone. It answers the question a buyer's memo actually asks: is this reach monetisable, by whom, and for what?
Advertising markets price audiences unevenly — finance, B2B decision-makers and in-market shoppers command premium demand. A valuation that ignores audience composition inherits none of that information.
An audience skewing to the top income bands supports premium categories — investments, luxury travel, high-end goods — that thinner audiences cannot. Six income bands and seven education levels make the skew explicit.
A niche blog read by IT directors at mid-size companies is a different asset from a consumer site of equal size. B2B fields — job function, seniority, company-size band — surface that difference per domain.
Domains whose audiences carry high-value intent segments — mortgages, investments, software — sit closest to transactions. They monetise accordingly through affiliate, lead-gen and endemic advertising.
A tightly-defined audience with high-confidence attributes is easier to package and sell than a diffuse one. Banded confidence (low / medium / high) on every attribute lets you discount noisy profiles systematically.
Enrich a target's entire domain portfolio in one join and test whether the claimed audience matches what the content actually attracts.
Triage aftermarket portfolios at scale: 102M-domain coverage means even long-tail names carry an audience profile to reason from.
Compare acquisition candidates on audience quality and coherence. Spot the under-monetised asset whose audience outclasses its CPMs.
Pressure-test a media or commerce thesis: does the portfolio company's audience actually contain the buyers the deck claims?
Target portfolio, comps set, or an aftermarket watchlist — any list of domains, from ten to millions.
Match against pre-computed records: demographics, interests, PI.* intent, personas and B2B firmographics per domain.
Define the factors your thesis prices — income skew, decision-maker share, intent density — and weight them by confidence band.
Rank candidates on audience factors next to traffic and financials. Spend diligence hours only where the audience justifies them.
Valuation work needs data you can cite in a memo: fixed vocabularies, versioned releases, per-attribute confidence and no PII dependency. That is the entire design of this dataset.
A roll-up screens three content sites of comparable size. The audience records — codes with their labels — tell three different stories.
| Attribute | Personal-finance content site | Celebrity & entertainment site | DevOps tutorial blog |
|---|---|---|---|
| Interests | INT.personal_finance.personal_investing Personal InvestingINT.personal_finance.retirement_planning Retirement Planning |
INT.pop_culture.celebrity Celebrity News & GossipINT.television.reality_tv Reality TV |
INT.tech_computing.computing Computing |
| Purchase intent | PI.finance_insurance.stocks_and_investments Stocks & InvestmentsPI.finance_insurance.retirement_planning Retirement Planning |
— low intent density | PI.web_services.web_hosting_and_cloud_computing Web Hosting & Cloud ComputingPI.software.computer_software Computer Software |
| Income level | high / affluent |
middle |
upper_middle |
| Audience type | b2c |
b2c |
b2b — b2b_job_function: it_ops, b2b_seniority: senior_ic |
| Confidence | high |
high |
medium |
| What the buyer learns | Premium-demand audience; strongest monetisation ceiling of the three | Value rests on volume and social reach, not audience quality | Small but B2B-dense; fits a developer-media thesis, verify with diligence |
The data doesn't output a price — no dataset honestly can. It tells you which of three similar-looking assets deserves the deeper look, and gives your memo coded, citable evidence for why.
| Signal | What it measures | What it misses | Role in valuation |
|---|---|---|---|
| Traffic estimates & rank | Volume of reach | Who the visitors are | Sizing the asset |
| SEO / backlink metrics | Search durability | Monetisability of the audience | Sustainability of reach |
| Seller-reported financials | Current monetisation | Unrealised potential | Pricing the present |
| Audience attributes (this dataset) | Who the content attracts: demographics, intent, firmographics | Volume — pair with traffic data | Pricing the potential; screening at portfolio scale |
The signals are complements. Audience attributes are the column that traffic tools and SEO suites cannot provide — and the one that separates two domains the other columns score identically.
The operator's view of the same data: prove your audience to advertisers after the acquisition closes.
Benchmark a target against its category before you price it.
The same profiling discipline applied to winning accounts instead of buying assets.
What high-intent audiences are worth to buyers — the demand side of the valuation question.
Zoom out from single assets to whole-category audience structure.
The industry view: audience data across sourcing, diligence and portfolio work.
Traffic and SEO tools measure how many people reach a domain and how durably. They say almost nothing about who those people are.
This dataset provides the missing column: audience composition — income bands, interests, purchase intent, B2B firmographics — per domain, from fixed vocabularies. In practice you use both: traffic data sizes the asset, audience data qualifies it.
Yes — portfolio screening is the primary mode. The database ships as a file you join to your own lists:
For 5M up to the full 102M corpus, custom enrichment or recurring feeds, contact us for a quote — custom licensing starts at $15,000/year. See pricing.
It depends on the thesis, but four factors recur:
PI.finance_insurance or PI.softwareAll are enumerated fields documented in the taxonomy, so factor models built on them are reproducible.
Every attribute carries a banded confidence value — low, medium or high — and serious valuation work should weight or filter on it.
Profiles describe the audience a domain's content attracts, inferred without PII or identifiers, on versioned vocabularies so profiles remain comparable across quarterly refreshes. For a decision-critical domain, verify the current picture with the real-time API at specific URLs.
Profile a live acquisition candidate in the demo, or license the Top 1M database and join audience attributes to your entire pipeline today.