Compare your audience profile against any competitor's — a rival publication, a category leader, or a fast-growing upstart. Every profile is derived from the domain's content across 102 million domains, using the same coded attributes: demographics, 285 interests, 283 purchase-intent segments, B2B firmographics and 1,667 personas. No panels, no tracking scripts, no PII.
Every team eventually asks the same question — who reads them, versus who reads us? The honest answer is usually assembled from fragments because each existing source has structural limits.
Panel-based products estimate demographics from an opt-in sample. Mid-tail competitors often show no data at all, or estimates built on a handful of panelists.
Readership studies are rigorous but expensive, and only cover titles that commissioned them. Media kits are self-reported by the competitor you are evaluating.
Cookie-based audience overlap tools stopped seeing Safari, Firefox and iOS traffic years ago. Their “overlap” describes an unrepresentative remainder of the web.
Reads what each domain publishes and infers the audience it attracts, using fixed vocabularies aligned with IAB Audience Taxonomy 1.1. Any two domains are directly comparable.
Every domain profile carries the same field families, so a benchmark is a straightforward diff of coded values. Browse the full vocabulary on the audience segmentation taxonomy page.
8 age brackets, 5-point gender skew, 6 income bands, 7 education levels and 14 life stages — plus household composition, employment, home ownership and urbanicity.
29 interest groups with 285 sub-interests (INT.*) and 34 intent groups with 283 in-market segments (PI.*). Same vertical, different commercial audience.
1,667 deterministic personas mapped from IAB content categories, plus B2B fields in LinkedIn-standard bands (job function, seniority, company size).
Every model-inferred attribute carries a low, medium or high confidence band. Restrict to high-confidence attributes when the benchmark needs to survive client or board scrutiny.
The same content categories always map to the same personas, so persona-level comparisons are stable across dataset refreshes — fully auditable, never inferred.
Four steps from a list of competitor domains to a side-by-side report. For a handful of domains the interactive demo is enough; for recurring or large comparison sets, teams use the domain database or the API.
List the domains to compare: your properties, direct competitors, aspirational benchmarks. Coverage is 102M domains, so mid-tail competitors are included on equal footing.
Look each domain up in the pre-computed dataset, or query the real-time API for page-level resolution on specific sections. Every profile returns the same field set.
Attributes are fixed vocabulary codes — age_bracket: 25_34, income_level: upper_middle — so comparison is a join, not an interpretation exercise.
Render codes as human-readable labels for the deck or dashboard. Quarterly dataset refreshes let you re-run the same diff and watch positioning move over time.
Both domains sit in the same IAB content vertical — personal finance — yet their coded profiles show they are barely competing for the same reader.
Codes underneath: age_bracket: 35_44, 45_54 gender_skew: male_lean income_level: upper_middle INT.personal_finance.personal_investing PI.finance_insurance.stocks_and_investments
Codes underneath: age_bracket: 25_34, 35_44 gender_skew: female_lean income_level: middle INT.personal_finance.frugal_living PI.finance_insurance.credit_and_debt_repair_credit_reporting
The takeaway: Publication A monetizes an investing audience with brokerage and wealth-management intent. Publication B reaches younger, family-stage households in-market for credit products. A brokerage advertiser is not choosing between “two finance sites” — the coded diff makes that explicit before a dollar is planned.
Each approach answers a different question well. Content-derived profiles are the only option that covers every domain with the same attribute schema.
| Dimension | Panel-based measurement | Surveys & media kits | Content-derived profiles |
|---|---|---|---|
| Coverage | Strong on large sites; thin to absent in the mid-tail | Only titles that commissioned or published them | Any of 102M domains, uniform schema |
| Comparability | Comparable within the panel's methodology | Self-defined metrics; rarely comparable across titles | Same versioned codes for every domain |
| Attribute depth | Core demographics, some behavior | Whatever the publisher chose to report | Demographics, 285 interests, 283 intent segments, firmographics, personas |
| Independence | Independent of the measured site | Self-reported by the competitor | Derived from published content, same method for all |
| What it measures | Observed sample of visitors | Surveyed or claimed readership | Audience the content predictably attracts, with banded confidence |
| Refresh cadence | Monthly, where covered | Annual at best | Quarterly dataset refresh; on-demand via API |
These are complements, not substitutes. Where panel numbers exist, they validate the content-derived view with observed traffic. Where they do not — which is most of any real competitive set — content-derived profiles are the only structured signal available.
By profiling their content instead of their visitors. Cookieless Audience reads what a domain publishes and infers the audience that content predictably attracts — age brackets, gender skew, income band, education, life stage, interests, purchase intent and personas — using fixed vocabularies aligned with IAB Audience Taxonomy 1.1. Nothing is installed on anyone's site and no visitor is observed.
Yes — that is the main advantage. Panel-based products need enough panelists visiting a site, which excludes most mid-tail and trade sites. Content-derived profiles cover 102 million domains with the same schema, so a niche trade publication is benchmarked with the same attribute set as a top-100 property. For section-level comparisons, the real-time API profiles individual URLs.
Every model-inferred attribute carries an explicit low, medium or high confidence band, and unsupported attributes are omitted rather than guessed. Restrict to high-confidence for client scrutiny; include medium for exploratory work. Personas are deterministic (category-to-persona mapping), so persona comparisons are stable and fully auditable.
For occasional lookups, the live demo and the API (plans from $99/month for 10,000 credits) cover it. For recurring benchmarking, the pre-computed database is more economical: the top 100k domains cost $490 one-time, the top 1M is $1,990 with instant checkout, and vertical or country slices run $190–$490. See database pricing.
Audience benchmarks rarely stand alone — they feed sizing work, sales stories and partner decisions.
Open the demo, enter your domain and a competitor's, and compare the full coded profiles side by side — demographics, interests, purchase intent, personas and confidence bands.