Cookieless Audience treats the web as a census: 102 million domains, each described with coded demographics, interests, purchase intent and firmographics from fixed, versioned vocabularies aligned with IAB Audience Taxonomy 1.1. Market maps, category sizing and due diligence become dataset operations instead of survey projects.
Today's questions demand segment-level resolution — not “how big is fitness media” but “which properties reach high-income endurance athletes versus budget-conscious beginners?” Traditional inputs fall short in different ways.
They aggregate to whole categories on annual cycles. By the time the report ships, the market has already shifted beneath it.
Precise about the few hundred people asked and silent about everyone else. Scaling from a sample to a market-wide view requires big assumptions.
Panel-based web measurement resolves the head of the market but blurs out the mid-tail — often exactly where interesting entrants, acquisition targets and niche communities live.
Each domain becomes a row with coded attributes from a fixed vocabulary — the same 8 age brackets, 6 income bands and 14 life stages for every domain. Distributions aggregate cleanly without re-coding. Coverage does not depend on panels, cookies or site cooperation.
The same rows and codes back three very different deliverables. The complete field reference lives on the audience segmentation taxonomy page.
Slice a vertical's domains by audience attributes and lay out who serves whom: which properties reach income_level: affluent audiences, which own life_stage: family_young_children, where B2B and B2C supply overlap. The map shows crowded cells and white space in the same view.
Count and characterize the supply side of a category: how many domains carry a given interest or intent segment at high confidence, and how that supply distributes across demographics and geographies via vertical and country slices. Supply-side structure is the piece traditional sizing models usually have to assume.
Profile a target's domains and its competitors' on identical coded attributes: does the audience match the equity story? Is claimed differentiation visible in the interests and intent segments the properties actually attract? Pairs naturally with competitor audience benchmarking for the head-to-head view.
A typical engagement runs in four steps, all on the same coded fields.
Start from a vertical or country slice of the database, a list of category keywords, or a set of seed domains. The universe is explicit and reproducible — anyone can re-run the same filter on the same dataset version and get the same rows.
Express the market definition as attribute filters: INT.healthy_living.fitness_and_exercise at high confidence, audience_type: b2c, a target income band. Confidence bands let you run a strict cut and a broad cut of the same market.
Group the qualifying domains by any attribute to build the market's audience distribution — age mix, income mix, life-stage mix, intent-segment presence. Fixed vocabularies mean the group-by is trivial and comparable across markets and dataset versions.
For named companies in a diligence or map, drop to page level with the real-time API — profile a target's key sections individually rather than trusting the domain average, and attach the coded evidence to the report.
An illustrative diligence question: a fund is evaluating a portfolio of fitness content sites and wants to know which audience pocket each property occupies. One row of the dataset looks like this — raw codes on the left, rendered labels on the right.
{
"domain": "example-strength-training-hub.com",
"audience_profile": {
"demographics": {
"age_brackets": [
{"code": "25_34", "confidence": "high"},
{"code": "35_44", "confidence": "med"... }
],
"gender_skew": {"code": "male_lean",
"confidence": "medium"},
"income_level": {"code": "middle",
"confidence": "medium"}
},
"interests": [
{"code": "INT.healthy_living.fitness_and_exercise",
"confidence": "high"}
],
"purchase_intent": [
{"code": "PI.recreation_fitness.gyms_and_health_clubs",
"confidence": "medium"},
{"code": "PI.apps.health_and_fitness_apps",
"confidence": "medium"}
],
"audience_type": "b2c",
"vocabulary_version": "v1.0"
}
}
It does not replace primary research — it replaces the guesswork between primary data points.
| Dimension | Syndicated reports | Surveys & panels | Domain-level audience dataset |
|---|---|---|---|
| Resolution | Whole categories | Sampled individuals | Every domain, coded attribute by attribute |
| Coverage of the mid-tail | Rarely itemized | Sample too thin | 102M domains, uniform schema |
| Reproducibility | Methodology summarized | Depends on instrument | Versioned vocabularies; same filter, same rows |
| Freshness | Annual cycles | Per-wave | Quarterly refresh; API on demand |
| What it measures | Analyst synthesis | Stated attitudes and recall | Audience each property's content is built for, with banded confidence |
| Best used for | Context and benchmarks | Why questions | Structure: who serves whom, where the white space is |
You define the market as a filter over coded attributes — for example, all domains carrying a given interest or purchase-intent segment at high confidence within a vertical or country slice — then aggregate the qualifying domains into distributions by age, income, life stage or firmographics. That yields the supply-side structure of the market: how many properties serve it, and how their audiences are distributed. It complements, rather than replaces, demand-side sizing from spend or revenue data.
Yes. A common pattern is profiling a target's domains alongside its stated competitors on identical coded attributes, then checking whether the audience the content actually attracts matches the audience in the equity story — demographics, intent segments, B2B reach. Because the method needs no cooperation from the target and covers mid-tail properties, it works pre-LOI. Page-level drill-downs via the real-time API let you profile individual sections of a target's sites rather than relying on the domain average.
Syndicated reports summarize categories; panels estimate visitor demographics for sites with enough panel traffic. This dataset describes every domain individually — 102 million of them — from its content, using the same fixed vocabularies for each, with a low/medium/high confidence band on every model-inferred attribute. It resolves the mid-tail that panels blur out, and its coded output feeds directly into your own analysis instead of arriving as prose. The approaches are complementary: panels measure observed visitors where they can; this measures what each property's content is built to attract, everywhere.
The top 100k domains with full audience attributes cost $490 one-time ($190/quarter refresh); the top 1M is $1,990 with instant checkout ($590/quarter refresh); vertical and country slices run $190–$490. Larger corpora — from 5M up to the full 102M domains — plus custom enrichment and feeds are quoted individually, with custom licensing from $15,000/year. See pricing or contact us for a research or redistribution license.
The same rows power narrower questions on either side of a market study.
Profile a few category domains in the live demo to see the coded fields, then license the slice of the database your study needs — from a $190 vertical cut to the full 102M-domain corpus.