The IAB Tech Lab Audience Taxonomy (current version 1.1) is the industry-standard classification for describing who an audience is — a fixed tree of demographic, interest and purchase-intent segments with stable node IDs, so that a segment means the same thing to every party in the supply chain.
Before the Audience Taxonomy, every data vendor named segments its own way. A media buyer comparing “Auto Intenders – Luxury,” “In-Market: Premium Vehicles,” and “High-End Car Shoppers” had no way to know whether they described the same people. Audience data was a market with no units of measure.
Every segment concept has a unique numeric node ID and a fixed position in a parent–child hierarchy. Data sellers map proprietary segments onto these IDs; platforms compare and transact in one shared coordinate system.
The taxonomy standardizes the label, not the method. Two vendors can build a “Hotels and Resorts” segment from different signals, but the buyer knows both claim the same concept — and can ask how each was derived.
With ~40%+ of traffic already cookieless, audience claims come from first-party and contextual derivations buyers cannot verify by retargeting math. A shared taxonomy plus disclosed methodology keeps those claims tradable.
Version 1.0 established the three-branch structure; version 1.1 refined the node set and is referenced by today's programmatic specs, most visibly Seller Defined Audiences. Our entire cookieless audience segmentation output vocabulary is built on this standard.
Everything hangs off three top-level branches answering three questions about an audience — who they are, what they care about, and what they are about to buy. A real segment is usually a composition across branches.
Stable descriptive attributes:
This is the branch behind every “A25-54” media plan. See how we infer these per page in website audience demographics.
Durable affinities — topics people consistently engage with, independent of any purchase. Sample tier-1 groups:
AutomotiveBusiness & FinanceCareersFood & DrinkHome & GardenSportsTechnologyTravel
Below each group sit specific nodes — Travel splits into destination and trip-type interests, Sports into individual sports.
In-market signals — product and service categories a person is actively shopping. The largest branch because commerce needs fine resolution. Sample groups:
Automotive OwnershipConsumer ElectronicsFinance & InsuranceReal EstateSoftwareTravel & Tourism
Intent segments decay after purchase. Why that matters for pricing is covered in purchase intent data.
| Branch | Question answered | Scale in 1.1 | Example nodes | Signal character |
|---|---|---|---|---|
| Demographic | Who is this audience? | Age, gender, education, household data, marital, personal finance | 25-29 · 30-34 · income band · urban | Slow-changing; backbone of reach planning |
| Interest | What do they care about? | 29 tier-1 groups, ~500 nodes | Travel · Sports · Style & Fashion | Durable affinity; good for upper-funnel |
| Purchase Intent | What are they about to buy? | 34 groups, 800+ nodes | Hotels & Resorts · Consumer Electronics | Perishable; highest CPMs; recency matters |
Every node carries a unique ID and a parent reference, so the same concept reads as a path or a single number.
A bid request or segment catalog transmits the number; both sides resolve it against the same file. No string matching needed.
A buyer targeting “Travel and Tourism” automatically includes every child node. Reporting rolls spend from leaf segments to branch totals.
Adoption means deciding which node ID(s) each of your segments corresponds to and recording that crosswalk where buyers can inspect it.
The taxonomy is a reference document, not a protocol — but several concrete mechanisms in programmatic advertising are keyed to its node IDs.
The most direct wiring: SDA lets publishers announce first-party segments in OpenRTB bid requests as Audience Taxonomy 1.1 node IDs (segtax: 4). See our SDA guide.
Data sellers list segments mapped to taxonomy nodes, so a buyer searching “Purchase Intent > Travel” sees comparable products from every provider side by side.
Platforms use the taxonomy as an internal schema: segments export, match or deduplicate across systems using the same node IDs. Reporting gets a stable dimension by branch and group.
Paired with data-transparency work, taxonomy IDs let a seller disclose what concept is claimed and how membership was derived. Standard labels make disclosures comparable.
Version 1.0 established the three-branch structure; 1.1 refined nodes and naming and has remained the referenced version for years. That stability is a feature — IDs are embedded in bid streams, deal terms and reporting pipelines.
A taxonomy that churned quarterly would break the comparability it exists to provide. Systems built against 1.1 IDs today are built on settled ground.
The two are constantly confused. The Content Taxonomy classifies what a page is about and drives contextual targeting. The Audience Taxonomy classifies who the people are and drives audience-based buying.
Separate trees, separate ID spaces, separate OpenRTB fields. For content-side guides, see our IAB Content Taxonomy 2.2 and Content Taxonomy 3.0 pages.
Rule of thumb: Content Taxonomy = what the page is about. Audience Taxonomy = who the people are. Contextual audience intelligence is the bridge: read the page with the first, infer the second — without observing a single user.
Our audience segmentation API infers the likely audience of a page or domain from content alone and returns every attribute from controlled, versioned vocabularies (v1.0) explicitly aligned with Audience Taxonomy 1.1. Each vocabulary value records its crosswalk to the IAB node IDs.
8 age brackets, 5-point gender skew, 6 income bands, 7 education levels and 14 life stages. Each value documents which IAB Demographic node IDs it spans — e.g. our 25_34 covers the taxonomy's 25-29 and 30-34 nodes.
Our interest vocabulary uses the Interest branch's 29 tier-1 groups as its backbone (INT.* codes), with 285 sub-interests beneath them. Model output is snapped onto canonical codes; unmapped output is dropped, never invented.
All 34 groups expressed as stable PI.* codes across 283 segments with zero custom additions. Every signal carries banded confidence. See our purchase intent data guide.
The full code lists, vocabulary JSON and per-value IAB crosswalks are published on the audience segmentation taxonomy page — browse any code and see the IAB node IDs it maps to.
A European city-break guide run through the structured endpoint. Left: the coded response from the closed vocabulary. Right: the same response rendered as labels for planners and deal descriptions.
{
"vocab_version": "1.0",
"audience_type": "b2c",
"demographics": {
"age_bracket": ["25_34", "35_44"],
"gender_skew": "balanced",
"income_level": "middle",
"life_stage": ["young_professional",
"newlywed_couple"],
"urbanicity": "urban",
"confidence": "medium"
},
"interests": {
"tier1": ["INT.travel"],
"tier2": ["INT.travel.europe_travel"],
"confidence": "high"
},
"purchase_intent": {
"codes": ["PI.travel.hotels_and_resorts",
"PI.travel.air_travel",
"PI.travel.sightseeing_tours_and_activities"],
"confidence": "high"
} // each code carries its IAB 1.1 node crosswalk
}
As a deal description: “25-44 urban city-break travelers, in-market for hotels, flights and tours” — composed from all three branches, derived entirely from what the page is about.
A standardized classification published by IAB Tech Lab for describing audience segments. It organizes attributes into three branches — Demographic, Interest and Purchase Intent — each a tiered hierarchy with unique node IDs, so all parties can describe, compare and transact segments using one shared vocabulary.
The Content Taxonomy classifies what content is about (page topics) for contextual targeting and brand safety. The Audience Taxonomy classifies who people are (demographics, interests, purchase intent) for audience-based buying. Separate trees, separate IDs, different OpenRTB fields — though content classification is often the evidence from which audience is inferred.
Version 1.1 has three branches. The Interest branch has 29 tier-1 groups expanding to ~500 nodes. The Purchase Intent branch is the largest with 34 groups and 800+ nodes. The Demographic branch covers age ranges, gender, education, household data, marital status and personal finance.
Version 1.1 is current. It refined structure and naming from 1.0 and is referenced by downstream specs — notably Seller Defined Audiences (segtax: 4). The taxonomy is intentionally slow-moving because its node IDs are embedded in bid streams, catalogs and reporting pipelines.
In SDA, a publisher attaches audience segments to OpenRTB bid requests as Audience Taxonomy 1.1 node IDs with segtax: 4. The buyer's DSP reads standard IDs rather than publisher-specific names, making first-party cookieless audience packages comparable across publishers.
Yes. The taxonomy defines segment vocabulary, not collection methodology. Segments from first-party data or content-based inference can be expressed in it just as cookie-derived segments were. Our approach infers audience from content alone and expresses results in vocabularies crosswalked to Audience Taxonomy 1.1 node IDs — privacy-safe by construction.
Run any URL through the live demo and get demographics, INT.* interests and PI.* purchase-intent codes back — every value crosswalked to Audience Taxonomy 1.1.
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