Purchase intent data reveals who is actively considering a purchase right now — comparing models, checking prices, reading reviews. This guide covers the traditional sources, plus a cookieless alternative: 283 intent segments in 34 groups inferred from page content, no cookies or IDs required.
Intent signals are the observable traces of an in-market window: searches, comparisons, spec-sheet downloads and review-reading binges that cluster tightly around a purchase decision and then stop.
A person can be interested in cars for thirty years but in-market for only ninety days. Interest describes a durable affinity useful for reach and brand planning. Intent describes a transient shopping window useful for conversion.
An ad for mortgage rates shown to an active comparison-shopper performs on a different order from the same ad shown to a general finance audience. In-market segments have historically commanded the highest CPMs of any third-party data category.
Intent can describe an individual (“this user shops for SUVs”), an account (“this company researches data warehouses”), or an audience (“this page's readers shop for vehicles”). Each has a different privacy footprint and appropriate use.
The sourcing challenge: most intent data was built by observing individuals — inheriting cookie dependence, consent requirements, and coverage loss on Safari, Firefox and iOS (~40%+ of traffic). This guide, part of our cookieless audience segmentation hub, explores a content-inferred alternative.
Five source families account for nearly all commercial intent data. Each observes a different behavior, at a different resolution, with different blind spots.
Queries are the purest intent signal: “best 7-seater EV 2026 price” is a person announcing their in-market state in their own words. Search engines monetize this through search ads, and query-derived audiences power much of in-market segmentation inside walled gardens.
Limit: raw signal stays inside the capturing platforms; outside, buyers get pre-packaged segments with little transparency.Product views, cart adds, configurator sessions, pricing-page visits — behavioral events collected on a marketer's own properties (first-party) or across the web via third-party cookies. This is the signal behind retargeting and most “in-market” data-marketplace segments.
Limit: cross-site collection is blocked by default on Safari, Firefox and iOS, and consent-gated everywhere else.Opted-in panels share browsing, search or purchase activity in exchange for compensation. Panels observe deep, longitudinal behavior for a small consented population, then project onto the broader market. Widely used for measurement and calibrating other datasets.
Limit: panel sizes are small, projection introduces modeling error, and niche categories may have too few in-panel buyers.Card networks, retailers, receipt-scanning apps and e-commerce platforms see actual purchases — the ground truth every other source approximates. Transaction-derived segments (“bought baby products in the last 90 days”) are strong predictors of adjacent purchases.
Limit: inherently backward-looking, and among the most sensitive personal data categories under privacy law.B2B vendors watch for content-consumption spikes at the account level: when an unusual number of readers at one company start consuming content on a topic, that account is flagged as “surging” and pushed to sales and ABM teams.
Limit: IP-to-company resolution is noisy (VPNs, remote work), topic taxonomies are proprietary, and coverage is partial.The newest family: instead of observing people, analyze the page. What a URL is about reveals the likely in-market state of its readership — the intent analogue of contextual targeting, upgraded from “what is this page about” to “what is this audience shopping for.”
Limit: it describes audiences, not individuals — a constraint treated honestly in the limitations section below.A mortgage-calculator page is read by mortgage shoppers. A “best CRM for small business 2026” comparison is read by CRM buyers. A hotel-review roundup for Lisbon is read by people planning travel. The content selects the audience — no visitor observation needed.
Content-inferred intent systematizes this. A model reads a page's topic, angle and funnel position, then outputs purchase-intent segments from a controlled vocabulary with banded confidence (low / medium / high). The unit of analysis is the page or domain, never the person — making the approach privacy-safe by construction.
In our implementation, intent is one of five signal families returned by the audience segmentation API for any URL in real time, and pre-computed across 102M domains for planning and curation at scale. Intent is only asserted where content supports it — a mortgage calculator earns high-confidence intent; a general news homepage earns none.
Intent codes are stable identifiers (PI.travel.hotels_and_resorts), so segments survive model updates and multi-vendor pipelines. Browse the full branch on the audience segmentation taxonomy page.
Neither approach dominates the other on every axis — they answer different questions.
| Dimension | Panel / behavioral intent (user-observed) | Content-inferred intent (page-observed) |
|---|---|---|
| Data basis | Observed actions of individuals or accounts: queries, browsing events, transactions, panel activity, IP-resolved content consumption. | The page itself: topic, funnel position, comparison structure, price signals. The audience is inferred from what the content selects for. |
| Privacy | Processes personal data; requires consent management, contracts and deletion workflows. | No personal data processed at any stage; privacy-safe by construction. No consent dependency. |
| Coverage | Bounded by tracking footprint: collapses where third-party cookies are blocked (~40%+ of traffic), thins with consent opt-outs. | Works on 100% of pages and traffic, including cookieless environments, new visitors and unconsented sessions. |
| Latency | Segments built from accumulated observations; membership often lags behavior by hours to weeks. | Evaluated at request time per URL, or from the pre-computed domain dataset. New pages carry signals immediately. |
| Granularity | Individual or account level — genuinely per-person when data is good (core strength and privacy cost). | Audience level: the aggregate in-market skew of a page's readership. Precise about pages, silent about persons. |
| Best use | Retargeting, closed-loop measurement, sales triggers on named accounts, suppression of existing customers. | Prospecting reach, in-market contextual targeting, inventory curation, seller-defined audiences, account scoring. |
Sophisticated buyers run both: user-observed intent where consented first-party relationships exist, and content-inferred intent for the growing share of cookieless traffic. The deeper comparison is covered in contextual vs behavioral targeting.
Intent data is only as useful as the category system behind it. Free-text model output cannot be traded, joined or audited — controlled codes can.
Every intent value comes from 34 tier-1 groups containing 283 tier-2 in-market segments, each with a stable PI.* code and a human-readable label. Versioned (v1.0) and aligned with the IAB Audience Taxonomy 1.1, so segments slot directly into industry pipelines.
Groups span consumer and business purchasing: Automotive, Travel, Finance, Software, Consumer Electronics and thirty more. Segments are where activation happens — not “Travel” but Cruise Travel, Hotels and Resorts, Travel Insurance. Browse the full structure on the taxonomy page.
PI.auto_ownership Automotive Ownership
PI.travel Travel and Tourism
PI.finance_insurance Finance and Insurance
PI.software Software
PI.consumer_electronics Consumer Electronics
PI.real_estate Real Estate
PI.education_careers Education and Careers
PI.business_industrial Business and Industrial
PI.health_medical Health and Medical Services
PI.home_garden_services Home and Garden Services
PI.family_parenting Family and Parenting
PI.clothing_accessories Clothing and Accessories
+ 22 more groups · 283 segments total
The same PI.* signals feed very different workflows depending on who is holding them.
Buy the pages whose readership is in-market rather than the users a cookie once claimed were. A travel-insurance campaign runs across every page scoring Travel Insurance or Cruise Travel intent at medium-plus confidence — reaching in-market readers on 100% of traffic.
Enrich a target-account list with the intent profiles of trade content, comparison pages and documentation their teams consume. Weight Software or Business and Industrial intent into ABM prioritization — a content-side complement to surge-based intent vendors.
SSPs package inventory into Deal IDs defined by intent: “auto in-market, high confidence, brand-safe” becomes a curated PMP assembled from the 102M-domain dataset plus per-URL scoring — a sell-side data product with a full derivation trail.
Publishers attach PI.* signals to their own inventory and pass them in the bid request as Seller Defined Audiences — IAB-aligned intent codes make the segments legible to any DSP that speaks the standard.
Sales teams use intent-scored domain lists as prospecting filters: every domain whose audience shows Logistics and Delivery intent is a lead list for a freight platform; every site with Retirement Planning intent is one for a wealth-management tool.
Intent tells you what an audience is shopping for; personas tell you who they are. Combine PI.* segments with the 1,667-persona layer — covered in the companion guide to audience personas in advertising — for plans like “First-Time Homebuyer + Mortgage intent.”
Segments you can defend to a buyer, a lawyer or an auditor are worth more than segments you cannot.
A high-confidence New Vehicles signal means a page's readership skews toward car shoppers — not that any specific visitor is one. For media buying this is the right resolution (impressions are aggregate), but it cannot replace user-level data for suppressing recent purchasers.
Inferred intent carries low / medium / high confidence, not a decimal probability. A mortgage calculator is high-band; a general personal-finance column is low-band or carries no intent at all. Set thresholds per use case: strict for guaranteed deals, permissive for prospecting.
No intent product — behavioral or contextual — predicts that a purchase will occur. It identifies where in-market attention concentrates. Conversion depends on the offer, the creative, the price and the moment; intent data improves the odds, nothing more.
Thin or ambiguous pages yield weak or absent signals — by design, the system returns nothing rather than guessing. Per-URL scoring handles content changes in real time; domain-level profiles refresh on the dataset's update cycle.
A single API call against an electric-SUV comparison review. Coded PI.* values on the left; rendered labels for planning on the right.
POST /api/audience/segment.php
{ "query": "https://autoreviews.example/2026-electric-suv-comparison" }
// response — purchase_intent family (other families omitted)
{
"purchase_intent": [
{ "code": "PI.auto_ownership.new_vehicles",
"label": "New Vehicles",
"confidence": "high" },
{ "code": "PI.finance_insurance.insurance",
"label": "Insurance",
"confidence": "medium" },
{ "code": "PI.auto_products.automotive_parts_and_accessories",
"label": "Automotive Parts and Accessories",
"confidence": "low" }
],
"vocab_version": "1.0"
}
The review compares purchase prices and trims (high-band New Vehicles), discusses EV insurance costs (medium-band Insurance), and mentions accessories in passing (low-band). Nothing else is asserted — no demographic guess is smuggled in as intent.
An auto OEM buys the high band; an insurer tests the medium band; an accessories retailer probably passes. Demographics, life stage and personas are carried as separate families with their own confidence bands.
Paste a page into the live demo and watch the PI.* branch light up — in-market segments with confidence bands, alongside demographics, life stage, personas and B2B signals. No signup, no cookies, no tracking.