Most publisher prospecting is a volume game with a low hit rate. Audience-match prospecting inverts the order: codify who your readers are in fixed, IAB Audience Taxonomy 1.1-aligned attributes, then derive which advertiser categories that profile is evidence for and rank your pipeline by fit.
Sales teams don’t lack advertiser names — directories and ad observation supply plenty. What they lack is a defensible reason why each name should buy this audience.
A generic outreach list produces meetings with buyers whose category never fit your readership. The mismatch shows up in the RFP response or the post-campaign report — the two most expensive places to learn it.
Agencies triage inbound by whether the seller shows category fit in the first paragraph. An opener built on a coded intent segment survives triage; adjectives do not.
The buy side has long used data to choose inventory. Audience-match prospecting gives the sell side the mirror image: a neutral dataset that says which demand your inventory is evidence for.
Four steps, run quarterly against each database refresh. The output is a ranked account list where every row carries its own evidence.
Pull your domains from the database and profile key sections through the real-time API. Get coded demographics, INT.* interests, PI.* purchase-intent segments, personas and B2B firmographics.
Each high-confidence PI.* segment names a demand category. PI.auto_ownership.new_vehicles points at vehicle OEMs; PI.finance_insurance.insurance at insurers. The 34 intent groups give the map its structure.
Cross the category map with your existing account sources — agency rosters, ad observation, CRM history. Tier prospects by matching segment count, confidence and persona overlap. Fit becomes a sortable field.
Lead outreach with the match, then carry the same data into the meeting as a pitch deck. Next quarter’s refresh shows which categories strengthened — your trigger list for re-engagement.
A mid-size automotive publisher profiles its domain and gets the coded record on the right — every value from the fixed v1.0 vocabulary. The sales lead reads it as a demand map.
PI.auto_ownership.new_vehicles and PI.finance_insurance.insurance identifies tier-one prospect categories: vehicle OEMs, dealer groups and auto insurers.INT.automotive.auto_buying_and_sellingAuto Technology INT.automotive.auto_technologyPI.auto_ownership.new_vehiclesInsurance PI.finance_insurance.insuranceAudience match doesn’t replace your existing sources — it is the ranking layer that makes them convert.
| Prospecting approach | Evidence you can show | Works for niche titles | Ongoing effort | Best role |
|---|---|---|---|---|
| Directory / list outbound | None — volume play | Yes | High, low conversion | Raw account discovery |
| Ad observation on rival sites | “They buy our competitor” | Yes | Manual, ongoing | Timing and account names |
| Panel-based audience rank | Reach ranking, big sites only | Sample too thin | Subscription | Top-tier reach claims |
| Audience-match prospecting | Coded intent segments + personas, confidence disclosed | Yes — any public content footprint | Quarterly refresh, automated join | Ranking the pipeline and opening the pitch |
Turn the audience match into the deck you bring to the meeting you just booked.
The same matching logic, run from the agency side of the table.
B2B firmographics for account-based sales and marketing workflows.
Know where your audience beats the competitive set before you claim it.
The full publisher playbook: monetization, curation and sales enablement.
All 34 intent groups and 283 segments your prospect map is built from.
No — it tells you something more durable: which advertiser categories your audience is evidence for, expressed in coded purchase-intent segments and personas. You combine that with the account sources you already use (agency directories, ad observation, your CRM) to name the companies. The audience-match layer is what ranks those accounts and gives your outreach an evidence-based opening line.
It works especially well there. Content-inferred profiling covers any domain with a public content footprint at the same attribute depth — 8 age brackets, 6 income bands, 285 sub-interests, 283 purchase-intent segments, 1,667 personas, plus B2B firmographics in LinkedIn-standard bands — so a specialist site gets the same quality of audience evidence as a top-100 property, with a banded confidence value on every attribute.
Yes. The dataset includes B2B firmographic attributes — company-size bands, seniority levels from individual contributor to C-suite, and 27 job functions — inferred from content, with no PII. A developer-focused publisher, for example, can show that its audience profile matches engineering and IT functions at mid-size companies, which is exactly the evidence a B2B software advertiser’s agency needs to justify a test budget.
A single vertical or country slice at $190–$490 is usually enough to profile your properties and the domains your target advertisers already buy. The top 100k domains with full attributes cost $490 one-time ($190/quarter refresh); the top 1M domains cost $1,990 one-time ($590/quarter) with instant checkout. Page-level profiling of your key sections uses standard API plans such as Pro at $99/month for 10,000 credits. Larger corpora and custom feeds are quoted, with custom licensing from $15,000 per year. Details on the pricing page.
Profile your properties in the demo, see which purchase-intent segments your content is evidence for, and license the slice that covers your vertical.