Finance is the category where identifier-based targeting hurts twice: privacy rules treat financial characteristics as sensitive, and the affluent, mobile-first audiences you want skew to Safari and iOS, where third-party cookies are blocked. The Cookieless Audience database offers a different instrument — 12 PI.finance_insurance.* purchase-intent segments, six personal-finance interest codes and full demographics, pre-computed for 102 million domains. You plan against the readership of financial content, never against any individual’s financial data: no PII exists anywhere in the pipeline.
Financial services marketing runs into a structural squeeze. On one side, privacy regulation and internal compliance teams increasingly restrict targeting built on individual-level financial attributes — credit status, investment behaviour, insurance history. On the other, the audiences with money to move skew heavily to Apple devices and privacy-protective browsers: cookies remain on Chrome, but Safari, Firefox and iOS block third-party cookies by default, and roughly 40%+ of traffic is cookieless today. The in-market investor reading a portfolio-strategy article on an iPhone is invisible to a cookie-based in-market segment.
Domain-level audience data resolves both constraints at once. Instead of profiling people, it profiles properties: a retirement-planning site has a readership with known age, income and intent characteristics that hold regardless of who is visiting or on what device. Targeting the site’s readership requires no individual data at all — a materially easier conversation with a compliance team than any identifier-based alternative.
Every attribute is drawn from fixed, versioned v1.0 vocabularies aligned with IAB Audience Taxonomy 1.1, with a banded confidence value per attribute — so media plans are documented in codes that survive audit, quarter after quarter.
PI.finance_insurance.stocks_and_investmentsINT.personal_finance.personal_investingupper_middle / high / affluent35_44 / 45_54highFive enumerated values define the plan — no individual data, no vendor black box. Run them over the domain file, or per-URL via the real-time API.
The finance-specific codes sit inside the full model — demographics, 29 interest groups, 34 intent groups, B2B firmographics — documented on the audience segmentation taxonomy page.
12 segments under PI.finance_insurance: banking, credit_cards, insurance, mortgage_lenders_and_brokers, stocks_and_investments, retirement_planning, tax_preparation_services, student_financial_aid and more.
Six codes under INT.personal_finance — personal_investing, retirement_planning, personal_debt, personal_taxes, insurance, frugal_living — separating research-stage readers from in-market shoppers.
6 income bands (low to affluent), 8 age brackets, 7 education levels, home ownership and 14 life stages — new_parent, established_professional, retiree — the axes financial products are actually built around.
Attributes describe a domain’s readership in aggregate. No individual is scored, no PII is processed, and every value comes from a published enumerated list your compliance team can review before a single dollar is spent.
For fintechs selling to businesses: firmographics on LinkedIn-standard bands, including a finance job-function code, seniority from c_suite down, and company-size bands from 1_10 to 5001_plus.
Each attribute carries low / medium / high confidence. Regulated advertisers typically plan at a high floor for product campaigns and relax to medium for upper-funnel reach.
The output at every step is a readable list of domains and enumerated codes — documentation your media, legal and compliance stakeholders can all read the same way.
State the product’s audience in vocabulary terms: life stage, income band, interest and intent segment.
“First-time homebuyers” becomes PI.finance_insurance.mortgage_lenders_and_brokers + newlywed_couple/family_young_children.
Apply the codes across 102M domains with a high confidence floor; exclude categories your suitability policy rules out.
Sanity-check the ranked list, record the exact filter codes — the plan’s audit trail is the query itself.
Feed the list into allow-lists, PMP/Deal-ID curation, direct buys and sponsorships across cookieless inventory.
A commission-free investing app wants professional 25–44s who read investing content and are actively comparing brokerages — on inventory its compliance team will sign off. Here is that plan as a database filter.
| Field | Filter value (code) | Reads as |
|---|---|---|
| audience_type | b2c | Consumer-facing readership |
| interest | INT.personal_finance.personal_investing | Personal investing content |
| purchase intent | PI.finance_insurance.stocks_and_investments | In market for investment products |
| age_bracket | 25_34 or 35_44 | Core acquisition demographic |
| income_level | middle to high | Investable income, mass-affluent |
| life_stage | young_professional or established_professional | Working professionals |
| confidence | high | Strictest evidence band only |
| Cookie / ID in-market segments | Keyword contextual targeting | Domain-level audience profiles (this dataset) | |
|---|---|---|---|
| Individual data involved | Yes — behavioural profiles, often financially sensitive | No | No — readership-level attributes only, zero PII |
| Cookieless reach (Safari, iOS, Firefox) | Largely blind | Covered | Covered — profile is a property of the domain |
| Audience precision | User-level but shrinking and opaque | Page topic only — no demographics or income | Intent × interest × income × life stage per domain |
| Compliance review | Vendor-dependent, hard to document | Simple but coarse | Published enumerated vocabularies; filter codes are the audit trail |
| Reproducibility | Segment definitions vary by vendor | Keyword lists drift | Fixed v1.0 codes — same filter, same meaning, every quarter |
No. Every attribute describes the aggregate readership of a domain — for example, that a brokerage-comparison site concentrates readers with PI.finance_insurance.stocks_and_investments intent in upper income bands. No individual is profiled, no financial characteristics are attached to any person, and no PII exists anywhere in the pipeline. That structural property is what makes the approach straightforward to present to compliance and privacy teams.
The PI.finance_insurance group carries 12 segments: banking, credit cards, insurance, mortgage lenders and brokers, stocks and investments, retirement planning, tax preparation services, student financial aid, credit and debt repair/credit reporting, payday and emergency loans, accountants and bookkeepers. Six INT.personal_finance interest codes cover the research layer above them. The full list is on the taxonomy page.
Affluent and professional audiences over-index on Apple devices and privacy-protective browsers, where third-party cookies are blocked — cookies remain on Chrome, but roughly 40%+ of traffic is cookieless today. Cookie-based in-market finance segments therefore systematically under-represent the highest-value prospects. Domain-level profiles describe Safari and iOS-heavy financial content sites with the same fields and confidence bands as everything else.
The top-100k domain file with full audience attributes is $490 one-time ($190/quarter refresh); the top-1M file is $1,990 one-time ($590/quarter) with instant card checkout and immediate download. Vertical slices — e.g. finance-heavy domains for one market — run $190–$490, and larger cuts up to the full 102M corpus, custom enrichment or feeds are quoted individually. API plans (e.g. Pro, $99/month for 10,000 credits) cover per-URL lookups. See pricing.
Reach the traffic cookie-based finance segments never see.
Turn a compliant domain list into curated Deal-ID packages.
Plan against 1,667 deterministic personas across 102M domains.
How agency teams run the same workflow across client categories.
Filter live intent data in the demo dashboard, or download the top-1M file and hand your compliance team a plan written in published codes.
Open the audience demo See database pricing