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Digital Out-of-Home

DOOH Venue-Content Matching: Make Screens, Content Feeds and Audiences Speak One Language

DOOH planning describes audiences by venue; web data describes them by domain. Cookieless Audience bridges the two with fixed, IAB Audience Taxonomy 1.1-aligned codes — 285 sub-interests, 283 purchase-intent segments and nine demographic dimensions — so you can vet syndicated feeds, keep screen content coherent with the venue audience, and build companion web domain lists. All at the domain and content level, with no identifiers involved.

102Mdomains with audience attributes
285sub-interests (INT.*)
283purchase-intent segments (PI.*)
0identifiers or PII required
IAB Audience Taxonomy 1.1 aligned Fixed, versioned vocabularies (v1.0) Domain & page-level classification Banded confidence per attribute No PII in the pipeline
The matching problem

Venue audiences and web content have never shared a vocabulary

Most DOOH screens syndicate content from web publishers — news tickers, sports highlights, recipes, finance headlines. When content and venue audience are mismatched, both engagement and ad context fail quietly.

Venue and content speak different languages

Venue owners describe audiences in prose (“young urban professionals, morning commute”). Content partners describe themselves in brand terms. Nothing forces the two descriptions to be comparable — unless both are expressed in the same coded taxonomy.

Mismatches fail silently

A wealth-management spot after celebrity gossip in an airport lounge, or esports clips in a retiree waiting room — when content doesn't match the venue audience, both jobs underperform without anyone noticing.

Fixed codes make matching mechanical

Every attribute uses a fixed vocabulary — interests as INT.* codes, purchase intent as PI.* codes, demographics as enumerated bands — so a venue profile and a content feed can be compared field by field and the overlap scored.

Workflow

How the match is made

Describe the venue in coded attributes, profile each content feed's source domain, score the overlap, and extend the same codes to a companion web domain list.

Four-step matching flow

Describe the venue in codesA gym network's audience becomes 18_24/25_34, urban, INT.healthy_living.fitness_and_exercise.
Profile the content sourcesLook up each feed partner's domain in the database; audience attributes return in the same vocabulary.
Score the overlapCount matching codes, weighted by banded confidence. High-overlap feeds fill the loop; low-overlap feeds get replaced.
Extend to the open webThe same code set filters 102M domains into a companion online list, so the DOOH flight and its digital extension target one audience definition.
Venue library

Typical venue types, expressed as audience codes

Illustrative starting profiles — each network tunes its own. Every value below comes from the same enumerated vocabulary in the published taxonomy, so it can be matched mechanically against any domain.

Gyms & fitness studios

Fitness-minded, younger-skewing, urban and suburban members between sets and on cardio floors.

INT.healthy_living.fitness_and_exercisePI.recreation_fitness.gyms_and_health_clubs18_24 / 25_34urban

Airports & transit hubs

Travellers with dwell time: business flyers mid-week, leisure and family travel around holidays.

PI.travel.air_travelPI.travel.business_travelINT.travel.family_travelestablished_professional

Grocery & pharmacy

Household decision-makers in an active buying mindset — the venue where content and intent sit closest.

PI.cpg.edibleINT.food_drink.cookingfamily_with_childrensuburban

Office towers & elevators

Working professionals on repeat exposure cycles; B2B firmographic fields apply here as well as consumer ones.

audience_type: b2bINT.business_financeemployed_full_timec_suite / manager

Clinics & waiting rooms

Longer dwell, broader demographics, high content sensitivity — feed vetting matters more here than anywhere.

INT.healthy_living.wellnessINT.medical_health.medical_servicesmixedconfidence: high only

Bars, QSR & entertainment

Social, evening-skewed audiences where sports and pop-culture feeds carry the room.

INT.sports.soccerINT.pop_culturePI.food_beverage.bars18_24 / 25_34
Where it runs

Two instruments, one vocabulary

The domain database covers 102M domains at once. The real-time API classifies individual URLs — useful when a feed's articles vary more than its masthead. Both are planning and curation tools, not impression-level ad decisioning.

102Mdomains, pre-computed
1,667deterministic personas
29interest groups
3confidence bands per attribute
Vet syndicated content partners Classify individual feed articles Build companion web domain lists Document venue-content fit for advertisers
Worked example

A gym-network screen loop vets three content feeds

Target venue profile: 18_24/25_34, urban, fitness and nutrition interests. Each candidate feed is profiled by its source domain's database record.

AttributeFitness & nutrition publisherGeneral news wireEsports highlights feed
Interests INT.healthy_living.fitness_and_exercise Fitness & Exercise
INT.healthy_living.nutrition Nutrition
INT.news_politics News & Politics INT.video_gaming.esports eSports
Purchase intent PI.sporting_goods.exercise_and_fitness_equipment Exercise & Fitness Equipment
PI.apps.health_and_fitness_apps Health & Fitness Apps
— broad / mixed PI.consumer_electronics.video_games_and_consoles Video Games & Consoles
Age bracket 18_24 / 25_34 35_44 / 45_54 18_24
Urbanicity urban mixed urban
Confidence high high medium
Venue-fit verdict Anchor feed for the loop Short headline slots only Test in evening dayparts
What the data decides — and doesn't. It doesn't know who is standing in front of a screen. It tells you which content sources demonstrably attract the audience the venue already has — the decision a screen-network programmer actually controls.
Method comparison

Ways to decide what belongs on a venue's screens

CriterionEditorial judgement aloneVenue surveys / panelsCoded audience matching
Repeatable across hundreds of venuesNo — depends on the programmerOnly where panels existYes — same codes everywhere
Covers long-tail content partnersSlowlyRarelyYes — 102M-domain coverage
Defensible to advertisersAnecdotalYes, where fieldedYes — documented codes + confidence bands
Cost per additional feed vettedStaff timeHighMarginal — a database lookup
Captures venue nuance (dwell, daypart)YesYesNo — combine with ops knowledge

These methods stack rather than compete: panels and editorial judgement define the venue profile; coded matching scales it across every feed and companion domain.

Keep exploring

Related use cases

FAQ

DOOH venue-content matching, answered

Our data is about websites. How does it apply to physical screens?

Through the content and the plan, not the screen hardware. Most DOOH loops syndicate content from web publishers, and every publisher domain has a database record describing the audience its content attracts. Vetting a feed partner is therefore a domain lookup. Separately, omnichannel plans pair DOOH flights with open-web buys — the same coded segment definition filters the 102M-domain database into that companion list, so both channels target one audience description.

Can this decide in real time which ad plays on a screen?

No, and we don't position it that way. This is planning and curation data: it informs which content feeds you schedule, which venues fit a campaign's audience rationale, and which web domains extend the flight. The real-time API classifies individual URLs on demand — useful for vetting specific articles before they enter a playlist — but neither product performs impression-level ad decisioning.

How precise can a venue profile be?

As precise as the vocabulary: 8 age brackets, 5-point gender skew, 6 income bands, 14 life stages, urbanicity, household composition and employment status on the demographic side; 285 sub-interests and 283 purchase-intent segments on the behavioural side; B2B firmographics for office and business venues. Every value is enumerated in the published taxonomy, and every attribute carries a low / medium / high confidence band.

What's the fastest way for a screen network to start?

List your content partners' source domains and look them up — the demo dashboard lets you try this interactively today. For production, the Top 100k database is $490 one-time and the Top 1M is $1,990 with instant download; vertical slices run $190–$490, and larger corpus licences are quoted via contact. Most networks join the file to their feed-partner list in an afternoon.

Give every screen a reason for what it shows

Profile your content partners in the demo, or license the database and score every feed and companion domain against your venue profiles.

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