What defines an exposure zone around a digital screen?

An exposure zone is a virtual geographic boundary drawn around the physical display. You define this polygon to match the exact area where a pedestrian or driver has a clear line of sight to the creative. Devices entering this specific area register as exposed in your attribution file.

How do dwell-time filters improve conversion tracking?

Dwell-time filters remove false positives from your conversion data. You set a minimum time threshold for devices inside the store polygon. This ensures you count actual shoppers who spend five minutes inside the building while filtering out pedestrians walking past the front door on the sidewalk.

Why do you need a control group for location studies?

A control group isolates the actual impact of your advertising. You track a matching audience that never entered the viewability zones of your campaign. Comparing their natural visit rate against your exposed audience reveals the true incremental lift generated by your media spend.

How do you measure attribution for static billboards?

You apply the same mobile location methodology to printed boards as you do to digital screens. You draw a geofence around the static placement and collect the mobile advertising identifiers of the passing traffic continuously throughout the duration of the physical lease.

Can you track conversions for long sales cycles?

You set custom attribution windows based on the product category. A fast-food campaign requires a tight window of a few days to track immediate visits. A luxury automotive campaign uses a ninety-day window to account for the extended research period before a dealership visit.

How does foot traffic attribution work for billboards?

Sneh Dhruv · Updated October 23, 2026

Foot traffic attribution works by capturing the mobile advertising IDs of people who pass a billboard and matching them against the devices that later enter a geofenced retail location. Trillboards lets you read measured attention, dwell and exposure, published with sample sizes and data windows, mapping exposed audiences to 287,009 store locations in the store-visit panel.

The numbers

Measure	Figure	Sample	Data window
Bluetooth device IDs read a day	35,097,192	28 days	Sep 13 – Oct 10, 2026
Named place polygons (Overture) in the place layer	2,808,838	2,808,838 places	Oct 13, 2026
Median exposure interval at a board	5.9	9,928,376 exposure intervals	Jun 28 – Sep 25, 2026
Share of camera-judged legible passes with at least one second facing the board	28.1%	846,637 judged passes	Jun 28 – Sep 25, 2026
Third-party board faces the geometry engine measures against	149,600	149,600 faces	Oct 13, 2026

Capturing the exposure event

Foot traffic attribution connects real-world ad exposure to subsequent brick-and-mortar visits. The process begins at the billboard. Historically, out-of-home currency models relied on raw traffic counts and mobile-panel estimates to generate an Opportunity to See metric. These models assume that a certain percentage of passing traffic notices the creative. Today, you capture the actual device identifiers of the people in the exposure zone.

To log an exposure, vendors draw a geofence or viewability polygon around the physical screen. When a consumer banking client runs a campaign, you collect the mobile advertising IDs of pedestrians and motorists who enter that zone while the ad plays. You log the timestamp and the device ID. This creates your exposed audience pool. You build your campaign strategy around these deterministic signals. When you plan a media buy for a regional hospital network, you map the exact screens that intercept your target demographic on their commute. You base your exposure files on massive signal ingestion, capturing 35,097,192 Bluetooth device IDs read a day across the network.

Mapping the brick-and-mortar destination

Once you build the exposed audience pool, you define the conversion zone. This requires precise GPS polygon mapping around the target locations. If you run a campaign for a national quick-service restaurant, you draw virtual boundaries around their physical stores.

You apply dwell-time filters to these polygons to separate actual customers from random sidewalk passersby. A device must remain inside the polygon for two to five minutes to count as a visit. The accuracy of your attribution depends entirely on the quality of your place data. A sloppy radius captures the coffee shop next door, ruining your conversion metrics. You rely on strict geometry, building campaigns against 2,808,838 named place polygons in the place layer to ensure you track visits to the exact building.

Isolating incremental lift against a control group

Attribution proves causality. You need to know if the billboard drove the visit. To prove this, you compare the visit rate of your exposed devices against a statistically matched, unexposed control group.

You build a control group of devices that share the same behavioral profiles and geographic baselines as your exposed group but never entered the billboard's viewability polygon. You look at the baseline foot traffic of both groups before the campaign launches. You track their behavior throughout the flight and for a defined window after the media stops running. This rigorous methodology isolates the incremental lift. This allows you to calculate the Cost Per Incremental Visit. You abandon modeled impressions. You base your return on ad spend on measured exposure. You know exactly how long people spend in front of the inventory, recording 5.9 seconds, the median exposure interval at a board, to ensure the exposed group had time to read the message.

Moving from traffic counts to measured attention

Standard foot traffic attribution assumes everyone who enters the geofence saw the ad. You know that assumption inflates the impact of the campaign. A driver looking at their phone or facing the opposite direction registers as an exposure in a standard mobile-location study.

To calculate true attribution, you move past raw traffic counts and verify who actually looked at the screen. You filter the exposure pool down to the people who faced the board. The drop-off from a legible pass to an actual view is steep. Measurement data shows that 28.1% of camera-judged legible passes had a passenger facing the board for at least one second. When you tie foot traffic attribution to measured attention, you give your client a definitive cost per acquisition based on verified views.

Integrating attribution into programmatic buys

Independent offline intelligence and attribution come from specialized third-party location firms. You integrate these services directly when planning campaigns through your demand-side platform. When you set up a campaign for a hotel chain, you select the attribution vendor in the platform interface and append their tracking pixels to your creative.

You factor the cost of the attribution study into your total media budget. Vendors bill these studies as flat campaign fees or small CPM surcharges on top of the media cost. Standard billboard CPMs average two to nine dollars. Digital programmatic CPMs typically range from twelve to thirty dollars. You evaluate the performance of 149,600 third-party board faces measured across the ecosystem, matching the bid requests carrying the screen's IP address, location and live audience measurements against your attribution vendor's device graph. This integration allows you to optimize your bidding strategy in mid-flight, shifting spend toward the screens driving the highest incremental lift.

Sources

Related on Trillboards

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Attention, dwell and exposure are measured and published with their sample sizes and data windows. Take the deal ID into your own DSP and buy the same audience as an OpenRTB 2.6 private marketplace deal. Plan it on live inventory to see the screens, weekly views and effective CPM for the audience you pick.

Frequently asked questions

What defines an exposure zone around a digital screen?: An exposure zone is a virtual geographic boundary drawn around the physical display. You define this polygon to match the exact area where a pedestrian or driver has a clear line of sight to the creative. Devices entering this specific area register as exposed in your attribution file.
How do dwell-time filters improve conversion tracking?: Dwell-time filters remove false positives from your conversion data. You set a minimum time threshold for devices inside the store polygon. This ensures you count actual shoppers who spend five minutes inside the building while filtering out pedestrians walking past the front door on the sidewalk.
Why do you need a control group for location studies?: A control group isolates the actual impact of your advertising. You track a matching audience that never entered the viewability zones of your campaign. Comparing their natural visit rate against your exposed audience reveals the true incremental lift generated by your media spend.
How do you measure attribution for static billboards?: You apply the same mobile location methodology to printed boards as you do to digital screens. You draw a geofence around the static placement and collect the mobile advertising identifiers of the passing traffic continuously throughout the duration of the physical lease.
Can you track conversions for long sales cycles?: You set custom attribution windows based on the product category. A fast-food campaign requires a tight window of a few days to track immediate visits. A luxury automotive campaign uses a ninety-day window to account for the extended research period before a dealership visit.

Plan it

Open the planner in the planner to see the screens, the weekly views and the price before you book, or browse the audience catalogue.