How much foot traffic lift does DOOH advertising drive?

Digital out-of-home advertising drives a median foot traffic lift of 20 percent for in-person outcomes, reaching up to 25 percent for retail sectors. You can read measured attention, dwell and exposure on Trillboards, drawing data from 6,828 screens counting nearby phones by radio to verify how many exposed consumers enter your locations.

The numbers

Measure	Figure	Sample	Data window
Named place polygons (Overture) in the place layer	2,808,838	2,808,838 places	Oct 13, 2026
Store locations visited in the store-visit panel	287,009	287,009 stores	Sep 13 – Oct 10, 2026
Bluetooth device IDs read a day	35,097,192	28 days	Sep 13 – Oct 10, 2026
Screens sensing by camera or radio	7,289	7,289 screens	Jul 13 – Oct 10, 2026

Benchmarking Physical Campaign Performance

Digital out-of-home advertising delivers a median foot traffic lift of 20 percent for in-person outcomes. This performance doubles the 10 percent lift seen in connected TV and broadcast television campaigns. Physical-first sectors see even higher returns. A quick-service restaurant chain running a regional campaign achieves up to a 25 percent median foot traffic lift when placing ads on retail and roadside screens. You set your budget and target specific venues where your buyers spend their time, knowing the baseline performance expectations for the medium.

Audit screen types in your target zip codes. Buyers secure these placements through demand-side platforms using a cost per thousand model. Programmatic rates range between $5 and $15. Roadside digital averages $8, retail environments average $10, and high-impact airport screens rise to $14 or more. Managed-service campaigns require minimum spends of $10,000 to $50,000, but self-serve platforms let you start with smaller budgets and scale up as you prove foot traffic lift.

You build your media plan around these benchmarks, allocating spend to the screens that drive the most efficient cost per visit. Pull performance reports from your demand-side platform after the first week. If retail screens drive a lower cost per visit, you immediately shift your remaining budget to those specific venue types. This active pacing prevents wasted ad spend on underperforming inventory.

Moving Past Impression Multipliers

The out-of-home category traditionally relies on modeled impressions to estimate reach. Measurement bodies provide traffic counts and mobile panels to build an impression multiplier for each screen. This tells you the average number of people likely passing a venue at a given hour. The Interactive Advertising Bureau defines viewability metrics alongside standards from the Media Rating Council, ensuring a consistent framework for these estimates. You buy media against these models, projecting your total reach based on historical averages.

Historical averages often miss sudden changes in weather or local traffic routing. Modern campaigns pull live data from the edge to move beyond these estimates. You verify your exact physical reach using 7,289 screens sensing by camera or radio. Instead of paying for estimated traffic, you read the exact number of people walking past the display while your creative played.

Filter inventory sources to prioritize screens equipped with real-time sensor technology. Select inventory partners offering deterministic audience validation during campaign setup. This shift from modeled traffic counts to actual device and face detection gives your data team a concrete foundation for attribution models. Knowing exactly how many people were present during the ad play eliminates guesswork from top-of-funnel reach calculations. If a live sensor reports a sudden foot traffic spike, programmatic rules can automatically increase your bid modifier to capture that premium audience. You stop relying on static spreadsheets and start making buying decisions based on the actual physical reality of the street.

Isolating Lift With Device Matching

To prove a campaign drove visits, you isolate the lift by comparing exposed consumers against unexposed control groups. You match the audience at the screen to the audience walking through your doors. The system captures the mobile signals of shoppers exposed to your ads. You see 35,097,192 Bluetooth device IDs read a day, providing the raw identifiers needed to track downstream actions. You build an audience of exposed devices and match them against an unexposed control group with similar demographic and behavioral profiles.

Ensure your attribution partner has a robust methodology for selecting this control group. Require a control group visiting similar neighborhoods at comparable times. You map these exposed devices to physical retail environments. The infrastructure maps 2,808,838 named place polygons in the place layer, letting you define the exact boundary of your quick-service restaurant locations.

Carefully draw these polygons to exclude adjacent sidewalks or shared parking lots. If your polygon is too wide, you capture false visits and ruin data integrity. When a device seen at the screen appears inside your polygon, you log a verified visit. You compare the visit rate of this exposed group against the matched control group to calculate the true foot traffic lift generated by your creative. This deterministic matching proves the value of your physical media buy to your stakeholders. Export this matched audience data to retarget users across mobile channels, building a cohesive strategy.

Structuring Your Measurement Window

Visit-lift studies require a defined observation period to capture the full impact of an exposure. Consumers rarely walk straight from a screen to a store. You track devices over days and weeks to account for delayed visits. The measurement framework relies on 287,009 store locations in the store-visit panel to verify when an exposed device finally crosses the threshold. You define a lookback window that matches the typical purchase cycle of your product, ensuring you capture every visit driven by the campaign.

Customize this window based on the advertised product. Coffee requires a short window, while luxury retail needs a longer period. You set up your demand-side platform to log bid requests and match them against store visits over a 14-day or 30-day window. The bid requests carry the screen location and live audience measurements, giving you the exact timestamp of the exposure.

Cross-reference this timestamp with the store entry time to calculate the lag between seeing the ad and buying the meal. This data lets you optimize your creative rotation and dayparting strategy to maximize the foot traffic lift across your entire network. If data shows morning exposures lead to afternoon visits, weight your bidding toward morning commutes. Shift your budget toward hours generating the fastest conversions, improving overall return on ad spend. Test different creative messages. Tracking which creative yields the shortest lag time before a visit helps you refine messaging and deploy effective assets.

Sources

• oaaa.org
• iab.com
• ariadne.inc
• iab.com
• oohguide.com

Related on Trillboards

• How can retailers measure foot traffic attribution from in-store DOOH and mobile?
• Which programmatic DOOH providers offer accurate conversion attribution?
• Where can you get real-world attention data for ad creatives?
• How does camera-based audience measurement work for out-of-home?
• Who are the DOOH audience measurement providers?
• Every measurement and data answer
• Sonic Drive-In Visitors: price and index
• Published CPM floors for every audience
• Every audience we sell
• Screens and audiences by city

Plan this on Trillboards

Attention, dwell and exposure are measured and published with their sample sizes and data windows. Plan it on live inventory to see the screens, weekly views and effective CPM for the audience you pick.