How to concretely measure point-of-sale traffic (Drive-to-Store) from a billboard?

Gone are the days when street advertising was seen as a blind expense. Discover how mobile data allows attributing a store visit to an advertising billboard.

For decades, Out-Of-Home (OOH) advertising suffered from an image deficit compared to digital: "It's beautiful for the brand, but impossible to measure". Digital promised precise ROI, down to the click.

Today, thanks to the fusion of Digital OOH (DOOH) and mobile geolocation data, this phrase has become false. At Jour de Chance, we measure the Drive-to-Store impact of a subway billboard with the same rigor as a Facebook retargeting campaign.

The secret tool: Passive Geolocation Data (SDK)

How do we know someone saw a billboard? By using GPS data (anonymized and GDPR compliant) from millions of smartphones. When a user accepts location sharing to use an app (Weather, GPS, News), their phone emits a highly precise location signal.

The Measurement Protocol in 3 Steps: The "Comptoirs de la Bio" case

To prove the effectiveness of this method, let's take the example of our campaign for "Les Comptoirs de la Bio", who sought a profitable alternative to physical mailbox flyers (now banned in many cities via the 'Oui Pub' system).

Step 1: Isochrone Targeting and creating "exposure zones"

Rather than buying billboards randomly, we defined the catchment area ultra-precisely by calculating isochrones: we selected only urban digital screens (DOOH) located less than 10 minutes (walking or driving) from each store in the network. Each selected screen creates a virtual exposure polygon (geofencing) around it.

Step 2: Identifying the Exposed vs. Non-Exposed

Thanks to our data partners (Adsquare, Singlespot...), we capture the identifier (asynchronous Mobile ID) of smartphones crossing a screen's polygon at the exact moment the "Comptoirs de la Bio" ad is broadcast on that screen.

This creates two cohorts for our statistical study:

The "Exposed" Group: Individuals who passed by the billboard at the right time.
The "Control" Group (Non-exposed): Individuals with the same demographic profile and travel habits, but who did not encounter the billboard.

Step 3: In-Store Counting (Walk-in Rate)

The final step is to physically geofence the "Comptoirs de la Bio" supermarkets. If the GPS signal of a mobile identified in Step 2 (Exposed or Control) is detected inside the supermarket within the following 14 days, it is counted as a visit.

The Result: Drive-to-Store Uplift

The magic of the algorithm isn't just counting total visits, but the Uplift (Incremental Visits). If the Control group generated 100 "organic" visits, and the Exposed group generated 145 visits, we can scientifically state that the billboard campaign generated a +45% Uplift.

🎯 The KPIs we deliver at the end of a campaign

CPV (Cost Per Incremental Visit): How much each new person pushed into the store by the billboard cost.
In-store Audience Profile: Average travel time, preferred visiting days to adapt future creatives on the billboards.

Forget the perception of your grandfather's advertising. Today, we no longer spend "to be seen", we invest to measure a concrete physical trace. The street has become measurable.