Footfall attribution in OOH and DOOH is about measuring how many store visits can be linked to exposure in out‑of‑home advertising. Instead of looking only at reach, views and impressions, footfall attribution shows how digital and classic out‑of‑home (DOOH/OOH) actually influence physical traffic to a store, restaurant, car dealership or other location. This makes out‑of‑home advertising more comparable with digital channels, where clicks, conversions and store visits are tracked in detail – something several players in programmatic and DOOH highlight as a key use case for footfall attribution (see StackAdapt).

What is footfall attribution in out‑of‑home advertising?

Footfall attribution is a method used to:

• Measure the number of store visits that can be linked to an out‑of‑home campaign
• Understand the effect of out‑of‑home on physical traffic
• Compare store traffic driven by different campaigns, formats and environments

In short: instead of settling for knowing how many people may have seen a message, footfall attribution aims to show how many actually take the step into a store after being exposed to out‑of‑home advertising. This definition is aligned with how footfall attribution is described as the link between digital campaigns and physical store visits (see Illumin).

How does footfall attribution work in OOH and DOOH?

In practice, footfall attribution is built on anonymised location data and geographic zones.

Exposure zones around ad units

A zone is defined around selected out‑of‑home units, such as DOOH screens and bus shelter placements, billboards or other street furniture. Electronic devices moving through the zone are counted as potentially exposed. In DOOH measurement, mobile identifiers (MAIDs) and polygon‑based zones are often used to precisely define these exposure areas (see StackAdapt’s overview of DOOH measurement).

Store zones around points of sale

The next step is to define a zone around each store, restaurant or physical location. When the same device later appears in the store zone, a store visit (footfall) is registered. Footfall measurement is usually conducted together with specialised data providers, such as Adsquare. Through the DSPs and platforms JCDecaux works with – for example VIOOH, Vistar Media, The Trade Desk, Google Display & Video 360, Adform and others – these solutions can be activated either as studies or always‑on measurement, to track store traffic and optimise DOOH campaigns over time.

Modelling probability

The model does not count every single step, but the probability that the store visit is linked to the campaign. In this way, a share of the increase in footfall can be attributed to OOH/DOOH exposure without identifying individuals. This is consistent with how footfall attribution is usually defined in industry introductions – as a statistical way of linking campaigns to physical visits (Illumin).

This type of location‑based footfall measurement works for both classic OOH and digital DOOH, and is highlighted as a way to compare effects between channels, including DOOH (StackAdapt).

Control groups – the key to reliable footfall measurement

For footfall attribution to provide useful insights, control groups are essential. Without them, it is difficult to distinguish campaign impact from baseline store traffic. Common ways to set up control:

• Areas with and without campaign: Similar areas with OOH/DOOH presence are compared with areas where no campaign is running.
• Exposed vs non‑exposed devices: Location data is used to separate devices that likely passed the ad units from those that did not. The difference in footfall between test and control group becomes a measure of campaign impact. Results are often broken down by:
  • Store or retail chain
  • Region or city
  • Time of day or day of week
  • Type of (D)OOH environment/network

At the same time, we see footfall attribution increasingly packaged directly into DOOH and retail media offerings, where store visits can be activated as a standard KPI in the buying environment (AIDigital describes this shift clearly).

Footfall attribution in prDOOH – from evaluation to optimisation

Footfall attribution works for both traditionally booked OOH networks and programmatic DOOH campaigns (prDOOH). In programmatic buying, the method becomes particularly valuable because results can be used for ongoing optimisation.

Examples of how footfall attribution drives optimisation in prDOOH:

• Screens, time windows and geographic zones that generate more store traffic can be allocated higher budgets during the campaign.
• Exposures that deliver weaker footfall response can be scaled down or replaced.

This is not a theoretical model – platforms such as Factori already show how advertisers optimise DOOH campaigns based on store traffic, visit patterns and time to conversion (see Factori). Programmatic DOOH case studies also demonstrate how brands track store traffic where campaigns are live and use the insights to steer their investments (see Confirm Media).

In this way, footfall attribution becomes not just something measured after the campaign, but a real‑time optimisation lever to maximise store traffic and campaign return.

How (D)OOH placement affects footfall impact

The placement of out‑of‑home inventory is crucial to how effective footfall becomes.

Close to retail locations and points of sale

FMCG networks such as Eurosize National FMCG are strategically designed to reach consumers at the point of purchase and form part of our national network. With placements in Sweden’s largest cities, units are carefully selected and located close to grocery and convenience stores, where customer flows are highest. A shorter path from screen to store typically increases the probability of a visit.

Select units and reach networks

Select units around priority stores can be combined with broader reach networks to:

• Build demand in the wider market
• Direct more foot traffic to specific locations
• Use creative OOH solutions to strengthen recognition

Various Innovate solutions can act as strong visual landmarks that reinforce brand recognition and increase impact in areas where footfall is measured. In practice, advertisers use footfall data to identify exactly which OOH locations best match the target audience’s movement patterns around stores (Factori provides several examples).

By connecting OOH placements with footfall data, brands can gradually build a map of the locations, formats and messages that drive the most store traffic.

Why footfall attribution matters for advertisers

For advertisers, footfall attribution means that investments in OOH and DOOH can be tied more closely to business outcomes.

Key advantages include:

• Measuring store traffic from OOH/DOOH campaigns, rather than only reach and views.
• Comparing impact across different formats and environments, such as bus shelters, street furniture or large digital screens.
• Optimising future OOH planning by shifting budget towards the units and set‑ups that generate the most footfall.
• Positioning out‑of‑home as both a brand channel and a concrete traffic driver to stores, restaurants, events or other physical destinations.

Industry organisation OAAA shows in a series of reports how attribution data, including footfall measurement, can be used to demonstrate OOH’s impact on footfall, purchase intent and ad recall (see OAAA’s summary).

When footfall attribution becomes a natural part of media planning, out‑of‑home can compete on the same terms as digital channels – with clear KPIs, measurable store traffic and a stronger link to sales and business results. This is also why more and more DOOH players are integrating footfall attribution as a standard element of their offering.