Retail media network footfall measurement: prove what in-store media reaches

In-store retail media measurement shows a retail media network how many shoppers pass each screen and shelf, how long they stay and whether a campaign lifted visits against the store’s own baseline. It runs on the store’s existing Wi-Fi. Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.

The short version

• Footfall, dwell time and campaign lift are measured for each screen and shelf placement, store by store.
• The measurement runs on the Wi-Fi a store already has, and a pilot can start in a few stores.
• Campaign effect is read as visits before, during and after the campaign against the store’s own baseline.
• Personal data is deleted at source; only anonymous, aggregated statistics remain.

What do retail media networks need to measure?

A retail media network sells access to shoppers: screens, shelf placements and in-store campaigns in the stores where the buying happens.

The store only reports what was sold. How many people actually passed our screens?

Count the people who pass each screen and shelf placement, per hour and per day, and how long they stay near it. That is the audience a retail media network has between the door and the till, measured on the store’s existing Wi-Fi. DOOH metrics explained shows how such counts become reach and impressions.

Which placement works best: the end of the aisle, the entrance screen or the checkout?

Two measures put every placement in order: the footfall past it and the dwell time beside it, which separates a glance from a stop. Wi-Fi reads the zones across the store. At the entrance, where the in/out count has to be exact, 3D sensor counting supplies that count.

How many of our visitors walk past this aisle at all?

Read two figures for the placement’s zone: how many people pass it and what share of the store’s visitors they make up. A retail heatmap shows where the traffic runs and which zones draw and hold it most, so a screen can go where the flow already is.

Did the campaign bring more people into the store, or was it a busy week anyway?

Compare store visits before, during and after the campaign period against the store’s own baseline, with dwell time read alongside. The lift is what the store gains beyond its usual rhythm for that week and season. It is the method from measure event impact, built on a baseline of comparable days.

Our stores are counted in different ways. How can advertisers compare them?

Once every store is measured the same way, the network’s audience becomes comparable, store against store and period against period. A fair comparison needs one counting method and one definition of a visit; see benchmarking footfall. Across stores, an index often travels better than a raw count.

Which stores should get a screen first?

Rank the stores by visitors per hour across the estate and start where the traffic is highest. Each store is counted the same way, so the visitor count is comparable, and the People counting API hands the series to the network’s own tools.

Which other in-store retail media questions can the count answer?

• A stop or only a glance? Dwell time around a screen or shelf tells the two apart.
• How do we turn store visits into impressions? Start from measured visits per store and day and the people passing each placement; the count is the input, whatever definition of an impression the network uses.
• What do we show the bank and the telecoms brand that sell nothing in our stores? The audience around the placement: the footfall past it and the time people stay near it.
• What about the screen by the checkout queue? Dwell time near the checkout shows how long people wait there, and rising dwell time at the till means a queue is building.
• Legal says no to cameras that guess age. Is there another way? Wi-Fi measurement needs no cameras and produces no personal data; see counting without cameras.
• Can the advertiser see sales next to the traffic? External streams such as POS and sales data can be brought into the platform, so visits are read beside what the till took, in aggregate.
• Can we sell the busiest stores in the region as a package? Visitors and reach per store and day, ranked and grouped, give the package a measured basis.
• Should Saturday morning cost more than Tuesday evening? Passers-by at a shelf or screen are counted per hour and weekday, so day-parts can be read from the pattern; for the outdoor version of the question, see DOOH analytics.
• Did the ad in the app and on the site bring people to the store? Only in aggregate: store visits before, during and after the campaign show whether visits rose, and no individual is followed from an ad to the store.

How does retail media measurement stay anonymous in a store?

Signals are anonymised at source, so only anonymous, aggregated statistics ever reach a report. The measurement runs on the store’s existing Wi-Fi (Wi-Fi people counting): the access points pick up the signals phones send as people move. The shopper does not install an app or log in, and no camera is used. A retail media network can report its audience without a camera privacy debate in the store. The method was examined by IMY, the Swedish Authority for Privacy Protection; the IMY decision is explained there.

What does a media buyer get, and can we pilot it first?

A media buyer gets the audience around each placement: visitors and reach per day for each store and placement, dwell time, and the campaign period read against the store’s own baseline. The same figures feed rate cards and post-campaign reports, so a placement is priced on measured footfall.

A network that wants to test before it commits can start with a pilot in a few stores, on the Wi-Fi they already run, and then roll out across the estate.