Visitor analytics for petrol stations & convenience retail

Petrol station analytics counts how many people stop on the forecourt, how many go into the store and how long they stay, site by site. It usually runs on existing Wi-Fi access points, and LiDAR counts arriving vehicles. Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.

The short version

• Per petrol station: how many people stop, how many go into the store and how long they stay.
• The same setup and definitions at every site make the stations in a chain comparable.
• People are usually counted over Wi-Fi access points already in place, and LiDAR counts arriving vehicles.
• Below: four operator questions, ten more, and what a Swedish chain measured across more than 100 sites.

What do petrol station operators ask footfall analytics to answer?

How many of the drivers who fill up ever walk into the shop?

Footfall counting on the forecourt and in the store gives each petrol station its pump-to-store capture rate. It counts the people who stop, those who come into the store and those who refuel and drive off, and it shows how long visitors stay. The counts usually come from Wi-Fi people counting on access points already in place.

How do we compare the stations in our network and copy the best ones?

Every petrol station is measured with the same setup and definitions, so a motorway site and a town-edge site can be read side by side. The comparison shows which forecourts have the highest pump-to-store capture rate, and the rest can learn from their layout, staffing and promotions. Benchmarking footfall explains how the comparison works.

Is EV charging a store opportunity for a petrol station chain?

When a customer charges the car, do they go inside or stay in the vehicle?

At a petrol station with EV charging, the people who charge split into three measurable groups: those who only charge, those who go on into the store or restaurant, and those who drive away. The chain sees this per site, so it can tell where the dwell time at the charger turns into store visits.

Do the shop and the restaurant at our charging bays draw the people who charge?

Each petrol station is zoned into the charging bays, the store and the restaurant, so visits to each can be counted and set against the time spent charging. Comparing stations shows where the offer by the chargers brings people in, and the counts arrive per site as data by zone.

Do vehicles on the forecourt show up in the store numbers?

Yes. LiDAR on the forecourt counts the cars that arrive, and parking occupancy shows how full the forecourt gets and how long vehicles stay. Read against store capture, the numbers cover the visit from kerb to till. Where an exact count at the store door matters, AI cameras or LiDAR add that precision to the Wi-Fi baseline.

Which other forecourt and store questions do we measure?

• How is the shop doing, apart from the fuel, the café and the service stop? It is measured separately from the forecourt and behaves like any grocery or convenience store, with short visits and commuter peaks.
• How many of the cars that pass turn in? Traffic counting by direction on the road and at the site entrance counts both the cars that pass and those that turn in, to be read against the visits to the store.
• Where does the funnel leak between the pump and the till? Drivers decide at the pump, turn off to the car wash or come in and buy nothing, and the forecourt funnel follows each step.
• Of the people who come in, how many buy? Buying is a separate step after capture, and in-store conversion rate explains how to read it.
• Did the new coffee or food-to-go offer bring more people in, or do the same people spend more? Store entries before and after the change show whether more people came in.
• Do the signs, screens and promotions at the pump get more people to go in? Capture rate before, during and after a change shows whether more of the people who stop come inside.
• How many people use the shop without filling up? The dependency rate shows what share of store visitors also fill up, and the case below puts figures on it.
• Which shelves and zones in the shop never get a visit? Hot and cold zones show where people go and where they do not, for layout and product placement; see retail heatmaps.
• How do we staff for the commuter peaks? Hourly and daily peaks show whether staffing follows the real arrival pattern; see staff planning with footfall data.
• Is it worth staying open at night? Hourly curves for visitors and for vehicles on the forecourt can be read against the opening hours, site by site.

A chain’s analysts who want the data per station in their own BI, instead of another portal, can take it through the People counting API.

What did a Swedish petrol station chain learn from measuring every site?

A Swedish petrol station chain measured pump-to-store capture across a network of over 100 sites, zoned from the pumps and EV charging to the store and the restaurant. Capture varied from 8 to 55 percent between stations in one measured period, and the best forecourts showed the rest what to change. The dependency rate was as low as 10 to 15 percent, meaning that only that share of store visitors also filled up, so the store had a customer flow of its own. Visits to the EV chargers could be measured as a store opportunity too. Because the setup is the same at every site, the privacy review is done once for the whole network. Read the forecourt chain case →