You know what sold last season, but not how many came in week by week, which parts of the floor held them and where they queued.

Footfall analytics for sports stores counts visits week by week and shows how long people stay in each department and at service points. Existing Wi-Fi access points do the counting, and a 3D sensor at the entrance makes the door count exact. Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.

Which questions can footfall analytics for a sports store answer?

When does the season really start in our shop, week by week?

In its weekly visitor count, a sports store reads the start of a season as a curve that begins to rise. Set it beside the same weeks a year earlier and you see whether the season is early, late or on time, and whether buying and the rota keep up.

A year of counting turns the curve into a baseline, so ordering and the rota can be planned months ahead. Black Friday and Christmas footfall applies the same approach to the peak weeks.

Customers wait for the running analysis and the shoe fitting: when does the queue build?

Read hour by hour, dwell at a running analysis or shoe-fitting area tells a sports store when a queue forms. A rise in dwell time there means a line is building and a fall means it has cleared, so the busy hours stand out.

The store’s existing Wi-Fi gives dwell for each area of the floor, so staff can be placed by measured waiting times in the hours queues tend to form. The count does not cover how the session itself goes.

Our workshop has a long waiting list: what can the count show at the desk?

In a sports store with a workshop, the count covers the service desk and the collection point. Hour by hour and weekday by weekday it shows visits and dwell at the desk, and when a queue builds. Where the desk needs an exact count, a 3D sensor above it adds one, along with the queue length.

The desk can then be staffed by measured waiting times in the hours customers gather. Nothing identifies a person and no personal images are stored. How long the work itself takes stays in the workshop’s own system.

The week before a big race: does the shop feel it in the count?

A sports store near a big race can read the days around it as visits per day: the weeks before, the race days and the days after, set against the store’s own ordinary days. This is how event impact is measured, and standard figures are ready the day after the visit.

Add the same weeks a year earlier, so the race is not mistaken for a season already building, and judge it against what those days would have looked like anyway. The count does not show whether visitors came for the race.

Footwear, apparel or equipment: which part of a large floor holds the customers?

Dwell by department tells a sports store which areas of a big floor people linger in and which they pass straight through. A retail heatmap draws the same picture on the floor plan, hot zones against cold ones. Floor figures show use, and what each department sells comes from the till.

Test a move with the same zones, the same method and comparable periods on both sides. A cold corner is worth investigating before anything is moved: the cause can be the layout, the product range or simply where the corner sits in the building.

What else do sports retailers want their footfall data to answer?

• Which store in our sports chain does it right? A chain can line its stores up on one scale: each is read against its own baseline and weighted for floor area or location, so a city-centre shop and one on the outskirts compare fairly.
• Is a quiet week the weather or the shop? A measured baseline for the date, the weekday and the weather helps separate the two, so a rainy Saturday can be told apart from a real shortfall.

How does a sports store start measuring footfall?

Sports stores are among the store types we measure. A sports store can begin with the access points that already carry its Wi-Fi, adding a 3D sensor at the entrance where the door count must be exact. Staff are left out of the figures. Beginning a few weeks before a busy period gives ordinary weeks to read it against, and a year of counting shows the shape of the store’s own season. For a choice between a simple counter, a sensor and Wi-Fi, people counter vs analytics sets them side by side.