You know what the tills took, but not how many came in to look, which departments held them and how long they waited at the service desk.

Footfall analytics for consumer electronics stores counts visits by hour and shows how long customers stay in each department and at the service desk. The store’s Wi-Fi does the counting, and a 3D sensor above the entrance gives exact door counts. Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.

Which questions can footfall analytics for an electronics store answer?

Are our salespeople on the floor in the hours the shop is busiest?

Footfall analytics for an electronics store counts visits by hour and weekday and leaves staff out, so the curve shows when customers are in the shop. Set the sales rota beside that visitor count and you see which hours are covered and which are thin.

The curve shows when customers are in. How many salespeople each hour needs stays your call. Staff planning with footfall data explains how an hourly curve becomes a rota.

People try the phone in our shop and buy it online: what can the count show?

In an electronics store the count puts two figures next to each other: how long visitors stay in a department, and how many visits end in a purchase at the till, the conversion. Long stays with low conversion can be what showrooming looks like in the numbers. Where those visitors buy afterwards stays outside the count.

Sales data comes in through an integration with your own system, so visits and till results sit beside each other. Web-shop figures make a poor yardstick, since a shop has no sessions or carts to count. Read the store against its own earlier weeks.

Black Friday week and the days after Christmas: how do we read them?

Read the peak per store in visits by hour, dwell and conversion, each against the store’s own ordinary weeks. In an electronics store that shows when the crowd arrived and how many of them bought. The first week after Christmas can bring gift-card redemptions and returns, so read it separately from the run-up.

Start before the peak. A store already counting on its existing Wi-Fi has ordinary weeks to compare November with, and next year’s peak can be set against this year’s hour by hour.

When does the queue build at customer service and the collection desk?

Dwell at each desk, read hour by hour, shows the queue: when time at the desk keeps rising, people are waiting. In an electronics store the desks to watch are customer service and the collection point. Each desk can then be staffed from measured queue times in the hours the queue builds.

Wi-Fi measures the dwell at the desk. Where the count at one counter has to be exact, a small 3D sensor can sit above it and read the queue length there. No personal images are stored and nobody is identified.

Appliances, phones or computers: where should the floor space go?

Dwell by department, before and after a move, shows which part of an electronics store holds visitors and which they cross without stopping. A retail heatmap puts that on the floor plan. It shows how the floor is used, while what each department sells still comes from the till.

To test a move, measure the same departments the same way before and after, over like periods. A department with little dwell is a question to look into, since the cause may be the layout, the range or simply the back corner.

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

• A telecom shop with a queue at the counter and a campaign running: what does the count show? Dwell at the service point shows when a queue forms, and visits before, during and after the campaign show whether it drew people in or only sold to people already coming.
• Which of our electronics stores works best? A chain can compare its stores on the same measures, weighted for floor area and location, so a big-city shop and a suburban one sit on the same scale.

How does an electronics store start measuring footfall?

Consumer electronics stores are among the store types we measure. An electronics store can start on the Wi-Fi already in the building, so the existing access points do the counting. A small 3D sensor above the door adds exact entrance counts, and staff are left out of the figures. After the first month the store has its hourly curve and dwell by department. A chain adds stores on the same setup and compares them like for like, in the dashboard or in its own BI through the API. If you are unsure whether a simple counter, a sensor or Wi-Fi fits, people counter vs analytics compares them.