You know the month's sales, but not how many visits came with them, which rooms held people or how long they waited for a planner.

Footfall analytics for furniture stores measures visits hour by hour, dwell in each department, the queue at the planning desk and conversion at the till. It runs on the store’s Wi-Fi, with a 3D sensor above the entrance for exact counts. Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.

The short version

• Footfall analytics for furniture stores shows visits by hour, dwell by department, waiting at the planning desk and conversion at the till.
• It can start on the store’s Wi-Fi, with a 3D sensor above the entrance for exact door counts.
• Dwell read next to conversion separates interest from friction. Where a visitor buys after leaving stays outside the count.
• Staff are left out of the figures, and a chain can compare its stores on the same measures.

Which questions can footfall analytics for a furniture store answer?

Long visits, few purchases: where in the store do customers spend the time?

In a furniture store, dwell time by department shows where visitors linger, and conversion shows how many visits end in a purchase at the till. Read together, the two figures separate interest from friction. A showroom is built to draw browsers, so a low conversion is not a fault in itself and is read against the store’s own baseline.

Sales figures reach the platform through an integration with your till system and are shown next to the visit counts, so the same weeks can be read for visits, dwell and results.

Customers look in our store and buy elsewhere: does the store still earn its place?

Visits to a furniture store can hold up when the purchase happens somewhere else, outside the count. Dwell shows that people still use the rooms, while conversion at the till counts only what was bought in the shop. A gap between the two can be the pattern called showrooming.

The count describes that pattern but cannot follow the sale to wherever it happens. Steady visits alongside a slipping conversion are the signal to watch, week on week.

Families come on Saturday: are our staff on the floor at the right hours?

A furniture store counts its Saturday visits by the hour, with staff left out of the figures. The curve then shows when customers are in the shop, and the rota can move towards those hours. Most stores already know their busiest day, but the peak hour within it is often misjudged.

Entrance counts from a 3D sensor above the door give that curve its exact shape. The change the data points to is often a shift of staff between hours, and less often extra headcount.

Where on the walk round the store do customers turn back?

Dwell and routes by department show where customers stop on the walk round a large furniture store, and where they turn back or never arrive. A retail heatmap puts that on the floor plan, with hot zones where visitors stop and cold zones they cross quickly. A cold zone needs interpreting before anyone moves a sofa.

Wi-Fi follows the whole floor as one continuous flow, so dwell and routes can be read across every department. Whether a quiet corner is a layout problem or simply the back of the building is for the store to judge.

Do customers wait for a salesperson at the kitchen planning desk?

At the planning desk of a furniture store, a queue shows up as dwell that keeps rising. Read hour by hour, that dwell tells the store when customers wait for a planner and when the desk stands quiet. Planners can then be scheduled to the hours when the waiting builds.

Wi-Fi counting measures the dwell at the desk. A 3D sensor mounted over the desk can give an exact count if one is needed. No personal images are stored.

The spring rush: how do we tell when it starts and read it week by week?

A furniture store can follow the spring weeks through its weekly visitor count, set against the store’s own earlier weeks, so a rise is seen early and read for what it is. Weather and public holidays can be separated out as well, which stops a warm weekend being mistaken for the season starting.

Easter can behave like a mini-Christmas for some formats, homewares among them, and it moves between years. Reading the weeks around it separately keeps the year-on-year comparison fair.

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

• Our interior shop in the city centre: is the window drawing people in? Capture rate compares the passers-by with the visitors who came in, measured for a period before a window change and a period after it.
• We rebuilt the room displays: is the new showroom doing better? The same zones, measured the same way before and after over like periods, show which of them now hold visitors and which have lost them, which a count at the door alone cannot show.
• How do our big stores compare with each other? A chain can turn every store into an index weighted for floor area and location, so a large city store and a small suburban one stand on the same scale.

How does a furniture store start measuring footfall?

Furniture stores are among the store types we measure. Counting can start on the Wi-Fi access points the store already runs, and a 3D sensor goes above the entrance where exact in and out counts matter. Your own staff are filtered out, so the figures describe customers only. After the first month the store has an hourly curve to read, and a chain can repeat the set-up store by store and send the data on to its own BI through the API. People counter vs analytics compares a simple counter with full analytics.