You know what sold, but not who walked past, came in, tried something on and left without a bag.
Store by store and hour by hour, footfall analytics shows who walks past, comes in, reaches the fitting rooms and buys, so you can judge the window, the floor and the rota on what each of them does.
Anonymous measurement for the single boutique and the fashion chain alike, approved by a data protection authority.
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Three questions every clothing store owner asks
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Is the window pulling people in, or just getting looked at?
Capture rate is the share of passers-by who actually enter. Measured per window change and per campaign week, it shows which windows pull people in and which only get looked at, so the window is no longer a matter of taste.
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Where in the store does the sale die?
Dwell by zone shows whether people reach the fitting rooms, how long they wait there on a busy afternoon, and which rails nobody stops at.
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Is the rota built on when customers come, or on habit?
An hourly visitor curve per store, week after week, shows when the floor is full and the till is quiet, so staff hours can follow demand.
On this page
Footfall analytics for clothing stores measures the steps before the till: passers-by, entries, dwell, the fitting rooms and sales. It runs on the store’s Wi-Fi, with a 3D sensor above the door where exact entrance counts matter. Bumbee Labs is the company behind the only footfall method in Europe approved by a data protection authority.
The short version
- Footfall analytics for clothing stores shows passers-by, entries, dwell by zone, the fitting rooms and conversion, store by store and hour by hour.
- Measurement starts on the Wi-Fi already in the store, with a 3D sensor above the door for exact entrance counts. No cameras are needed.
- Eight questions follow, from the rota and the shop window to rent and opening hours.
- After them come ten shorter questions and a section on getting started.
Which questions can footfall analytics for a clothing store answer?
Does our rota follow the customers, or the way we have always done it?
An hourly visitor count for each store, week after week, shows when the floor is full and the till is quiet. Staff hours can then be planned around when customers arrive. Wi-Fi supplies the count, and a 3D sensor above the door makes it exact where that matters.
Do people come in after a window change, or just look and walk on?
Capture rate per window change and per campaign week shows which windows pull people in from the street and which only draw glances. Passers-by are counted on the Wi-Fi that covers the pavement outside. Entries are counted at the door, exactly if a LiDAR people counting unit sits there. A new sale sign counts as a window change too.
Any store with a window on a busy street asks the same question, and flagship stores have their own page on it.
Is the fitting-room queue costing us sales on busy afternoons?
Rising dwell at the fitting rooms in the busy hours means a queue is building, and a queue there costs conversion. Retailers we measure have moved staff from the till to the fitting rooms in those hours and watched conversion follow. Measuring conversion needs sales data, which comes in through a POS integration with your own till system.
Which rails does nobody stop at, and where do shoppers turn round?
Dwell by zone shows where people linger and where they turn round: at the front of the store, along the rails, in the women’s, men’s and children’s departments, at the fitting rooms and at the till. A rail nobody stops at is a zone with no dwell, and a retail heatmap puts it on the floor plan.
How many of our visitors actually try something on?
The share of visitors who reach the fitting rooms is the step between walking in and buying. Together with dwell time in the fitting rooms by hour, it gives the earliest sign of a queue there. Wi-Fi shows which zones of the clothing store visitors reach and how long they stay there.
More people came in during sale week, but a smaller share bought. What happened?
Retailers we measure have judged a sale week on both visits and the share who bought. More people came in while a smaller share bought, and they planned the next campaign around that. Read in-store conversion rate beside the week’s visitor count, and measure event impact the same way across the campaign calendar.
The page on retail conversion benchmarks covers what a good conversion rate looks like for fashion retail, and why your own baseline matters more than the published one.
Is the rent right for a unit in a shopping centre or on a shopping street?
Your own passers-by, visits and capture rate give a rent discussion figures that tenant and landlord can both read and accept, in turnover rent and renewal talks alike. For a clothing store on a shopping street, in a shopping centre or in a retail park, they show how the unit did against the street around it.
Is the street busiest when our shop is open?
Count the passers-by outside in the evening and at weekends and set them against your opening hours. Retailers we measure have changed their hours after the flow outside showed neighbours trading while they were shut, or an empty street on a slow morning. The count comes from the Wi-Fi that covers the pavement; see Wi-Fi people counting.
What else do fashion retailers want footfall data to answer?
- Where should our next store go?Counting the flow outside a planned location before the store opens shows what the street offers.
- Do customers come back after the sale?With opt-in, repeat visits show as the share of returning visitors after a campaign or a change of season.
- Does the shoe department sell, or only the shoes in the window?Capture rate at the window and dwell in the department tell the two apart for a shoe or bag shop inside a fashion chain.
- Shopping centre or shopping street?Set capture rate for the same concept side by side in both settings and ask which setting suits it.
- Saturday afternoon: full floor, empty till?Dwell by zone and hour shows which parts of the floor are full, and so where a member of staff is needed.
- January: do returns pull our conversion down?Visits and transactions by hour show how conversion moves in January, while the reason behind it, returns included, needs the till data alongside.
- When does the season really start?Sports and fashion chains have followed the start of the ski and running seasons week by week, and set buying and staffing on the curve rather than the calendar.
- Nobody goes up the stairs: does the upper floor lose them?Dwell by zone shows where visitors go and where they turn round, which is where to start when an upper floor stays quiet.
- Women’s, men’s or children’s: who gets the floor space?Dwell per department, before and after a move, shows which department holds visitors and which they walk straight past.
- What happens in the first weeks after a new store opens?Following those weeks hour by hour shows which slots local marketing should aim at.
How does a clothing store start measuring footfall?
Most clothing stores begin on the Wi-Fi already in the building. The existing access points do the measuring, and one or two extra ones at most cover the entrance and the pavement outside. Where exact entrance counts matter, a small 3D sensor or LiDAR unit goes above the door, and staff are left out of the counts. The first month gives you the hourly curve, capture rate and dwell by zone for that store. A chain can add more stores on the same setup and compare 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, the guide to people counter vs analytics compares them.
Curious what this looks like for your venue?
How fashion retailers use the data
A fashion chain we measure followed capture rate and fitting-room dwell across its stores through a campaign period. The campaign pulled more people in from the street, yet a smaller share of them reached the fitting rooms. The bottleneck was the queue at the fitting rooms on busy afternoons. The chain moved staff from the till to the fitting rooms during those hours, and conversion followed. Several chains we work with run the same funnel in every store. They can see whether a weak store has a capture-rate problem or a conversion problem, and aim the fix at that step.
We are excited about the collaboration with Bumbee Labs through our Unicorn Academy program. A fantastic solution that fits in most of our industry segments such as retail, transportation, public sector etc. We are happy to support Bumbee Labs with our expertise and global presence, and we are delighted to add such an innovative solution to our portfolio, to be offered standalone or integrated in our own solutions.
Frequently asked questions
How does footfall analytics measure the window capture rate of a clothing store?
Passers-by are counted on the Wi-Fi that reaches the pavement outside the clothing store, and entries at the door, exactly where a LiDAR or 3D sensor sits above it. Capture rate is the share of passers-by who come in. Read per window change and per campaign week, it shows which windows pull people in and which are only looked at.
Can footfall analytics show whether the fitting rooms in our clothing store are holding back sales?
Yes. It shows what share of visitors reach the fitting rooms and how long they stay there, hour by hour. Dwell that rises in the busy hours means a queue, and a queue there costs conversion. A fashion chain we measure answered such a queue by moving staff from the till to the fitting rooms on busy afternoons.
How can footfall analytics help a clothing store plan staff on the shop floor?
Counted hour by hour, week after week, visits to each clothing store show the hours when the floor fills up while the till stays quiet. Dwell by zone and hour adds which parts of the floor are busy, so staff hours can follow the customers, on the floor, at the fitting rooms or at the till, instead of habit.
Can we combine footfall data from our clothing stores with our sales data?
Yes. Sales data comes in through an integration with your own POS or retail system, so conversion and sales appear beside the footfall figures in one dashboard.
Where this fits
Part of
Industries and segments
Methods
Platform and integrations
Data protection:GDPR-compliant footfall analyticsThe IMY decisionGDPR CCTV