You know what sold. You don't know who walked past, came in, tried something on and left without a bag.
The till records the last step of a long funnel. Footfall analytics for clothing stores shows every step before it, so the window, the floor, the fitting rooms and the rota can each be judged on what they actually do.
For the single boutique and the fashion chain alike. Anonymous, 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, the window stops being a matter of taste. Some windows pull people in; others only get looked at.
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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 follow demand instead of tradition.
The funnel a clothing store lives on
Fashion retail runs on a funnel that is rarely measured end to end: people pass the window, some come in, some reach the fitting rooms, some buy. The till only records the last step. Everything that happens before it, the capture rate of the window, the rail nobody stops at, the fitting-room queue on a busy afternoon, is where the sale is won or lost. Footfall analytics for clothing stores measures those steps anonymously and turns them into decisions about the window, the floor plan, the rota and the campaign calendar.
What you see, store by store
- Passers-by and capture rate: how many walk past, how many come in, and what a new window or a sale sign changes.
- Dwell by zone: front of store, the rails, the women’s, men’s and children’s departments, the fitting rooms, the till, so you see where people linger and where they turn around.
- Fitting-room pressure: the share of visitors who try something on, and dwell at the fitting rooms by hour, is the earliest sign of a queue, and a queue there is lost conversion.
- Conversion: visitors against transactions, brought in through an integration with your own sales system.
- The hourly curve: every day, every store, so staff hours follow when customers actually arrive.
- Store against store: the same measures in every location, so your best store becomes the playbook for the rest.
How those zones and paths come together on a floor plan is shown on retail heatmaps. What a good conversion rate looks like for fashion, and why your own baseline matters more than the published one, is on retail conversion benchmarks.
What clothing retailers have done with it
These are things fashion retailers we measure have actually done, in stores on shopping streets, in shopping centres and in retail parks:
- Changed the window schedule after measuring capture rate per window change: some windows pulled people in from the street, others only drew glances.
- Moved staff to the fitting rooms on the hours when dwell there climbed, and watched conversion follow.
- Re-planned a department after dwell per department showed where visitors spent their time and where they walked straight past.
- Judged a sale week honestly: more people came in, a smaller share bought, and the next campaign was planned on that fact rather than on the sales total.
- Followed a new store’s first weeks hour by hour and pointed local marketing at the slots that needed it.
- Measured an outlet and a temporary sales floor the same way as the permanent stores, and used it to decide what to keep.
- Benchmarked a whole chain on the same funnel, so a weak store was diagnosed on capture rate or on conversion and given the right fix.
- Sports and fashion chains have watched the season start week by week, skis, running, and set buying and staffing on the curve rather than the calendar.
- Children’s fashion brings its own pattern, longer visits with prams and families, and fitting-room queues that build earlier in the day.
A flagship on a city shopping street
A Nordic sport and fashion brand’s flagship store on Biblioteksgatan in Stockholm used Bumbee Labs to see not only how many people came in, but how long the visit lasted and how customers moved through the store, to judge which displays actually worked and to make the most of a strong brand in a strong location. The same questions apply to any store with a window on a busy street. Read about flagship stores →
Getting started in a clothing store
Most clothing stores start on the Wi-Fi already in the store: measurement runs on the access points you have, with at most one or two added to cover the entrance and the pavement outside. Where exact entrance counts matter, a small 3D sensor or LiDAR unit above the door adds precision, and staff are filtered out of the counts so the numbers are clean. The first month gives you the hourly curve, capture rate and dwell by zone for that store; a chain adds stores on the same setup and gets like-for-like benchmarking, in the dashboard or straight into your own BI through the API. The method is anonymous by design and is the only footfall method in Europe approved by a data protection authority, so the privacy review starts from a question that is already answered.
How Wi-Fi people counting works → Simple counter, sensor or Wi-Fi: which fits your store? →
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
We already have a door counter. What does this add?
A door counter gives you one number per day. Footfall analytics for clothing stores connects that number to everything around it, the passers-by who never came in, the capture rate of the window, dwell at the fitting rooms and the rails, and the conversion from visitor to buyer, so you know not just how many came, but what to change.
Does this work for a single boutique, not just chains?
Yes. A single store gets the same capture, dwell and conversion picture as a chain, and the questions are the same, does the window convert, where do people linger, when should staff be on the floor. Chains add store-to-store benchmarking on top.
Can we combine footfall with our sales data?
Yes. Sales data is brought in through an integration with your own POS or retail system, so conversion, average spend per visit and sales per visitor hour sit next to the footfall figures in the same dashboard.
Do we need cameras in the store?
No. The core measurement runs on the Wi-Fi you already have, anonymously, and it is the only footfall method in Europe approved by a data protection authority. Where you want exact entrance counts, a small optical 3D sensor or LiDAR unit above the door adds precision without storing images of anyone.
What fashion retailers do with the data
A fashion chain we measure tracked 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 queue at the fitting rooms on busy afternoons was the bottleneck, not the window. Staff were moved from the till to the fitting rooms on those hours, and conversion followed. Several chains we work with run the same funnel in every store, so a weak store is diagnosed on capture rate or on conversion, and gets the right fix rather than a generic one.