Retail heatmaps and customer path analysis
A retail heatmap is a colour-coded view of how much time shoppers spend in each zone of the sales floor. EscherSense builds it anonymously from your existing ceiling cameras, and its path analysis reveals the most common routes from entrance to checkout, movement between aisles and the dead zones that almost nobody visits.
A product that isn't selling is not always the wrong product; sometimes shoppers simply never walk past it. Store layouts are usually set by habit or visual preference, and those assumptions are rarely tested.
A heatmap shows which square metres actually earn attention. Decisions such as where the promo table goes, how categories are sequenced and how aisles are laid out can rest on measurement instead of guesswork. Whether a change worked is answered by comparing the same metrics before and after; for shelf-level detail, the shopper behaviour module takes over.
How it works
Floor plan mapping
Each ceiling camera's view is aligned with the store's floor plan, so every point in the image corresponds to a real spot on the floor.
Anonymous tracking
Each visitor gets a temporary number and their movement is followed while in view. Tracking ends as soon as they leave the frame.
Dwell-time map
Time spent in each zone is aggregated into a heatmap that highlights hot and cold areas.
Path extraction
The most common routes from entrance to checkout, and transitions between aisles, become a flow diagram.
What it measures
Dwell time
Average time spent in a zone; the most direct signal of interest.
Zone visit rate
The share of store visitors who reach a given aisle or department.
Dead-zone ratio
Floor area that receives almost no visits, as a share of total sales space.
Dominant paths
The routes shoppers take most often, including how they reach the checkout.
Aisle-to-aisle transitions
Which category shoppers visit after which; cross-selling opportunities read straight from the paths.
Period comparison
The same metrics before and after a layout, seasonal or promotional change.
In-store use cases
Bringing a dead zone back to life
If a back-wall section gets noticeably fewer visits, the heatmap shows it within the first weeks. Moving a popular promotional product there and comparing before and after tells you whether it pulls aisle traffic deeper into the store.
Choosing where the promo table goes
The promo table is placed on a dominant path without blocking the flow. The effect of moving it is measured with an A/B layout test on the same metrics.
Category and shelf layout in a supermarket
Track footfall density in fresh produce and how changing the category sequence reshapes the route. You can see whether the promoted product actually sits where shoppers walk.
Corridor flow in a shopping mall
Traffic distribution across floors and corridors feeds floor-plan and leasing decisions. It becomes clear which corridors carry visitors and which stay empty.
Privacy and GDPR/KVKK
A heatmap measures movement on the floor, not people. There is no facial recognition and no identity matching; the temporary number assigned to a visitor is valid only while they are in view and expires when they leave the frame. Outputs are aggregate density and path data. By default, video is processed on a server inside the store and raw footage never leaves the premises; if your IT policy prefers, processing can run in the cloud instead. Retention periods are defined, and the setup is designed around KVKK and GDPR requirements.
Setup and integration
Heatmaps use ceiling cameras that cover the sales floor; during setup the view is calibrated against your floor plan. Standard ONVIF-compatible IP cameras with an RTSP stream are enough, and the feed is read passively without touching your NVR. Outputs include the heatmap image, a path flow diagram and a zone dwell-time table, all available via REST API, webhooks and CSV export to your BI tools.
Frequently asked questions
How is a retail heatmap created?
Each visitor's position is tracked anonymously from ceiling camera video, and time spent in each zone is aggregated into a map. Because the image is first aligned with the floor plan, hot spots map directly to aisles and fixtures. Only movement on the floor is used, never identity.
What is the difference between a heatmap and path analysis?
A heatmap shows where and for how long shoppers spend time, while path analysis shows the order in which they move between those spots. Path data reveals aisle-to-aisle transitions and cross-selling opportunities. A typical store shows 3 to 5 dominant paths.
What cameras do I need for in-store heatmaps?
You need ceiling cameras that cover the sales floor, and in most stores the existing security cameras do the job. All of the floor within camera view is included in the analysis. Any blind spots are identified and reported in the free site survey.
What decisions can heatmap data support?
The most common uses are product placement, choosing promotional areas, category sequencing and aisle layout. Dead zones can show that the issue is location, not the product. Seasonal shifts and campaign effects can be tracked on the same map.
How do I measure whether a layout change worked?
The A/B testing module compares the same metrics before and after the change. Differences in dwell time, zone visit rate and dead-zone ratio are reported directly. The permanent layout is then decided by measurement, not debate.
Is heatmap tracking GDPR compliant?
Yes, heatmaps are generated from anonymous movement data without processing identity. No facial recognition is used, and the tracking number expires once a person leaves the frame. Processing runs in store by default; see the FAQ for more detail.
Related modules
Shelf interaction and shopper behaviour analytics
Shopper behaviour analytics measures how store visitors interact with shelves and products, using camera video. EscherSense counts picking a product up, examining it and putting it back as separate events, and anonymously reports how many passers-by look at your window, the mix of family and solo shoppers, and when shelf layouts drift out of order.
Explore module →In-store campaign performance and ROI measurement
Retail campaign analytics measures the separate effect of an in-store campaign on visitor traffic, product engagement and conversion rate. EscherSense compares these three indicators before, during and after the campaign on the same metrics, and combines them with POS data to show the campaign's real return on investment by store and across the chain.
Explore module →People counting and store conversion rate, from your existing cameras
People counting is the automatic counting of visitors entering and leaving a store from camera video. EscherSense does it with AI on the security cameras you already have: it separates entries from exits, removes staff from the count and matches net footfall against POS transactions to give you the store's true conversion rate.
Explore module →This module is also covered in the solution family of CX Teknoloji, the team that builds EscherSense: cxteknoloji.com/en/cozumler/isi-haritasi/