Anonymous demographic analytics: visitor age and gender mix
Retail demographic analytics is the statistical estimation of visitors' age band and gender from camera video. EscherSense does this without identifying anyone, without storing face images and in line with KVKK and GDPR. The output is never a personal record, only hourly and daily aggregate distributions your store and marketing teams can act on.
Who a store really serves is often an assumption. Collections, window displays, tone of voice and even the music are chosen around it, and if the assumption is wrong, part of the budget is spent on the wrong audience.
Demographic analytics measures the gap between the audience you target and the people who actually walk in. Does the profile shift through the day? Do branches differ? Is the campaign reaching the right people? Combined with campaign analytics, it gives marketing decisions a solid footing.
How it works
Visitor detection
Anyone who walks into view is picked up by the model and assigned a short-lived anonymous number.
Statistical classification
Age band and gender are estimated; no personal record is created and no face image is stored.
Aggregate distribution
Instead of individual data, hourly and daily distributions are produced and shown on the dashboard and in reports.
What it measures
Age band distribution
How visitors split across five age bands, from 18–24 to 55 and over.
Gender ratio
The female-to-male visitor ratio, broken down by hour and by week.
Hourly profile shift
How the audience in the morning differs from the one in the evening.
Segment mix
The share of family and solo shoppers, inferred from cues such as group size, prams and shopping bags.
Branch and floor profiles
Visitor profile comparisons across stores, regions or mall floors.
In-store use cases
Planning collections around the real audience
If the store's core audience falls in a different age band than assumed, product mix and placement can be rebuilt around it. The effect is then tracked through conversion in the following periods.
Switching digital signage by time of day
If the visitor profile changes through the day, looping a single message is a waste. The hourly mix tells you which content to show in which slot.
Profile differences between branches
Stores in the same chain can serve very different audiences by location. Profile comparison lets you tailor range and promotions to each branch.
Floor-level profiles in a shopping mall
Mall management can see how visitor profiles differ between floors and zones, and use it to assess tenant mix and floor plans.
Privacy and GDPR/KVKK
Demographic analytics is not facial recognition: the system never knows who someone is, it only estimates statistically which age band and gender a visitor belongs to. Face images are not stored, no identity matching takes place, and reports contain only aggregate distributions. We provide support for a KVKK- and GDPR-compliant configuration and for preparing your privacy notice. 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
Demographic analysis needs an angle close to face height, so the camera is usually placed at the entrance or at the head of an aisle. ONVIF-compatible IP cameras with an RTSP stream are used, and suitability is assessed free of charge before installation. Distribution data can be sent via REST API to BI tools, CRM segmentation and marketing automation.
Frequently asked questions
Is retail demographic analytics the same as facial recognition?
No, demographic analytics is not facial recognition and never identifies a person. The system only makes a statistical estimate of age band and gender. Face images are not stored, and results are reported as aggregate distributions.
Is in-store demographic analysis GDPR and KVKK compliant?
Yes, the setup is designed around GDPR and KVKK requirements. No identity data is processed, processing runs in the store by default and retention periods are defined. We also help with a compliant configuration and with preparing your privacy notice.
How accurate is age and gender estimation?
EscherSense measures 96.2% accuracy for demographic analysis. Accuracy depends on the camera angle, and the best results come from a view close to face height. Thresholds are calibrated on your site during the pilot.
Which age bands are reported?
Visitors are reported in five age bands: 18–24, 25–34, 35–44, 45–54 and 55+. Each band can be viewed by gender and by hour. This lets you see how your audience changes through the day.
What camera do I need for demographic analysis?
You need a camera that views the entrance or aisle head from close to face height. Cameras looking straight down from the ceiling are ideal for counting but not for demographics. We assess whether your current cameras are suitable free of charge before installation.
What decisions is demographic data used for?
The most common uses are collection planning, window and signage content, campaign targeting and branch-specific product ranges. Data can be passed to CRM segmentation and BI tools. See pricing for module plans.
Related modules
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 →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 →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 →This module is also covered in the solution family of CX Teknoloji, the team that builds EscherSense: cxteknoloji.com/en/cozumler/demografi/