Almost every store has cameras. Almost nobody gets useful data out of them. Yet the most important question in physical retail — how many people walk in, and how many buy? — is answered exactly there, without buying anything new.
Why footfall changes decisions
Receipts tell you how many bought. Without traffic, though, you can't tell whether a sales dip is a footfall problem (marketing, window, hours) or a conversion problem (assortment, staff, pricing). Those are two different diseases with two different cures — and without counting entrances you're treating them blindfolded.
With visits and receipts cross-linked per store you get:
- each store's real conversion, comparable over time;
- the actual peak hours for planning shifts and openings;
- the measurable effect of windows, promotions and re-layouts.
How it works without dedicated hardware
Commercial people-counters have existed for years: proprietary sensors, installation, subscription. The road I use is different: existing IP cameras already emit a video stream (RTSP) — a small server with a vision model analyses it and counts people in and out. No new sensors, no construction work.
Raw numbers lie happily, though: reflections, staff walking in and out, closing hours. That's why half the job is cleaning — filtering noise and anchoring the numbers to real opening hours. It's the difference between a counter and a number you can trust.
What about privacy?
The system counts silhouettes, it does not recognize people: no biometric data, no faces stored. It's a GDPR-compatible setup, documented in the store's privacy notice.
Where to start
With a pilot in a single store, on the cameras already there: two-three weeks to tune the model and verify accuracy against sample counts. Then extend across the network and cross-link sales — as in this real retail-network case.
Want to know if your cameras can do it? See the Video analytics & Computer Vision track or write to me — the camera model is all it takes to answer.