Count and understand your visitors: from simple in/out to AI analysis
You only manage well what you measure. Footfall sensors turn your point-of-sale traffic into actionable data: how many visitors, when, where, and how they move. Two levels meet every need — simple in/out counting, and advanced AI analysis — all anonymised, no image stored, and fed into the LMS.
Use cases
In/out counting
Incoming and outgoing flows in real time, bidirectional, accuracy around 98% at ceiling level.
Staff exclusion
Staff aren't counted: net, reliable customer traffic, hour by hour.
Gender breakdown (AI)
Share of female/male visitors to adjust offer and communication — no image stored.
Adults / children (AI)
Adult and child distinction to better read family footfall and the departments concerned.
Footfall heatmap
Hot and cold zones of the store: optimise layout, gondola ends and customer journey.
Dwell time & queues
Dwell time per zone and queue detection to open checkouts at the right moment.
Conversion rate
Cross-referenced with revenue and receipts, traffic gives the conversion rate per store, per aisle and per hour.
Physical audience joins retail media
Measuring in-store footfall — anonymised and GDPR-compliant — becomes the basis for steering aisles and valuing in-store retail media: audience, reach and frequency, as in digital.
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