Know who walks in, when, and where they go

People counting and heatmap analytics measure how many people enter a store, where they walk, and where they stop. The data turns gut feeling into numbers: conversion rate per store, zone performance, staffing matched to actual traffic. Modern sensors are GDPR-compliant — they count and track movement patterns without identifying individuals.

You measure online traffic by the second. In-store should be no different.

Most retailers know exactly how many visitors hit their website each hour, what each one looked at, and where they dropped off. The same retailers often know how many people walked into their stores yesterday – maybe. Footfall and heatmap analytics close that gap. They turn the store from a black box into something measurable, comparable, optimisable.

What the data tells you

Where data changes the decision

Anonymous by design, not by promise

There’s a real difference between systems that “anonymise” face data after capture and systems that don’t capture identifiable data in the first place. We use ToF (time-of-flight) sensors that count silhouettes — no images, no faces, no individuals identified. No consent needed because no personal data is processed. We provide DPA and DPIA documentation for retailers that need it for compliance.

For demographic data (age range, gender), we use aggregate statistical methods that do not identify individuals. This is an optional add-on, not the default.

The setup

Software & integration

Dashboard:

web-based, multi-store

Reports:

scheduled email, CSV export

API:

connects to BI tools, POS

Integrations:

standard for retail BI platforms

Staff Planner:

optional add-on with auto-rostering inputs

Hardware

Sensors:

ceiling-mounted ToF (time-of-flight)

Coverage:

~25 m² per sensor (varies by ceiling height)

Privacy:

no faces stored, no individual tracking

Power:

PoE (power over Ethernet)

Mounting:

ceiling, discreet, 8mm profile

From first conversation to live in the lobby

A people counter rollout typically takes 5–8 weeks per location for install plus integration. Multi-store rollouts run in parallel — usually 8–12 weeks for a national chain depending on store access and IT integration.

Measure before and after

Every pattern you can see is a decision you can act on: staff scheduled to match real footfall, layouts that follow how people actually move, and campaigns you can prove worked.

Frequently asked questions

Is this really GDPR-compliant?

Yes. The sensors count and map movement without recording images or identifying anyone, so there is no personal data to store in the first place. It is anonymous by design, which
keeps you clear of GDPR concerns from day one.

Can we exclude staff from the count?

Yes — staff carry small RFID badges that the sensor recognises, so they’re filtered out automatically. Or we exclude back-of-house zones entirely. Both are common.

What about multi-entrance stores?

Multi-sensor setups handle multi-entrance, escalators, lifts and zone transitions. We map the store before installing so the data reflects how customers actually move.

How fast is the data?

Real-time on the dashboard. Scheduled reports run hourly, daily, weekly. Custom alerts when thresholds are hit (e.g. queue forming, low footfall, abnormal patterns).

Will it integrate with our existing BI?

Yes — REST API for footfall, conversion, dwell time. We’ve integrated with Power BI, Tableau, Looker and several retail-specific platforms. Integration is part of the rollout, not a separate project.

Can it tell us why customers don't convert?

Honest answer: not directly. Footfall and heatmap data tell you where people went and how long they stayed — they don’t tell you why someone left without buying. They’re an excellent starting point for hypothesis-building, paired with sales data and the occasional in-store observation.

Want to see what LCD could do for your stores?

Book a 20-minute consultation. We will talk screen sizes, placement, content management and what kind of ROI to expect for your specific use case.