A fashion retailer turned store cameras into a footfall-and-conversion engine

The challenge

Merchandising and staffing decisions were made without knowing footfall, conversion, dwell or queue reality per store.

Merchandising and staffing decisions made without footfall, conversion, dwell or queue data are essentially guesses — and across 80 stores, small guessing errors compound into real lost sales.

What we deployed

VISTA Retail Intelligence — footfall, conversion, heatmaps, dwell and queue analytics from existing store cameras.

Key capabilities

• Footfall countingAccurate store traffic from existing cameras.
• ConversionTraffic-to-sale conversion per store.
• Heatmaps & dwellSee where shoppers go and linger.
• Queue analyticsCheckout queues measured.
• No new hardwareRuns on existing store CCTV.
• Pilot-to-rolloutProven in 10 stores before 80.

How it works

Store CCTV is analysed for footfall, conversion, zone dwell and queues; merchandising and staffing move to evidence, tested first in a 10-store pilot.

Outcomes

• Footfall and conversion per store
• Heatmaps and dwell by zone
• Queue and checkout analytics
• Pilot before an 80-store rollout

The use case in depth

The camera a store already has can be its best analyst. Footfall, conversion, dwell and queue analytics turn CCTV into the retail metrics that drive merchandising, staffing and layout — grounded in what shoppers actually do.

Piloting across 10 stores before an 80-store rollout is the disciplined path: prove the analytics change decisions and lift conversion, then scale the software across the estate on cameras already in place.

Frequently asked questions

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