Improving Queue Management with Shopping Cart Detection
Challenge
Retailers across categories lose millions every year to shoppers who abandon long queues. Shopping carts can contribute to the perception of long queues by taking up more floor space than visitors shopping with handbaskets.
Limiting queues means expanding capacity, which is often a matter of maximizing the use of existing resources. To improve resource allocation, such as lane openings or cashier placement, retailers need to accurately identify or measure:
Visitors entering and leaving the store with or without shopping carts
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Abandoned shopping carts in high-traffic areas
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Visitors through unauthorized doors/areas
Tom Tailor Boosts Retail Performance with Xovis Data
Solution
Brick-and-mortar retailers use real-time measurements of visitor flows and the performance of key in-store processes to optimize resource allocation and boost sales. Known as in-store analytics, this data-driven practice enables floor managers to proactively address situations that put revenue at risk.
A sensor-based in-store analytics solution that accurately tracks visitors’ journeys is crucial for effective queue management. To achieve this, retailers need data-capture technology that delivers:
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Real-time people-flow data throughout the store
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Accurate shopping-cart detection and tracking
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Consistent quality data for automated process and alert triggers
Sensalytics relies on sensors from Xovis for a wide range of retail use cases. The company has the know-how and commitment to help us meet the demands of our customers.
Benefits
Using an in-store analytics system with sensors equipped with shopping cart detection ensures predictive modeling is based on accurate data. The result is shorter queues and less revenue lost to queue abandonment.
The shopping cart detection is also important for:
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Recovering abandoned shopping carts before they disrupt people flow or become a liability
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Identifying visitors leaving the store with shopping carts through unauthorized area
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Understanding shopping patterns of visitors with or without shopping carts
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