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Surveillance in retail

According to the National Retail Foundation, businesses can lose up to 1,44% of their turnover to shrinkage thanks to one of these five sources: employee theft, shoplifting, vendor fraud, paperwork errors and, worst of all, the unknown.

Retailers have to defend against product loss and shrinkage by constantly monitoring their stores or centres. And how can retailers monitor what they cannot see or predict? Therefore, deep learning technology in retail analytics plays an essential role in the retail industry today. It allows retailers to reduce expenses, improve their return on capital and manage their resources better.

Some of the practical applications of retail analytics using deep learning are:

• Store traffic management: Improve customer service by planning staff hours around peak times and optimise promotional strategies based on when a zone or the store will be busiest.

• Queue management: Improve business efficiency and increase customer satisfaction through managing the load at checkout points.

• Monitoring checkout transactions: Prevent product loss and shrinkage or fraud and track incoming and outgoing visitors.

So, embrace deep learning because it’s changing the way surveillance in retail is done. Don’t be left behind.

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