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How Video Analytics Can Help Retail Stores Tackle Shoplifting

Shoplifting is a major problem in the UK, costing retailers billions of pounds each year. In 2022/23, shoplifting accounted for £1.76 billion ($2.15 billion) in losses for retailers, according to the British Retail Consortium (BRC). This represents an increase of 27% from the previous year.

Shoplifting can have a significant impact on retailers, both financially and operationally. It can lead to lost sales, increased costs for security and insurance, and a decrease in employee morale. In some cases, shoplifting can even force retailers to close their doors.

The State of Shoplifting in the UK

Shoplifting is a complex problem with a variety of contributing factors. Some of the most common reasons for shoplifting include:

• Economic hardship: Shoplifting rates tend to increase during periods of economic hardship, as people may resort to theft to meet their basic needs.
• Addiction: Shoplifting is often associated with addiction to drugs or alcohol. People who are addicted may steal to support their habit.
• Opportunity: Shoplifting is more likely to occur in stores with poor security or where staff are not paying attention.
• Boredom or excitement: Some people shoplift for the thrill of it, or because they are bored.

Shoplifting can occur in any type of retail store, but it is most common in supermarkets, clothing stores, and electronics stores. It is also more common in large cities and urban areas.

 

The Impact of Shoplifting on Retailers

Shoplifting can have a significant impact on retailers, both financially and operationally. Financially, shoplifting losses can eat into profits and make it difficult for retailers to stay afloat. In some cases, shoplifting can even force retailers to close their doors.

Operationally, shoplifting can lead to a number of problems, including:

• Increased costs for security and insurance: Retailers often have to invest in additional security measures, such as security guards and CCTV cameras, to deter shoplifting. They also have to pay higher insurance premiums to cover their losses.
• Lost sales: Shoplifting can lead to lost sales for retailers, as stolen merchandise cannot be sold.
• Decrease in employee morale: Shoplifting can be a demoralizing experience for retail employees. They may feel like their work is undervalued and that they are not being supported by their employers.

How Video Analytics Can Help Tackle Shoplifting

Video analytics is a technology that uses artificial intelligence (AI) to analyze video footage. It can be used to detect and track objects and people in video footage, and to identify suspicious activity.

Video analytics can be used to tackle shoplifting in a number of ways. For example, it can be used to:

 

• Identify potential shoplifters: Video analytics can be used to identify potential shoplifters based on their behavior. For example, it can be used to identify people who are concealing merchandise, removing tags, or loitering around high-value items.
• Detect known shoplifters: Video analytics can be used to detect known shoplifters who have been banned from stores. This can be done by creating a database of known shoplifters and using video analytics to match their faces to the footage.
• Track shoplifters throughout the store: Video analytics can be used to track shoplifters throughout the store, even if they move between different aisles or floors. This allows retailers to monitor shoplifters closely and to intervene quickly if necessary.
• Alert staff to suspicious activity: Video analytics can be used to alert staff to suspicious activity in real time. This allows staff to intervene quickly and prevent theft from occurring.
• Investigate shoplifting incidents: Video analytics can be used to investigate shoplifting incidents that have already occurred. This can help retailers to identify and apprehend shoplifters, and to recover stolen merchandise.
• Improve staff efficiency: Video analytics can help to improve the efficiency of staff by automating tasks such as monitoring CCTV footage and identifying suspicious activity. This frees up staff to focus on other tasks, such as customer service and sales.

Real-World Examples of Video Analytics in Action

A number of retailers in the UK are already using video analytics to tackle shoplifting. For example, the supermarket chain Tesco uses video analytics to identify potential shoplifters and to alert staff to suspicious activity. Tesco has reported that video analytics has helped to reduce shoplifting losses by up to 20%.

Another retailer that is using video analytics to tackle shoplifting is the clothing chain Marks & Spencer. Marks & Spencer uses video analytics to identify people who are concealing merchandise and to remove tags. Marks & Spencer has reported that video analytics has helped to reduce shoplifting losses by up to 15%.

A supermarket chain used video analytics to identify a group of shoplifters who were working together to steal high-value items. The video analytics system detected the shoplifters concealing merchandise and passing it off to each other as they left the store. The store manager was alerted to the suspicious activity and was able to apprehend the shoplifters before they could leave the store

A jewellery store uses video analytics to track the movements of high-value items around the store. The system alerts staff if an item is moved without authorization, allowing them to intervene and prevent theft.

A clothing store used video analytics to identify a shoplifter who was removing tags from merchandise and then returning the items for a refund. The video analytics system detected the shoplifter removing the tags and then following her throughout the store as she returned the items for a refund. The store manager was alerted to the suspicious activity and was able to confront the shoplifter before she could leave the store.

An electronics store used video analytics to investigate a series of shoplifting incidents in which high-value items were being stolen. The video analytics system was able to identify the shoplifters and to track their movements throughout the store. The store manager was able to use the video footage to identify the shoplifters and to apprehend them.

A liquor store uses video analytics to detect customers who are underage. The system compares the customer’s face to a database of underage drinkers. If there is a match, the customer is refused service.

A convenience store uses video analytics to identify customers who are pumping gasoline without paying. The system monitors the license plate numbers of vehicles at the gas pumps and compares them to a database of customers who have failed to pay for gasoline in the past. If there is a match, the customer is flagged for further investigation.

Conclusion

Shoplifting is a major problem in the UK, costing retailers billions of pounds each year. Video analytics is a technology that can be used to tackle shoplifting in a number of ways. It can be used to identify potential shoplifters, to alert staff to suspicious activity, and to investigate shoplifting incidents.