Combating Shoplifting with AI-Powered Video Analytics

Shoplifting has long been a persistent headache for retailers. It not only causes financial losses but also disrupts store operations, affects the customer experience, and contributes to rising product costs. Currently, as economic hardships grow amid inflation, so does the number of shoplifting offenses. 54% of American small businesses reported a rise in shoplifting. It is not just happening in the U.S. The trend is global. According to Statista, in England, shoplifting rose by 19%, reaching 342,343 incidents in 2023. France also faced a 14% growth of in-store theft. Those range from banal thefts of groceries and other basic necessities to organized retail crimes of specific items that are stolen to be later resold online.

Shoplifting might be perceived as a minor crime, but its cumulative effects are staggering. According to the National Association for Shoplifting Prevention, more than $13 billion worth of goods are stolen from retailers in the United States annually. Apart from profit loss, shoplifting contributes to increased security expenses as it requires placing more security guards and cameras in stores and implementing technologies to tackle the problem.

The implications extend to consumers too, as retailers often raise prices to cover their losses. This affects loyal customers, who now have to pay more for their desired items. Moreover, having to deal with shoplifters results in inconveniences for honest customers, making their shopping experience less appealing.

Though shoplifting seems like an innocuous act, it may end up doing harm to both retail workers and shoppers. Usdaw’s latest annual survey found that 31% of shoplifting incidents in 2022 turned violent and were accompanied by threats and abuse. That makes employees feel anxious and unsafe at work while frightening consumers and discouraging them from shopping at the store.

All that highlights the need for retail outlets to increase security measures to curb shoplifting. One of the ways is by leveraging cutting-edge solutions, notably AI-powered video analytics that can detect anomalous shopping behavior that can result in stealing merchandise, and by utilizing facial recognition technology to identify repeat offenders.

AI-powered video analytics to enhance video surveillance

To tackle the problem, retailers increase the number of security guards and cameras in stores and place signs warning that shoplifters will be prosecuted. However, it does not make much difference, as offenders often get away with stealing. The problem is that it is quite difficult to spot such events. Though there is video surveillance in most stores, human operators cannot implement proper monitoring of numerous cameras simultaneously and detect all acts of shoplifting. According to NASP, only 1 in 48 shoplifters are caught, and only about half of those people are turned over to police for prosecution.

AI technology can help address this issue. It is designed to detect behavior associated with shoplifting by analyzing the live footage captured by CCTV cameras, thus helping retailers prevent losses and boosting the efficiency of store security guards.

Retail analytics is efficient as well as cost-effective, as it does not require retailers to invest in new security equipment. AI-powered video analytics software is seamlessly integrated into the existing video surveillance system, enabling the security team to monitor the entire store area in real time and receive alerts whenever suspicious behavior is detected. They can watch the video footage, assess whether the detected anomaly is actually shoplifting and take the necessary actions to stop the suspect while still in the aisle or at the checkout.

Moreover, cutting-edge AI video analytics accurately differentiates between genuine suspicious behavior and false alarms, which results in fewer unnecessary disruptions for both customers and staff and creates a more secure shopping environment, improving customer satisfaction.

Facial recognition to identify repeated offenders

Shoplifting often occurs spontaneously rather than being planned in advance. 73% of adults and 72% of juveniles arrested for shoplifting do not plan to steal before entering stores. However, approximately 3% of shoplifters can be classified as professional thieves who steal items to profit from them. They often target high-value items such as electronics, designer clothing, and jewelry, so financial damage to retailers can be significant.

While traditional security measures such as CCTV cameras and security guards can be effective to a certain extent, they are often not enough to deter professional shoplifters or those with addiction issues. Facial recognition could provide an additional layer of security that would make it much harder for these individuals to steal from stores.

One of the key advantages of this technology is that it can quickly identify known offenders who have previously been caught stealing from stores. This means that if someone who has been caught before tries to enter a store, the system will capture and immediately analyze facial features to match against a database of individuals with prior shoplifting records. When a match is found, store personnel are alerted, enabling them to monitor the individual closely and intervene before a crime is committed.

Another advantage of facial recognition technology is that it can help identify patterns of behavior that may indicate someone is planning to steal. For example, if the system detects someone repeatedly entering and leaving a store without making any purchases, it may flag them as a potential shoplifter. This would allow staff members to keep a closer eye on them and intervene if necessary.

Apart from deterring habitual shoplifters, facial recognition can also help reduce false accusations, as the technology relies on accurate biometric data. Privacy concerns are valid, but robust security measures and transparency can ensure responsible use of this technology.

Behavior analytics to give predictive insights

AI algorithms can learn to recognize customers’ suspicious activities that are often associated with shoplifting. By analyzing shopper movement patterns, dwell times, and interactions with merchandise, they create predictive models that can identify potentially risky behavior in real-time. For instance, loitering around high-value items, frequenting blind spots, or unusual movement patterns can trigger alerts. This proactive approach allows store employees to intervene before a theft occurs, providing a seamless shopping experience for genuine customers while thwarting potential shoplifters.

Based on the insights gained from analyzing customer behavior and the history of shoplifting events, the system can predict high-risk times or days so that retailers can optimize store layouts and staff allocation to minimize these risks.

Efficient post-incident analysis

AI-powered video analytics can be a valuable tool for analyzing shoplifting events after they happen. It enables tracking the movements of the individual throughout the store, providing a comprehensive overview of the incident. The video footage evidence can assist in a law enforcement investigation or be used to initiate a productive dialogue with the individual and potentially reach an out-of-court resolution where the stolen goods are either returned or paid for.