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C2RO BLOG | AI-Driven Strategies to Counter Changing Room and Self-Checkout Scams

Drawing of a megaphone on a chalkboard saying the word shoplifter.

The retail industry faces a significant challenge in the form of a shoplifting epidemic, with reports of theft incidents skyrocketing in recent years. This surge in shoplifting is occurring alongside the widespread adoption of self-checkout technology, which has introduced new vulnerabilities in the retail security landscape.

In response to these challenges, it becomes imperative for retailers to deploy robust strategies to safeguard their businesses against theft. Within this array of security measures, the adoption of Biometric-Free AI Video Analytics emerges as a powerful deterrent against shoplifting, offering a proactive solution to bolster security and minimize losses.

In this blog, we delve into the present state of the shoplifting crisis, exploring its ramifications and the influence of self-checkout technology. We further examine the crucial role of changing rooms in the context of fashion retail and highlight why Biometric-Free AI Video Analytics stands out as the preferred strategy for addressing this widespread challenge. Additionally, we examine outdated technologies and underscore the operational efficiency benefits enabled by this groundbreaking solution.

The Rising Trend of Shoplifting:

Shoplifting has long plagued the retail industry, but in recent years, it has reached alarming levels. Factors such as economic instability, increased demand for certain goods, and evolving consumer behavior have contributed to this surge in theft incidents. According to recent studies, shoplifting costs retailers billions of dollars annually, eroding profit margins and undermining business sustainability. Furthermore, the psychological toll on retail employees and the negative impact on customer experience cannot be overstated. It is evident that addressing the shoplifting epidemic is paramount for the survival and success of retail businesses.

The Fashion Retail Segment Challenge:

The fashion retail segment presents the biggest and most difficult challenge in terms of controlling theft due to its unique combination of factors, including the high value and desirability of its merchandise, the prevalence of small and easily concealable items, the necessity of providing private spaces like changing rooms for customers, the fast turnover of inventory, and the need for a welcoming and unobtrusive shopping environment. These factors create numerous opportunities for theft, ranging from quick snatch-and-run incidents to more sophisticated methods of concealment and collusion.

The Changing Room – A Crucial Yet Challenging Aspect of Retail Experience:

Changing rooms are a hotspot for apparel theft, accounting for a staggering 90% of all instances. Moreover, they represent a significant portion, about 40%, of total retail shrinkage across diverse segments and markets. Reports detailing how thieves bypass anti-theft measures in the fashion retail segment shed light on a myriad of tactics used to outsmart security protocols. These strategies range from exploiting blind spots in surveillance coverage, such as changing rooms or areas lacking camera visibility, to employing tools like booster bags or magnets to deactivate or remove RFID tags. Additionally, thieves orchestrate distractions or chaotic situations to divert staff attention, often leveraging teamwork and coordination to execute thefts seamlessly. The widespread adoption of retail checkout systems adds another layer of complexity to this challenge. Shockingly, approximately 4% of items go unscanned at checkout, with each unscanned item potentially costing hundreds of dollars. As such, these factors underscore the pressing need for retailers to bolster their security measures and stay vigilant against evolving theft tactics, ensuring the protection of their valuable merchandise and bottom line.

The Role of Self-Checkout Technology:

The advent of self-checkout technology was intended to streamline the shopping experience and reduce labor costs for retailers. However, it has also inadvertently facilitated shoplifting. The self-checkout process lacks the constant supervision of a cashier, creating opportunities for dishonest individuals to exploit loopholes and bypass security measures. Studies have shown that theft rates tend to be higher at self-checkout lanes compared to traditional cashier-operated lanes. Moreover, the anonymity afforded by self-checkout systems makes it easier for perpetrators to evade detection and escape accountability.

Obsolete Technologies in Retail Security:

Despite advancements in technology, many traditional security measures in retail have become obsolete in effectively combating shoplifting. One such example is the reliance on manual surveillance by security personnel. Human monitoring is inherently limited by factors such as fatigue, distraction, and the inability to monitor multiple areas simultaneously. Additionally, legacy surveillance systems that lack advanced analytics capabilities struggle to differentiate between genuine customer behavior and suspicious activities. These outdated technologies are no longer sufficient to address the evolving tactics employed by shoplifters.

C2RO - Theft Deterrence with Biometric-Free AI Video Analytics:

Biometric-free AI Video Analytics emerges as a robust solution not only to combat shoplifting but also to amplify operational efficiency within retail establishments. The AI harnesses millions of biometric-free micro-interactions, enabling accurate interpretation of customer interaction states. Think of it as having a vigilant guard monitoring every transaction across all machines, 24/7, without breaks or distractions.

In the world of fashion apparel, RFID technology is extensively employed for self-checkout and inventory management. Building upon this foundation, we've enhanced our proprietary AI platform by integrating real-time RFID data with our non-biometric/anonymous AI profiles. This patented innovation empowers retailers with unprecedented capabilities to deter in-store and changing room tampering, the latter being the predominant method of clothing theft in fashion retail.

Every item selected by a customer is seamlessly indexed to their non-biometric profile. This system enables real-time detection of tag tampering or removal, whether in changing rooms or blind spots within the store. Should an RFID be discarded or tampered with, the system promptly alerts staff, ensuring swift intervention. Moreover, as customers exit the changing room or move out of blind spots, the system seamlessly resumes tracking their journey. In case of discrepancies between indexed RFIDs and items presented at checkout, the system triggers alerts, allowing for immediate action to prevent theft and maintain inventory accuracy.

Biometric-Free AI Video Analytics offers a wealth of insights derived from video data, empowering retailers to make informed decisions to reinforce security and operational efficiency. By scrutinizing patterns and trends in shopper behavior, foot traffic, and security incidents, retailers can pinpoint areas for enhancement, fine-tune store layouts, and refine security protocols. Moreover, data-driven analytics enable retailers to assess the efficacy of security measures over time, facilitating continuous refinement and adaptation to evolving threats.

The shoplifting epidemic poses a grave threat to the profitability and reputation of retail businesses. While the proliferation of self-checkout technology exacerbates this challenge, retailers can proactively mitigate the risk of shoplifting by leveraging advanced security solutions such as Biometric-Free AI Video Analytics. By harnessing the power of artificial intelligence and video analytics, retailers can detect and deter theft in real-time while upholding customers' privacy rights. In an era where outdated technologies fade into obsolescence, Biometric-Free AI Video Analytics emerges as an indispensable tool for retailers committed to safeguarding assets, enhancing the customer experience, and fostering sustainable growth in an increasingly complex retail landscape.

What makes C2RO a recognized pioneer in privacy-aware AI video analytics? C2RO’s organizational, technical, and physical safety measures related to C2RO ENTERA software were carefully audited by the company’s office of the DPO through an in-depth Privacy Impact Assessment (PIA). The results of the PIA were then reviewed by the CNIL, the French national data privacy supervisory authority, as well as by a Canadian law firm which concurred that C2RO ENTERA™ does not present high residual risks for the privacy rights of individuals, making C2RO ENTERA™ the first AI video analytics technology providers to be compliant with both European and Canadian data privacy laws.

C2RO’s solution allows commercial real estate and enterprise customers to leverage their existing surveillance cameras to understand how visitors engage and behave in their physical spaces by analyzing their demographically classified journeys. With better visibility into how visitors navigate your physical spaces, you can identify inefficiencies in your operations and issues in security, allowing you to improve visitors’ experience. Moreover, the newly captured insights in physical spaces can be combined with customers’ digital footprint, allowing for a 360-degree view into visitor behavior to build effective marketing strategies that create a unified visitor experience.

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