How can retailers reduce shrinkage during a critical sales period?

This year’s summer months should be a much-needed boost for retail sales: the heatwave has led to a spike in retail activity as people flock to the shops to buy seasonal essentials. Coupled with the World Cup, which provided an additional boost to entertainment venues and retailers, retailers are positioned for a strong summer trading period.
But with increased footfall also comes risk. Large numbers of people in a store can pose a security challenge, whilst a spike in retail sales can also go hand in hand with a spike in opportunistic crime, particularly at self-checkouts.
The question for retailers is how to manage that risk without undermining the very footfall they've worked to attract.
Traditional security isn’t working
Retail security has become an increasingly important topic in recent years, with many stores now deploying some sort of anti-theft physical security measure, such as locked cabinets. In extreme cases, some high street retailers have taken to locking their front doors and limiting how many customers can be inside at a given time.
Whilst these traditional security methods can be effective at reducing loss, they also disrupt how people shop. Recent research conducted with the Loss Prevention Research Council (LPRC) shows that three-quarters of shoppers see measures like locked cabinets as a reason to avoid shopping in-store, as they slow down checkout and make the experience much less seamless.
Loss prevention isn’t a zero-sum game, it’s a balancing act between improving customer experience and encouraging repeat visits. This tension is pushing retailers to reconsider what "security" should look like on the shop floor.
AI is changing the approach
Instead of intrusive physical security measures like locked cabinets, modern solutions such as AI-powered cameras are changing retail security. Visible cameras can deter opportunistic theft in high-risk areas such as self-checkouts, but at the same time do much more to support security teams and reduce stock loss.
Combining AI analytics and cloud infrastructure, retailers can receive real-time alerts for any unusual activity, such as loitering near a self-checkout area and intervene before problems escalate. A micro-market retailer managing 24/7 self-service locations, for example, configured a custom alert for customers who enter a store and don't complete a purchase within a set window – allowing staff to investigate before a loss occurs.
Increasingly, this technology also links directly to point-of-sale data, giving security teams a way to cross-reference footage against specific transactions. That makes it possible to flag more subtle forms of self-checkout fraud – such as repeated voids and overrides, or barcode switching – rather than relying solely on visible deterrence.
Beyond security: operational insight at checkout
The same camera infrastructure that supports loss prevention is increasingly being used for a different purpose: understanding how customers actually move through a store. Heatmaps built from camera data can show where footfall is heaviest, where queues are forming, or when a self-checkout bank is sitting idle. That gives retailers a live view of checkout performance.
For store managers, this has practical value beyond shrinkage. Staffing can be adjusted in response to real queue lengths rather than fixed rotas, and store layouts can be refined based on where customers actually cluster. Handled well, this turns a security investment into an operational one, helping keep checkout moving during exactly the kind of high-footfall periods, like this summer, when both queues and risk tend to rise together.
Enabling proactive, not reactive, security
AI-powered cameras don’t just allow for real-time reaction to potential security issues. Activity logging and video-based analysis can turn hours of captured footage into structured data that helps retailers understand where and when losses are occurring.
This creates a comprehensive record that teams can use to analyse shrink patterns and track long-term trends. Over time, this data can inform operational decisions and help identify systemic risks across multiple store locations.
This sort of security solution is scalable, and doesn’t just help to mitigate stock loss in one location; it can be used to improve security across multiple stores and take a proactive approach to tackling shrinkage.
When an incident does occur, modern platforms can combine footage from multiple cameras into a single timeline, showing an individual's path through a store before, during and after an event. That saves investigation teams from manually scrubbing through separate feeds, and makes it easier to build a complete picture – and a stronger case – when footage is shared with loss prevention teams or police.
Privacy considerations
Whilst the above measures are effective at mitigating retail theft and shrinkage, any deployment needs to proactively address privacy considerations. Strong data governance is key to ensuring proper levels of privacy in any security environment. Retailers can take advantage of centralised platforms that ensure only designated employees can access video footage, and that user actions on the platform are recorded in an audit log. This ensures all actions, like viewing a camera feed, are linked to an individual user, in turn allowing admins to audit access – whether it is through routine audits or one-off investigations.
Securing stock without deterring customers
This summer presents a real test for retailers hoping to capitalise on a strong sales period, but it also brings new risks and challenges. The retailers best placed to manage them are likely to be those treating security and operations as connected problems, rather than separate budget lines.
That means investing in technology that enables rapid security response, and in data that gives deeper insight into store activity and customer flow. Retailers who take this approach to infrastructure will be best positioned to make the most of this summer – while keeping shrinkage under control.
.jpg)
.jpg)
