How loss prevention analytics turns store data into action

Retailers can usually put a number on what they lose, and it’s well documented that the industry loses billions of dollars to shrink every year. The problem is knowing exactly where loss starts, why it happens, and what to do about it. This is because shrink figures cover everything from theft and fraud to missed scans and process errors.

Loss prevention analytics answers those questions first by recording loss, explaining it, and then giving retailers the tools to act on it. 

What is loss prevention analytics?

Loss prevention analytics is the practice of turning a retailer’s raw operational data into answers that provide a comprehensive view of loss. It can also surface patterns that would otherwise stay hidden, like how loss events are linked, e.g., how loss patterns that start on the shop floor complete at self checkout.

The data feeding loss prevention analytics comes from various sources in the retail store and broader estate, captured by loss prevention technology such as POS, video, inventory, and incident records. 

The value of loss prevention analytics for retail teams

Standard shrink reporting tells a team how big the problem is. Loss prevention analytics goes further by showing the detail behind it: which lanes, behaviors, times of day, and events are linked. More importantly, it lets teams respond accordingly.

There are four major causes of shrink according to the NRF, and each calls for a different response, and a single shrink figure can’t say which causes contribute to how much loss.

ECR Retail Loss found that 4% of self-checkout transactions involve a loss event, at an average value of around $5. Small events like these add up when repeated across thousands of transactions. The patterns they form, and the steps retailers can take to mitigate the loss, are only visible with loss prevention analytics.

The components of loss prevention analytics

Loss prevention analytics programs are usually built on four pillars: data sources, linked data, exception detection, and reporting.

These feed into each other; data sources linked together provide a comprehensive view of the estate, making it easier for abnormal patterns to emerge.

Data sources

Every program starts with what it can draw on. The chief of these is point-of-sale (POS) records, which form the foundation of most loss prevention today and are crucial for understanding what happens at the till. With SCO and integrated loss prevention technology, teams can also see forms of loss that don’t generate transaction data, like missed scans. 

Other data sources include:

• CCTV Video 
• Inventory counts
• Stock records and incident logs.

Linked data

Linking connects the sources so one event can be interpreted in the context of everything else. 

If a customer walks out without scanning an item, the POS log won’t show it. LP teams only see the loss later as a stock discrepancy in shrink reports but can’t trace it back to a source.

However, if this data is paired with store intelligence, video footage can show an item left in the basket, and once the two are linked, teams can see exactly how the loss occurred. Beyond flagging this in the analytics, sophisticated systems can also act in the moment and stop the loss in the moment.

Solutions like Everseen’s checkout loss prevention are some of the best examples of this in action.

Exception detection

The next layer of loss prevention analytics is identifying loss events and telling genuine mistakes apart from deliberate theft. At self-checkout, for example, retailers estimate that roughly half of loss comes from honest shopper mistakes.

Seeing every loss event is one level of analytics. A deeper level helps teams tell error from intent, so each gets a proportionate response.

Exception-based reporting does this on the transaction side. It compares transactions against a store’s own baseline and flags outliers like unusual refunds, post-voids, and repeated no-sale drawer openings.

Visibility and reporting

The last component is the bigger picture. With the data linked, loss patterns can be broken down by store, lane, loss type, and time of day, showing where problems concentrate and which fixes are working. From there, advanced systems can zoom out to the whole estate. Regional managers can see which issues repeat from store to store, so a fix that works in one location can be rolled out across all of them.

More on this in our guide on store-wide retail intelligence.

Types of loss prevention analytics

Most loss prevention analytics work at three levels, moving from showing teams what happened to telling them what to do about it. 

Descriptive analytics: Recording loss

Descriptive loss prevention analytics helps retailers establish a dependable baseline against which they can track and report progress over time. It draws on records such as incident logs, inventory counts, and audit data.

Retailers have relied on these methods for decades, even before more sophisticated loss prevention technologies entered the picture. They are useful for understanding what loss has occurred, but not for much else.

Diagnostic analytics: Explaining loss

Explaining loss means connecting sources, such as POS data with video, or transaction records with inventory, so a team can trace loss back to a cause. That might be a missed scan, a process failure, an admin error, or theft. 

Understanding cause is critical because it informs the right response. Without it, teams rely on estimates and intuition, and this gets worse when they work from total loss figures rather than individual loss instances. 

While diagnostic analytics explains what has already happened, it doesn’t tell teams what is likely to happen next or what to do about it.

Prescriptive analytics: Acting to recover loss

At this level, analytics prompts a response before, during, or immediately after a loss event. In each case, teams understand where loss occurs and can act to mitigate it directly.

At self-checkout, that could be an on-screen message prompting shoppers to rescan items. On the shop floor, it could be an alert to an attendant or manager to assist shoppers in the aisles. People still decide what happens next, informed by the data.

In some instances, the data also helps teams pre-empt situations in the store before they escalate. For example, live customer counts and heat maps can point to an upcoming rush at checkout. In this case, managers can be notified and open extra lanes before queues build and customers walk out.

Types of loss prevention analytics at a glance

How Everseen delivers loss prevention analytics for retail teams

Loss occurs throughout the store, so the more data a loss prevention analytics solution can capture and interpret, the more complete the picture for store teams.

Everseen’s platform gives teams access to the entire store’s loss profile by integrating with data sources across the operation.

Checkout Intelligence: Loss data at the checkout

Checkout Intelligence gives full visibility into what is happening at self-checkout and staffed lanes across the whole estate. It detects more than 30 unique loss patterns, including non-scan incidents, product switching, and abandoned transactions. Store staff and loss prevention managers can see which kind of loss is happening, which patterns are most common, and the most involved items.

But checkout only accounts for part of total loss. Everseen’s other capabilities cover the remaining forms of loss on the shop floor.

Store Intelligence: Loss data from the rest of the store

Store Intelligence extends visibility to the rest of the store, where loss starts well before payment. One example is recognizing behavioral patterns in the aisle that correlate with concealment.

It can also identify perishable stock nearing the end of its shelf life, which the platform flags and records. Here, Everseen’s solution can send alerts to managers so they can act before the stock is written off. 

Data Intelligence: Where the analytics come together

Everseen’s Data Intelligence is how retailers access all of their store data. Besides just pulling up store numbers, teams can use the conversational AI interface to ask questions in plain English and get the answer drawn from their store’s data. They don’t need to open a dozen reports to access key insights.

The ROI case for loss prevention analytics

The financial case comes down to two questions: how much revenue is recoverable, and what does it cost to recover it?

Analytics earns its return first by making loss measurable for teams. When causes are separated, with a loss value tied to each, a team can see where recovery is possible and judge what it is worth. Everseen’s Data Intelligence shows the financial impact as a live figure from day one, so leadership can see and defend it at board level, not wait for an end-of-quarter estimate.

The ROI case also has to account for the value of the insights themselves. Every loss event stopped at self-checkout is recovered revenue, which is an immediate return. But the system also identifies loss patterns across the estate, so a single fix can pay off many times over.

For specific ROI figures on Everseen’s broader vision AI solution, see The ROI of Vision AI in retail. 

Knowing where loss comes from changes how teams respond

A 2026 University of Leicester study for ECR Retail Loss found that retailers struggle to tell malicious non-scans from accidental ones. Their own estimates of how much self-checkout loss is down to theft range from 6% to 80%. It’s one example of how uncertainty about the source of loss makes it harder to stop.

That gap is what loss prevention analytics closes. When teams can tell an honest mistake from true loss, they can respond proportionately, and when loss is flagged in real time, they can recover it before the transaction closes.

Everseen’s loss prevention technology connects loss data to the interventions that recover it, so teams can see where loss comes from and what to do about it. Talk to the Everseen team to see how it can help your estate.

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