In June 2026, the University of Leicester published the most comprehensive study ever conducted on self-checkout losses. Led by Professor Matt Hopkins and commissioned by ECR Retail Loss, it drew on data from 39 retailers with a combined annual turnover of 1.16 trillion dollars. The headline numbers tell one part of the story. Stores typically see losses rise 22 percent in the first year after implementing self-checkout. Stores with SCO also report losses that are 33 percent higher on average than comparable locations without it.
Most coverage has missed the finding that changes everything about how you should respond to those numbers. Colin Peacock, group strategic coordinator at ECR Retail Loss, summarized it plainly: “Most SCO loss is generated by ordinary people making everyday mistakes. That is good news. It means retailers can engineer their way out of the problem.” Professor Hopkins went further: “The biggest gains come from designing systems that make accuracy easier for customers, not from trying to read their intent.”
The bagging area scale is designed to read the customer’s intent. It compares what was scanned against what was placed in the bag and flags the discrepancy. If most of the loss is not intent but error, you are deploying the right technology against the wrong problem. That distinction determines everything about whether your loss prevention strategy actually works.
What the Study Actually Found About Where the Loss Comes From
The Leicester study found that 54 percent of transactions in stores equipped with self-checkout now flow through those lanes. SCO is no longer a convenience option at the margin. It is the primary transaction channel in most equipped stores. Consequently, a 33 percent higher loss rate at the channel handling the majority of transactions is a structural margin problem, not a pilot result.
The study also found that missed scans are the most frequent loss type at self-checkout. They occur in 1 to 4.8 percent of transactions, costing an average of 2.50 euros per incident. Critically, the research distinguishes between two fundamentally different causes of those missed scans: intentional theft and honest customer error. Awkward items, fiddly barcodes, confusing screens, and delays in staff assistance all produce missed scans in transactions where the customer had every intention of paying. Estimates of how much SCO loss is deliberate theft vary widely, from 6 percent to 80 percent, depending on the store format, the market, and the product mix.
That range is not a data quality problem. It reflects a genuine operational reality: different stores have fundamentally different loss profiles. A loss prevention strategy that does not know which profile applies to its own stores is designing against an assumption rather than a fact.
Why the Bagging Scale Is the Right Tool for the Wrong Problem in Most Stores
What the Scale Was Designed to Do
The bagging area scale works on a specific logic. It compares the weight of the item placed in the bag against the expected weight of the scanned SKU and flags any discrepancy above the tolerance threshold. That logic is designed to catch intentional behavior: the customer who scans one item and places a different, heavier item in the bag, or the customer who places an item without scanning it at all. The scale is a theft-detection tool. It operates on the assumption that the discrepancy is deliberate.
The 71 percent deployment rate and the 77 percent manager confidence level make complete sense if the primary problem is theft. However, if the primary problem is error, the scale’s logic breaks down in two directions simultaneously. It generates false positives for the honest customer struggling with a produce item that did not scan cleanly. At the same time, it misses the deliberate loss that occurs before or after the lane rather than at it.
What the Study Found Actually Reduces Loss
The Leicester report documents several interventions that produced measurable results. Exit gates showed strong results: one retailer reported a 28 percent drop in walkaways and a 30 to 40 basis point improvement in store loss over 12 months after installing them. “Nudges” that signal to customers that the system is paying attention were also effective. Furthermore, public view monitors showing the customer their own scanning activity produced measurable reductions in missed scans. None of these interventions work by trying to detect theft. They work by making accurate scanning easier and more expected for customers who were not trying to steal in the first place.
The report also documents a retailer running 90 percent self-service penetration at 1 percent stock loss. That outcome is not achieved with better scales. Retailers achieve it with the right combination of exit verification, staff positioning, customer flow design, and SKU-level risk management. The goal is to concentrate human attention on the products and transactions where intentional loss actually occurs.
The Friction Equation That Most Deployments Never Calculate
Every false positive the scale generates requires associate intervention. That intervention interrupts the associate’s primary task, delays the honest customer, and creates the exact kind of friction that produces customer abandonment. The Raydiant research documented that 67.3 percent of consumers report having used a dysfunctional self-service kiosk. The scale is a significant contributor to that experience. When the scale flags an honest customer for a produce item that did not read cleanly, the retailer has simultaneously failed to prevent a loss and damaged the relationship with a customer who was not trying to cause one.
The calculus that most loss prevention teams run treats scale false positives as a minor operational nuisance. In deployments I have worked on across the United States and Latin America, that calculus rarely includes the abandonment penalty. This is the customer who experiences enough friction at the SCO lane to choose a staffed checkout instead. Or worse, the customer who does not return to the store at all. That cost does not appear in the shrink report. It appears in the revenue report, twelve months later, when the SCO program’s contribution to basket size is below projection.
What the Retailers Running at 1% Stock Loss Know That Most Retailers Do Not
The most instructive finding in the Leicester report is not the 22 percent first-year loss increase. It is the corollary: losses today are no higher than they were in 2018, which means the retailers who took the risk seriously have learned to manage it. The 1 percent stock loss at 90 percent SCO penetration is not a vendor claim. It is a documented retail outcome from a store that got the deployment design right.
Two Loss Problems That Require Two Different Solutions
The retailers who reach that outcome do not try to solve both loss problems with the same tool. Honest error gets addressed at the interface layer, through better UX, clearer prompts, and exit verification. The deliberate theft problem, by contrast, gets handled at the exit layer through SKU-level risk management that limits SCO eligibility for high-value, high-theft items. Using the bagging scale to address honest error is like using a security camera to fix a confusing menu screen. The tool is real. The application is wrong.
The Metrics That Reveal Whether SCO Is Actually Working
They also measure the right things. Shrink rate at the kiosk lane is a different metric from total store shrink. Managing SCO performance requires tracking both the lane-level rate and the contribution of non-lane loss. Additionally, they staff the floor based on SCO outcomes, not SCO coverage. The associate who walks the SCO area watching for stuck transactions and slow customer scanning catches more loss than the associate stationed at a monitoring desk watching camera feeds.
As I described in the context of the self-service ROI gap analysis, the gap between the vendor’s projected return and the retailer’s actual Year-1 result is almost always a deployment design gap, not a technology gap. The same principle applies here. The retailers who close the loss gap are not buying better scales. They are designing better deployments.
What This Means for LatAm Retailers
The Leicester study drew on retailers from 11 countries across Europe, Australia, Canada, and the United States. The LatAm self-checkout environment has specific characteristics that make the error versus intent distinction even more consequential. Product packaging in many LatAm markets is less standardized, which creates higher variability in the weight database the scale relies on. Barcode quality varies more across supplier tiers. Furthermore, the customer experience with SCO technology is earlier stage in most markets, which means the honest error rate at the lane is naturally higher than in markets where SCO has been available for a decade.
For a LatAm grocer or retailer deploying SCO at scale, the first-year loss increase will likely manifest primarily as honest error. Intentional theft is a smaller share of the problem in early-stage SCO markets. That is not a reason to avoid SCO. It is a reason to invest in interface design, exit verification, and staff positioning that address honest error directly. The retailers in LatAm who get the distinction right in 2026 will be operating the 90 percent penetration, 1 percent loss model by 2028. The ones who deploy the standard scale-and-camera combination and call it a loss prevention program will face that 33 percent differential longer than they expected.
The Question Worth Asking Before Your Next SCO Expansion
The One Data Point That Changes the Strategy
The Leicester study is the most comprehensive picture the industry has ever had of where SCO loss actually comes from. It confirms that the problem is solvable. One retailer in the study is already solving it at 90 percent SCO penetration and 1 percent stock loss. The path from the 22 percent first-year loss increase to that outcome runs through a specific decision that most retailers have not made.
Do you know what percentage of your SCO losses come from honest customer mistakes versus deliberate theft?
If you cannot answer that question with data from your own stores, you are designing your loss prevention strategy around an assumption. The assumption may be wrong in most of your locations. And the scale you deployed to protect your margin may be generating friction against the customers who were never the problem. Meanwhile, the loss you actually have continues to walk out through a different route.
The retailers who close the SCO loss gap are not buying better technology. They are asking better questions before the deployment is designed. The Leicester study just gave every retailer in the industry the data to ask the right ones.
If you are evaluating a self-checkout deployment or a loss prevention architecture for your retail network, connect with me here or reach me on LinkedIn. I am happy to walk through the deployment framework we use across the U.S. and Latin America.
Adriana Rivas is a retail technology executive and AI strategist, and the founder of a U.S.-based hardware company specializing in self-service kiosks, POS systems, electronic shelf labels, and digital signage deployed across the United States and Latin America. She is the award-winning author of How to Implement Self-Service Without Failing (Amazon #1 Hot New Release, Silver Nonfiction Book Award 2025) and recipient of the Gold Stevie® Award, Thought Leader of the Year 2026. She is also recognized by Thinkers360 as the #7 Global Thought Leader in Retail and a Certified Master Expert in Retail.