As the results from the pilot tests came in, Gaudette began to realize the true scope of the ongoing loss. “I’d seen the footage from other StopLift pilots, but when I saw my stuff going out the door and not getting paid for, it was kind of an emotional moment,” he states.
Not only did the system point out cashiers who were sweethearting and cashier errors, it also pointed out a technical glitch that had been adding to shrink for some time—a malfunction in the POS exception reporting software. Gaudette explains: “Big Y has a loyalty card, and if a customer forgets it, the customer punches in a phone number, and the loyalty card number comes up. While the customer was punching in that number, all the scans that the cashier was doing did not register. The cashier would hear the ‘beep, beep, beep,’ but the scans were never captured.”
Theft and losses were also found to be plentiful in the self-checkout aisles. “Unfortunately, it only takes one person with larceny in their heart to steal from a self-check; it takes two in a regular lane,” wryly comments Gaudette. The self-scan attendants have since been retrained to be more observant of customers and to watch for items that can be both purposely and accidently overlooked on the bottom sections of grocery carts.
These “bottom-of-the-basket” losses were also found to be common in the cashiered aisles. “I have to admit that I was a little embarrassed, because I had been telling my executives that we don’t have a bottom-of-the-basket problem,” says Gaudette.
After the incidents were exposed by StopLift, retraining of front-end management and staff was undertaken. As a result, bottom-of-the-basket losses are “pretty minimal right now,” he states.
Regarding cashier errors, “We assumed that most losses were intentional, but we also found that there were a huge amount of unintentional misscans where the cashiers thought they had scanned the item, and they hadn’t,” says Gaudette. This issue was addressed in retraining.
Of the intentional sweethearting incidents, Gaudette says that some of what they have learned goes against conventional wisdom. It has been assumed that in sweethearting, there is a relationship between the employee and the customer; however, the incidents caught by StopLift show this is “not true all the time.” Neither is the assumption that certain types of items—for example, high-priced meats—are more likely to be sweethearted.
“What we see in the surveillance videos,” says Gaudette, “is that the selection of the sweetheart items [was] often random because the opportunity presented itself—the supervisor walked away or the service clerk was no longer at the end of the register, for instance.”
At the end of the pilot testing, Gaudette says that it was evident that the StopLift system should be rolled out to all Big Y stores. In preparation for that, Big Y developed “an awareness and education program for the staff [as well as a] counseling process,” he explains.
Loss prevention now receives incident reports from StopLift within 48 hours of an event. Loss prevention then sends each incident report, accompanied by the related video clip, to the manager of the store where the incident occurred. The manager has seven days to reply regarding what action has been taken.
The company has a zero-tolerance policy; employees caught sweethearting are fired. An employee who makes unintentional errors meets with the store’s manager, who explains that the clerk missed scans. “The reaction is almost always ‘No, I didn’t.’ But the manager can show the employee the video,” Gaudette says.
Retraining is then given to the employee. In some cases, cashiers whose performance did not improve after retraining have been reassigned to different jobs. “Some people are just not good at being cashiers. They are often relieved when that happens,” he says.
The Checkout Vision System has now been installed in 47 of the 58 Big Y stores, and it will soon be in all locations. Since the pilot program began, verified incidents of sweethearting and accidental error are down 86 percent. Gaudette says of projected cost savings, “We’re estimating $3 million in nine months. This more than covers the cost of the system. We’ve got our money back already, and we’ll be preventing future losses.”
T-Mobile USA, Inc., a national provider of wireless voice, messaging, and data services, operates about 2,000 company-owned stores across the United States and Puerto Rico. According to Joe Davis, CPP, senior manager of loss prevention for the company’s South Region, “Loss prevention is a new organization inside T-Mobile—just over two years old.” The first step, he says, was “to evaluate what systems were in place to reduce loss and fraud.”
He says the team found that “We had video systems in place that were adequate for physical security—watching the front and back doors—but we didn’t have a robust capability for reviewing forensic video and tying people to transactions inside a business.”
For example, an individual might come into a store and conduct a transaction at the register, claiming to be someone they were not. “Shortly, we get notification from the real person, who says, ‘Hey, I just got a bill from T-Mobile for five new phones and $5,000!’” says Davis. At that point, “We’d have to find the overhead video of the register and try to identify the person who came in and conducted the transaction.”
It was a labor-intensive investigative process, and given the volume of incidents, resources weren’t always available to thoroughly research each reported case, Davis says. Then in 2008, the loss prevention team came across a technology that seemed to offer a way to get the job done cost-effectively. It was the 3VR SmartRecorder P-Series, by 3VR Security, Inc., of San Francisco, a hybrid digital video recorder/network video recorder (DVR/NVR) that had software with embedded analytics and biometrics, such as facial recognition, which could intelligently search surveillance video. It could also integrate with POS, intrusion, access control, and other systems.
T-Mobile decided to pilot the system from the autumn of 2008 until early this summer. The system captures the faces of people coming into a store, and applies metadata—data about the context of the image—and it then catalogs each one. The images can also be tied to POS transactions, if desired, in a searchable database that resides on the company’s network so that in the future, if there were an incident of identification fraud, for example, Davis could watch the video and review each face captured in association with that incident. “Then I can search forensically across my network in all locations where I have the system deployed,” explains Davis.
“The system uses the metadata to find the closest matches to the face that I’m looking for. I can set an accuracy range too so that I’ll be served up faces that are a 50 percent match if I want to look at a broad range, or I can see faces that match at 95 percent for a narrow range,” he says. “What that allows us to do is look for individuals who are targeting T-Mobile stores across multiple locations and across specific markets.”