LP Magazine

JAN-FEB 2019

LP magazine publishes articles for loss prevention, asset protection, and retail professionals covering shrinkage, investigations, shoplifting, internal theft, fraud, technology, best practices, and career development.

Issue link: http://digital.lpportal.com/i/1078914

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Page 60 of 77

Prescriptive analytics identified and broke up an organized retail crime (ORC) ring that turned out to consist of student employees at the call center of a fashion retailer. America were made in brick-and-mortar locations during the last quarter. Stores offer immediate gratification—something Amazon has attempted to do with one-hour delivery options for Prime Now combined with free returns. LPM: Beyond these, how are specific retailers improving e-commerce as well as brick-and-mortar operations with prescriptive analytics? YEHIAV: When it comes to prescriptive analytics, there are many examples from retail and e-commerce. Here are a few success stories from leveraging a prescriptive analytics solution. A large retailer with a popular loyalty-card program used prescriptive analytics to uncover an issue they weren't even aware was a problem. Leveraging prescriptive analytics, they were able to discover a trend that involved shoppers cheating their reward points for free rewards. The retailer's previous system did not integrate points accumulation until the end of each day, so if a customer brought their receipt to multiple stores in a single day, it was easy to receive many times more reward points than were due, then convert it to real money, just like a free ATM machine. The retailer's prescriptive analytics solution caught the recurrence and saved millions within the first week of use. Prescriptive analytics identified and broke up an organized retail crime (ORC) ring that turned out to consist of student employees at the call center of a fashion retailer. The ORC group would legitimately buy a product online and, after receiving it, would call the center to complain they never got the product. Thus, they would receive a second product plus a $20 gift card as a customer-appeasement gesture. Prescriptive analytics stopped the ring, saving the retailer an average of $50,000 a month. Another fashion retailer used prescriptive analytics to identify a glitch in their transaction process. This glitch purged transaction records from the retailer's point-of-sale (POS) system after seven minutes of idle time. Cashiers who knew about this glitch found a way to exploit it. When the cashiers loaded a live gift card, they waited the required idle time, generating cash out of thin air. Leveraging prescriptive analytics, the data revealed that the cashiers were loading money onto gift cards but not ringing up the amount. Instead they waited, and seven minutes later, the glitch kicked in, leaving active funds on the gift card but no evidence of a completed transaction. From there the cashiers could return the gift card for cash or spend the funds on merchandise without issue. Thanks to prescriptive analytics flagging this, the company was made aware the glitch even existed, took corrective action on the cashiers, and fixed the issue. The savings and future losses avoided were significant. 59 LP MAGAZINE | JANUARY–FEBRUARY 2019

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