Digging into Details
NRF’s 2012 Return Fraud Survey of executives at 60 retail companies found the industry lost an estimated $8.9 billion to return fraud in 2012. Almost all said they had experienced the return of stolen merchandise in the past year; 65 percent said the return of used, non-defective items, known as “wardrobing,” is a growing issue.
As a leader in retail transaction optimization solutions, The Retail Equation uses predictive analytics to turn individual shopper visits into more profitable experiences. Vice president of marketing Tom Rittman says the company’s Verify-3 Return Authorization software helps reduce shrink and fraudulent returns at the register and goes beyond exception reporting by creating a more “actionable” solution that hones in on the small percentage of suspicious situations.
“It’s a lot more accurate,” he says. “We look at the consumer’s entire realm of behavior and use statistical models to approve the return, warn [the consumer] or decline it.”
David Speights, chief statistician for The Retail Equation, says the predictive models are more accurate because they use “hundreds of variables,” whereas exception reporting might only use a few. This makes it easier to focus on suspicious transactions and minimize flagging legitimate returns.
The Retail Equation looks for “outliers,” things that have a low probability of happening naturally. The system can also flag customers who have made no purchases and are trying to return products. Speights says the system uses a variety of data keys to analyze each transaction and return, essentially “looking inside the receipt” and using triangulation with or without a form of ID to measure the risk. It then makes a real-time decision for the associate.
“We can get an encrypted credit card number, loyalty number, receipt numbers, e-mail addresses,” he says, “and link all of those transactions together to give us predictive models.”
Fewer denials, instant ROI
Because it narrows the targets and does a better job of denying only fraudsters, The Retail Equation goes straight to the problem. In one recent case study, a major women’s apparel retailer tested the solution in 100 locations and saw an average return-rate reduction of 20 percent. The retailer discovered that some of the worst offenders went from high return frequencies to almost no returns.
When it comes to denying a return, Verify-3 also takes some of the confrontation out of the equation. The software prints a receipt that includes a phone number consumers can call to learn more about the reason why their return was denied. Much like systems that verify checks, referring to a third party helps minimize any tension with the retailer or clerk.
“Part of our service is to deflect any tension in the store,” Speights says, “so we provide that service for the retailer.”
The Retail Equation uses a number of different metrics to quantify the effectiveness of the system. The first is the return rate to determine how much product is going out vs. coming back in. The second is time between the purchase and return — in most cases, the shorter the duration, the less likely the product is to be damaged and the easier it is to sell. Finally, the solution attempts to track consumer behavior after a “warning” to see if the behavior stopped or continued.
“In most cases, we’ve seen a 90 percent reduction in denials,” says Speights. Fraudsters and wardrobers “are highly less likely to engage in their behavior once they know the retailer is on to them.”
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