A whopping 69 percent of retailers treat returns as a cost of doing business, meaning they simply accept that they will incur $165 million in merchandise returns for every $1 billion in sales. This problem is further exacerbated during the holiday season when 17.9 percent of merchandise sold is returned. But what if one shift could help them reduce this expense?
One crucial yet often overlooked strategy for reducing returns is leveraging data to inform and communicate personalized policy changes. For instance, a shopper who has historically abused a retailer’s lenient returns policies should be warned about future restrictions. Meanwhile, loyal shoppers who abide by the retailer’s rules can earn more relaxed policies that don’t require receipts or grant extended time to return during busy holiday seasons.
So how can retailers use their detailed shopper data to create and communicate individualized policies for consumers who abuse returns during the holiday season and year-round? The secret is artificial intelligence.
The Problem: Common Costly Returns Behaviors
Some of the most common and costly types of fraud are seen as excusable in the eyes of the consumer. In fact, the consumer might not even realize their action is against the store’s policy.
For example, bracketing — the act of purchasing items in various sizes, only to return the ones that don’t fit — is on the rise, with 20 percent of U.S. shoppers under the age of 30 reporting that they always do it. Similarly, wardrobing fraud is another common type of fraud in which a customer purchases merchandise, uses it and then returns it as if it were new.
Both scenarios have become increasingly common because of social media. When consumers share how they were able to work around a return policy and oftentimes receive an unwarranted refund, this can encourage other bad actors to try the technique. This behavior is known as bandwagon fraud, and it can be common around the holidays.
If these consumers were warned that their actions would impact their future shopping experiences, they might change their behavior.
The Solution: An Individualized, AI-Based Approach to Returns Policies
Traditionally, an influx of fraudulent scenarios like those described above would force retailers to create stricter returns policies. These policies set rigid limits to the number or value of returns for a shopper during a given period. This policy would impact everyone — even if the shopper has never engaged in fraudulent returns behavior.
Now, retailers can rely on AI models that detect patterns in shopping behaviors and recommend a personalized returns policy that will correct their behavior without impacting anyone else. The shopper may be given a personalized warning that lets them know that their previously recorded fraudulent behavior will result in both purchase and returns declines if it’s repeated in the future.
For example, a consumer in the top 20 percent of a retailer’s spenders may be given a warning after 10 costly returns, while someone who spends very little and returns frequently may be warned of future return declines after their fifth infraction.
By taking a holistic, yet individualized approach that considers the entirety of a shopper’s behavior, retailers can protect themselves from returns fraud while still creating the best shopper experience possible.
The Result: Individualized Returns Policies
The way shoppers respond to warnings about their return behavior will vary based on their relationship with the brand and their intentions. Someone who strategically tried to commit fraud will likely accept the warning without action, taking their fraudulent activities elsewhere where they may not be caught so quickly.
On the contrary, the top 20 percent of spenders for a given retailer may dispute a warning and become wary of the retailer’s policies moving forward. To avoid negatively impacting this important relationship, these warnings should be issued sparingly. The warnings should communicate the retailer’s gratitude for the consumer’s overall value, despite the recent excessive returns, and provide a path forward.
Finally, average consumers who may or may not have intentionally committed fraud, either by bracketing or another method, will likely change their behavior. The warning may encourage them to pay closer attention to the returns rules, regulations and policies and adjust their behavior accordingly.
Overall, individualized returns policies generated by AI have the power to reduce fraudulent returns while improving shopper lifetime value, profitability, and the in-store experience.
The End of Holiday Returns As Just ‘A Cost of Doing Business’
Returns will always be a part of the holiday retail experience and shopper journey, but they don’t have to be so costly for retailers. As the sophistication of returns fraud increases, retailers must invest in more sophisticated systems that detect and deter fraud before it happens. By using AI-based tools to review consumer behavior and swiftly communicate resulting changes in returns policies, retailers can build long-lasting shopper relationships while reducing fraudulent returns.
Michael Osborne is the chief executive officer for Appriss Retail, a company that reduces losses from retail fraud and theft while protecting the retail customer experience.
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