Responding to retail theft or fraud can be a delicate issue for retailers and their loss prevention or asset protection teams. Approach the problem softly and incidents may increase; confront it rigidly and shopper loyalty could decline.
While there are blatant cases of loss, like a shoplifter entering a store and openly stealing merchandise, many cases like returns fraud can be more subtle. In nuanced situations like returns fraud, retailers need to approach each case with surgical precision, relying on data and not emotion, so as not to offend loyal shoppers by blanketly denying a return.
The challenge for retailers is that retail fraud continues to get more sophisticated, especially when it comes to returns, making it harder to identify. To protect the retail experience, and strike a balance in how they address shoppers making a return, retailers need to implement a more personalized, nuanced approach to how they fight retail abuse and reduce losses overall.
Data reveals a rise in retail fraud
The National Retail Federation's annual report, in partnership with Appriss Retail, shows that the number of returns fraud and abuse cases have increased from 10.2% in 2022 to 13.7% in 2023. The impact equates to total dollar losses of $101 billion in 2023, up from $85 billion in 2022.
As e-commerce grows as a channel of choice for consumers, so do cases of online returns fraud. The NRF report said online sales increased by 10% in 2023, totaling $1.4 trillion. At the same time, online returns increased, representing nearly 18% of all online sales or $247 billion in returns.
Fraudulent e-commerce incidents include creating counterfeit digital receipts that bad actors bring to stores to execute a phony return. Retailers are also seeing more cases of claims and appeasements fraud, where an online shopper falsely claims their purchase arrived damaged or not at all to receive a refund or future discount. Another popular instance of abuse includes wardrobing, where a shopper buys an item like a dress, wears it once, and returns it used.
Trying to keep up with modern attempts of theft and fraud is tough for a retailer, and just having a strict, blanket policy like, "no receipt, no return," doesn't do enough. The policy can also upset loyal shoppers in the process. This is why a more flexible and personalized approach is what works best.
Returns policies that address the good, the bad, and the mixed behavior
The reality for retailers of all verticals is that some of their most profitable consumers may exhibit a mix of good and bad behaviors that can impact loss.
Case in point, tracking returns fraud among shoppers deemed "good" or "bad" and shoppers with mixed behaviors can get tricky. Appriss Retail conducted internal research of 20 large retailers to study differing consumer behaviors around product returns and retailer channels and found:
- Three-quarters of consumers who return a high number of products are doing so honestly at every retailer they encounter.
- On the flipside, 17% of consumers consistently demonstrate returns behavior that leads to retail loss wherever they shop.
- Then, it gets nuanced, where 8% of consumers exhibit mixed behavior, showing red-flagged behavior at some retailers but not at every retailer they shop.
To understand this further, consider a sporting goods retailer with a shopper who frequently buys merchandise but returns many items, too. Some retailers may have systems that instantly alert that this shopper is a "bad" or unprofitable shopper. However, digging deeper into that shopper's total behavior might present something more nuanced.
On one hand, that shopper may return a lot, but the shopper buys so much that they're considered one of the most loyal and profitable for that sporting goods store. So even though that shopper likes to return items, they're a "good" or valuable shopper. Looking at that shopper's behavior at a hardware retailer, that same shopper's data might show that the shopper buys and returns a lot but results mostly in loss for the retailer. The shopper ultimately shows mixed behavior across channels, highlighting how both retailers can tailor their policies toward that shopper's mixed behavior. For example, the retailers may want to offer a less flexible returns policy such as a tighter return window.
AI can cut through mixed behavior
To support loss prevention teams, AI can be the scalpel that assists them to surgically look at each shopper and their returns behavior. By reviewing each shopper's returns history with precision, retailers can spot a consumer with suspicious returns behavior and issue them a strict policy. Meanwhile, a valuable shopper can be given a flexible policy to maintain loyalty, and a consumer with mixed behavior can be dealt with in between.
AI and predictive technology leverage statistical models and read through millions of transactions and returns quickly to support loss prevention teams in identifying unusual behavior that could be some of today's more sophisticated attempts at fraud. The technology reviews data without bias, supporting staff to build a personalized, nuanced retail experience.