Consumers differ from one another: Embracing a nuanced strategy against retail fraud

For retailers and their loss prevention or asset protection teams, addressing retail theft or fraud can be a sensitive matter. If you tackle the problem gently, there may be a rise in incidents; if you confront it rigidly, customer loyalty could decline.

Although there are obvious instances of loss, such as a shoplifter walking into a store and overtly taking items, many cases, such as returns fraud, can be less apparent. In complex scenarios such as return fraud, retailers must handle each instance with meticulous care based on data rather than emotion to avoid alienating loyal customers through blanket return rejections.

Retailers face the challenge of increasingly sophisticated retail fraud, particularly regarding returns, which complicates detection. In order to safeguard the retail experience and achieve equilibrium in their handling of returning customers, retailers should adopt a more individualized and subtle strategy for combating retail abuse and minimizing losses.

Data indicates an increase in retail fraud According to the National Retail Federation’s annual report, produced in collaboration with Appriss Retail, the incidence of returns fraud and abuse has risen from 10.2% in 2022 to 13.7% in 2023. This impact corresponds to total financial losses amounting to $101 billion in 2023, an increase from $85 billion in 2022.

With the rise of e-commerce as a preferred channel for consumers, incidents of online return fraud are also increasing. According to the NRF report, online sales grew by 10% in 2023, reaching a total of $1.4 trillion. Concurrently, online returns surged, accounting for almost 18% of all online sales, or $247 billion in returns.

Fraudulent e-commerce occurrences encompass the generation of fake digital receipts, which malicious individuals present at stores to carry out a bogus return. Retailers are witnessing an increase in claims and appeasement fraud, where online customers deceitfully assert that their order was either damaged upon arrival or didn’t arrive at all, in order to obtain a refund or a future discount. Another well-known form of exploitation is wardrobing, in which a customer purchases an item (e.g., a dress), wears it once, and then returns it after having used it.

For retailers, keeping pace with contemporary theft and fraud attempts is challenging, and relying solely on a strict blanket policy such as “no receipt, no return” is insufficient. This policy might also alienate loyal shoppers in the process. That is the reason why a more flexible and individualized approach is most effective.

Returns policies that address the good, the bad, and the mixed behavior Retailers across all sectors face the reality that some of their most profitable consumers may demonstrate a combination of good and bad behaviors that can affect loss.

For example, monitoring returns fraud among customers categorized as “good” or “bad,” as well as those exhibiting mixed behaviors, can be challenging. Appriss Retail carried out an internal investigation of 20 major retailers to examine variations in consumer behavior regarding product returns and retailer channels. The findings included:

75% of consumers who return a large number of products do so honestly at all the retailers they meet.
Conversely, 17% of shoppers exhibit a consistent pattern of return behavior that results in retail loss at all the places they shop.
Then, it becomes nuanced: 8% of consumers display mixed behavior, demonstrating red-flagged actions at certain retailers but not at all the retailers they frequent.
For further comprehension, think of a sporting goods retailer and a customer who often purchases items but also returns many of them. Certain retailers might possess systems that provide immediate notifications identifying this shopper as “bad” or unprofitable. However, a more nuanced picture might emerge from a deeper examination of that shopper’s overall behavior.

That shopper might return items frequently, but due to their high volume of purchases, they are regarded as one of the store’s most loyal and profitable customers. Thus, although that shopper enjoys returning items, they are considered a “good” or valuable shopper. Examining the behavior of that shopper at a hardware store, their data may indicate that they make numerous purchases and returns, but the retailer often incurs losses as a result. The shopper ultimately exhibits mixed behavior across channels, demonstrating how both retailers can customize their policies in response to that shopper’s mixed behavior. As an example, the retailers might consider providing a returns policy with less flexibility, like a shorter return period.

AI can cut through mixed behavior To aid loss prevention teams, AI can serve as a scalpel, helping them to examine each shopper and their returns behavior with precision. Retailers can identify consumers who exhibit questionable returns behavior and apply a stringent policy to them by carefully analyzing the returns history of each shopper. In the meantime, a flexible policy can be offered to a valuable shopper to keep their loyalty, while a consumer exhibiting mixed behavior can be managed in the middle ground.

AI and predictive technology utilize statistical models to rapidly analyze millions of transactions and returns, aiding loss prevention teams in pinpointing atypical behavior that may represent some of the more advanced fraud attempts currently occurring. The technology processes data without prejudice, aiding staff in creating a personalized and nuanced retail experience.