Retailers and their loss prevention or asset protection teams may encounter a challenging situation when confronted with retail larceny or deception. Softly confronting the issue may result in an increase in incidents, while rigorously confronting it may result in a decrease in consumer loyalty.
Although there are instances of loss that are obvious, such as a shoplifter entering a store and overtly swiping merchandise, there are also more subtle cases, such as returns fraud. In order to avoid offending loyal consumers by blanketly denying a return, retailers must approach each case with surgical precision, relying on data rather than emotion, such as in nuanced situations such as returns fraud.
Retailers are confronted with the challenge of identifying retail deception, which is becoming increasingly sophisticated, particularly in the context of returns. Retailers must adopt a more nuanced, personalized strategy for combating retail abuse and reducing overall losses in order to safeguard the retail experience and maintain a suitable balance when dealing with consumers who return items.
Retail fraud is on the rise, as indicated by the data.
The annual report of the National Retail Federation, in collaboration with Appriss Retail, indicates that the number of returns fraud and abuse cases has increased from 10.2% in 2022 to 13.7% in 2023. The impact is equivalent to a total of $101 billion in dollar losses in 2023, a significant increase from $85 billion in 2022.
The incidence of online returns fraud is on the rise as e-commerce becomes the preferred channel for consumers. According to the NRF report, online transactions amounted to $1.4 trillion in 2023, representing a 10% increase. Simultaneously, online returns increased, accounting for roughly 18% of all online transactions or $247 billion in returns.
Fraudulent e-commerce incidents involve the creation of counterfeit digital receipts that are brought to stores by bad actors in order to effectuate a bogus return. Retailers are also experiencing an increase in the number of claims and appeasements fraud cases, in which an online consumer fraudulently asserts that their purchase was damaged or not received at all in order to receive a refund or future discount. Wardrobing is another prevalent form of abuse, in which a consumer purchases an item, such as a dress, utilizes it once, and then returns it in a soiled condition.
It is challenging for a retailer to remain current with contemporary larceny and fraud attempts, and a strict, generalized policy such as “no receipt, no return” is insufficient. The policy may also cause dissatisfaction among loyal customers. This is the reason why a more adaptable and personalized approach is the most effective.
Returns policies that encompass the positive, negative, and conflicting aspects of conduct
In all verticals, retailers are confronted with the reality that their most profitable consumers may exhibit a combination of both positive and negative behaviors that can result in losses.
For instance, it can be challenging to monitor returns fraud among consumers who are classified as “good” or “bad” and those who exhibit blended behaviors. Appriss Retail conducted internal research on 20 major retailers to investigate the varying consumer behaviors associated with product returns and retailer channels. The results were as follows:
At every retailer they encounter, three-quarters of consumers who return a significant number of products are conducting themselves honestly.
Conversely, 17% of consumers consistently exhibit returns behavior that results in retail losses regardless of the location of their shopping.
Then, the situation becomes more complex, as 8% of consumers demonstrate muddled behavior, exhibiting red-flagged behavior at certain retailers but not at all other retailers.
To gain a more comprehensive understanding of this, consider a sporting goods retailer that has a customer who frequently purchases merchandise but also returns a significant number of items. Certain retailers may have systems that immediately notify them that this consumer is a “bad” or unprofitable one. Nevertheless, a more comprehensive examination of the shopper’s overall behavior may reveal a more complex situation.
On the one hand, the consumer may make numerous returns; however, they are regarded as one of the most loyal and profitable customers of the sporting goods store due to their substantial purchases. Therefore, despite the fact that the consumer has a proclivity for returning items, they are a “valuable” or “good” shopper. The data of that consumer at a hardware retailer may indicate that the shopper makes numerous purchases and returns, but the retailer primarily experiences a loss. Ultimately, the shopper exhibits mixed behavior across channels, underscoring the potential for both retailers to customize their policies to accommodate the shopper’s mixed behavior. For instance, retailers may wish to implement a more restrictive returns policy, such as a shorter return window.
Artificial intelligence has the ability to traverse diverse behavior.
AI can serve as the scalpel that enables loss prevention teams to conduct a surgical examination of each consumer and their return behavior. Retailers can identify consumers who exhibit dubious returns behavior and implement a stringent policy by meticulously examining their returns history. In the interim, a flexible policy can be implemented to maintain the loyalty of a valuable purchaser, and a consumer with muddled behavior can be addressed in a manner that is appropriate for the situation.
AI and predictive technology utilize statistical models to rapidly analyze millions of transactions and returns, thereby assisting loss prevention teams in identifying anomalous behavior that may be indicative of some of the more sophisticated fraud attempts of the present day. The technology examines data without bias, enabling staff to create a personalized, nuanced retail experience.