‘No Receipt, No Return’ Policies Are Major Missteps for Retailers

return policy

“No receipt, no return.” This common retail policy is designed to protect retailers from unnecessary loss by eliminating the opportunity for consumers to return shoplifted items for a profit. And, with 37.9% of retailers reporting that returns fraud has become a bigger threat to their businesses in the last five years, policies like this are on the rise. However, the unintended consequences of these restrictions can result in more damage than protection. 

To prevent return fraud and abuse without punishing honest customers for the deeds of a few bad actors, retailers must turn to artificial intelligence. With AI, retailers can create fine tunable return models that protect retailers from a wide variety of theft, fraud, margin erosion, and abuse, without hindering the returns experience for your best customers.  

The Reasons Behind ‘No Receipt, No Return’ 

Across the retail industry, returns are on the rise. The recent Returns as an Engagement Strategy report published by Appriss Retail and Incisiv found that returns increased by 78% since 2020. And with that, means a growing number of fraudulent returns too. In fact, NRF and Appriss Retail found that 10% of retail returns are fraudulent, costing the industry upwards of $85 billion a year. 

These findings highlight the importance of minimizing returns, especially fraudulent ones. To crack down on these profit-threatening trends, retailers are taking drastic measures including instituting strict new policies like “no receipt, no return.” 

The Negative Impact of Strict Policies

Whether retailers are limiting returns windows or requiring receipts, strict return policies are detrimental to a retailer’s reputation. 

Restrictive return policies can discourage shoppers from making a purchase. For example, when a new customer sees a confining policy in the fine print, they might take their business elsewhere in favor of an option that’s less binding. Similarly, if a long-time customer is penalized for losing one receipt, their ongoing loyalty might be discouraged and their trust diminished. 

As a result, retailers must find an alternative for restrictive policies like “no receipt, no return.” Fortunately, artificial intelligence offers a path forward. 

The Benefits of Fine Tunable AI Models

Retailers should implement AI-based return models that mitigate loss while simultaneously bolstering the shopping experience for profitable shoppers. Once integrated, the AI engine can begin analyzing a variety of data for each shopper. The considerations include the frequency of returns, preferred returns channels, average basket sizes, and any history of fraud or abuse. Then, the engine will recommend in real time a personalized return recommendation for each shopper. 

Imagine a shopper that is known to buy six items and return five a few weeks later, likely after wearing them. In this scenario, the AI technology may recommend a restrictive policy that limits the number of returns the shopper can make per year. The shopper will be notified before their next purchase, giving them the opportunity to continue buying from the retailer without abusing returns. 

On the contrary, for a shopper who makes frequent purchases with large basket sizes yet only returns a few low-cost items per year, they would be offered an extra lenient policy. This unique experience could include a lengthy returns window and no receipt restrictions. In addition, the retailer can offer personalized incentives when this profitable shopper does make the occasional return. 

For example, the AI-engine could offer a discount code for 20% off a purchase made the same day as the return. This extra initiative to save the sale could turn a potentially disappointing shopping trip into an opportunity to further connect with the shopper and ensure ongoing loyalty. 

Finally, by taking an AI-driven approach, these policies can adjust as the customer’s behavior changes. If someone that originally earned a lenient policy begins taking advantage of the retailer, they will receive warnings that encourage them to be more cognizant of the rules. 

Overall, AI-driven policies can improve customer relationships while protecting the retailer from fraudulent and abusive returns. 

Lean Into AI-Driven Fraud Detection

Returns are a costly fact of doing business for retailers, but that doesn’t mean they have to accept fraudulent or abusive returns. With behavior-based, AI-driven fine tunable models for returns, retailers can ensure every customer is treated fairly. 

Top shoppers will benefit from loose policies and rewards while customers intent on harming the business will be turned away. With AI, the returns process can be optimized to improve the retailer’s reputation, profits, and customer lifetime value. 

— Pete Barker, Director of Product, Appriss Retail 

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