A returnless refund—the practice of refunding or replacing a consumer’s purchase without requiring that the item itself be returned—has been an unpublished option that retailers have used at their discretion for many years. The decision is gaining popularity due to the surge in ecommerce; retailers are using it to avoid high return shipping and handling costs, especially if the item is of low value or is damaged.
The latter was my first experience with a returnless refund. The left ear bud from an expensive set arrived dead out of the box. Upon calling the support line, I was told to take a “before” photo, smash the ear bud to bits, take an “after” photo, and then email the images. My replacement arrived in a couple of days. Like 50% of the people who experience this type of service, I appreciated the ease and would purchase from the brand again. As someone who writes about retail return fraud, though, I was curious.
What are some of the risks a retailer faces when choosing to offer returnless refunds?
- Collusion – Store associates aren’t the only employees who could engage in sweethearting. Consumer support representatives in the call center could inappropriately offer the benefit to people they know.
- Discount seekers – Not wishing to pay full price, a consumer claims that a piece which arrived in pristine condition is damaged and wants a discount to keep it. Getting the item free is a bonus they won’t forget!
- Repeaters – Having discovered the option, the consumer tries to trigger the offer again with the goal of either accumulating more of the product for personal use or for reselling on the secondary market.
- Return fraud – When the consumer possesses two of an item and wants only one, he or she may try to return one of the items to the store in hopes of making a profit.
How can the risks be managed?
Retailers set their guidelines for when the option can be offered. It may be an option when the price of the item plus return shipping and handling are more than the retailer can recoup if they resell. Overstocked or unseasonal inventory can also affect the decision; a pair of $100 sandals with a $20 margin that would get shipped back in August would be received when the sandals are being sold for a fraction of their original price. It may not be profitable.
A percentage of consumers will take advantage of an opportunity to get free goods, and a percentage of employees will help them.
Profitability aside, however, retailers don’t want to train consumers to expect free merchandise. That is why there may be more involved in the decision than mathematics. Does the retailer know the consumer and track all their in-store and online refunds? Have they or someone in their household received a similar consideration recently? Is the consumer a high-value shopper? Can they prove the item is unusable or unsuitable? (My ear bud example is a case in point, where a photo of the unusable item was proof enough.) Unfortunately, this level of one-to-one attention may not scale, particularly if online returns remain high as they were in 2021 when 20.8% of ecommerce purchases were returned for some reason.
Even with this information, a consumer support representative probably will not know the full scope of the consumer’s history with the retailer and make an informed decision without the aid of technology.
Exception Based Reporting (EBR) can be used to uncover collusion through the call center in the same way it reveals collusion in the store. The retailer needs to make the data available to the EBR system and develop appropriate baselines for comparisons. Appriss® Secure® is an example of a widely-used exception analytics platform designed to find employee-based loss.
A consumer support representative probably will not know the full scope of the consumer’s history with retailer and make an informed decision without the aid of technology.
Consumer-focused artificial intelligence (AI), like the technology in Appriss® Engage®, examines the consumer’s in-store and online purchase and return behavior in real-time and recommends a course of action. It quickly uncovers patterns of risky behavior as demonstrated by the “repeaters” and “return fraud” above, and to a lesser extent the “discount seekers.” Similar to solutions that prevent chargebacks, AI can be used to minimize the impact of returnless refunds while still keeping consumer loyalty high.
Returnless refunds are sometimes worth the risks
In a news segment on the NBC Today Show, Amazon and Walmart were quoted as saying that they want consumers to have a positive experience; this may include a returnless refund. With consumers reining in their spending, goodwill can go a long way in maintaining loyalty. At the same time, however, retailers know that a percentage of consumers will take advantage of an opportunity to get free goods, and a percentage of employees will help them. Technology can help reduce the risks, and let the retailers do what they do best—please consumers.
Carrie Cassidy, Director, Marketing, Appriss Retail
A technology advocate for more than 25 years, Carrie makes information about advanced data analytics solutions accessible to retail professionals through a variety of media. She has written numerous white papers, case studies, and articles for a variety of industries ranging from motion control to human resources.