Devising an Optimal Returns Policy for Your Business

For many years, permissive returns policies have been the norm in ecommerce. Amid rising costs and fears of a looming recession, however, online retailers are finding they can no longer afford the level of generosity that customers have come to expect. For the post-holiday season just past, it’s estimated that the total value of returned goods will be around $171 billion. With numbers like these, it’s no wonder that most U.S. retailers were revisiting their returns policies as of late 2022.

How these changes will ultimately affect the sector remains to be seen. But as an academic researcher who has studied retail returns for more than 10 years, I can make one prediction with certainty: Making returns more costly — in terms of money and/or time — will take a toll on sales, especially in online retailing and more so in certain product categories. Devising a new returns policy, then, entails managing a series of trade-offs that vary from one business to the next.

Under lenient returns policies, retailers tend to be too quick to blame high returns on so-called “opportunistic renters” — consumers who buy with the intention of returning the item rather than keeping it. According to the National Retail Foundation, only 11% of returns nationwide fall into this category. This underscores the sector’s thorny dilemma. When adding restrictions and costs to the returns equation, retailers should remember that the vast majority of consumers have good reasons for returning items. Policies should be designed with fairness rather than mistrust in mind.

A mistake in this area can have harmful consequences. Just ask Best Buy, Victoria’s Secret and other big-box brands that received scathing press in 2018 for using third-party AI software designed to flag and ban fraudulent returns. Unfortunately, the algorithm misidentified loyal customers as opportunistic renters, revoking their return and exchange privileges for extended periods of time. Add these reputational costs to the potential expense of purchasing and implementing a new software solution, and you have genuine reasons for retailers to think twice before adopting these technologies.


Instead of automatically classifying buyers based on their behavior, a perhaps less controversial idea is offering discounts in exchange for waiving the right to free returns. I term this strategy customer self-selection. The ecommerce website, which was sold to Walmart in 2016, used this strategy — just to name one example.

My research (done with various co-authors) shows that self-selection is at least as profitable as algorithmic identification for product categories where renters’ willingness to pay for temporary use of an item is relatively low. For example, high-end fashion brands offering clothing that might be (opportunistically) rented for one special night out would be a better candidate for third-party software than a less expensive clothing line. Some product types (e.g. electronics) should not be open to self-selection, especially when there is high potential for rental demand, because they lose too much value in the returns process.

As in other papers of mine, the post-return resale price or clearance price of an item emerges as a critical factor. Obviously, retailers can generally recapture more value from returned items when they can charge more for them — either by negotiating better deals with clearance partners (such as TJ Maxx for apparel and for home furnishings) or setting up their own clearance outlets.

For example, Nordstrom’s off-price subsidiary Nordstrom Rack allows for more control over clearance pricing, especially since resale items are retained under the umbrella of Nordstrom’s strong brand. My research confirms this intuition that brands can charge more for returned items by selling them at their own clearance outlets rather than through store-clearance.

Resale price also comes into play when considering the strategic behavior of online buyers. With ecommerce, virtually everyone has the tools to be a sophisticated shopper, comparing brands and prices with ease and carefully timing purchases so as to pay less.

In a flexible-returns environment, consumers are aware that full-price items are more likely to boomerang and become available at clearance prices. In other words, lenient returns policies can incentivize savvy shoppers to wait rather than impulse buy. So while tightening returns policies (all else being equal) lowers sales, it’s not necessarily the case that lenient returns policies always boost sales — thanks to the wait-and-see shopping behavior I just described.

Resale prices are one key lever retailers can use to make the best of this situation. Inventory is a separate but related lever. Work by other researchers indicates that low-inventory signals — such as messages on product pages saying “stock is running low” or, better yet, providing the exact number of remaining items — create a “scarcity effect” that can motivate sales.

Abundant inventory, on the other hand, is likely to trigger wait-and-see behavior, since it will be easier for a deferring customer to find a product at a lower price. Lenient returns policies make this effect even worse. Essentially, initial price, inventory and returns policies are all interrelated and have to be decided in an integrated framework.

Setting the ideal returns policy for your business, therefore, will require working across marketing and operations silos. Retailers may also benefit from revisiting their returns policies far more often than most currently do. Properly addressing the complex trade-offs involved in returns could have a material impact on profitability in our increasingly challenging online retail environment.

Mehmet Altug is an Associate Professor of Operations Management at the School of Business at George Mason University. Prior to joining George Mason, he was a faculty member of the Decision Sciences department at the George Washington University School of Business. He has a PhD in Decision, Risk and Operations from Columbia Business School. His research interests are in the areas of pricing and revenue management, retail operations and supply chain management. His recent work studies how consumer return policies affect retail operations, including retailer’s inventory and pricing decisions, and how retailers decide on their return policies in the context of opportunistic and strategic consumer behavior.

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