No easy answers for retailers’ returns dilemma

Mehmet Altug, an associate professor of operations management, has been researching retail returns policies for a decade. The issue has recently come to prominence, as the lenient policies of online retailers have led to skyrocketing return rates (now exceeding 20 percent in the U.S.). Altug’s various academic papers delve into the difficult trade-offs retailers face when setting returns policies. While there are no easy answers, Altug’s research identifies factors that can help retailers achieve more strategic flexibility.

Who’s smarter—online shoppers or their counterparts in physical stores? On the one hand, e-commerce consumers have a huge advantage in comparison shopping. They can cross-reference products, learn about competing brands and scout for deals in minutes from their living room couch. However, basic physical features of a product—its size, texture, fit, etc.—aren’t represented well on digital devices. That’s why purchasing items such as clothing online can be a stab in the dark.

Retailers have used lenient return policies to counteract e-commerce’s sensory-deprivation problem. Such policies effectively create an antidote for buyer’s remorse, so that consumers can confidently click “Buy Now” for an item they’ve never seen or used. But the industry’s permissiveness regarding returns may be backfiring.

According to the National Retail Foundation (NRF), the average rate of return for online purchases in 2021 was 20.8%, up from 18.1% the year before. The total value of returned items for 2021, according to retailers’ estimates, was more than $761 billion.

Mehmet Altug, an associate professor of operations management at George Mason University's School of Business who has been researching pricing and retail operations—and consumer returns in particular—for over a decade, says, “When I first started doing research on this, I used to say up front ‘pricing is the main decision, returns are just something to deal with.’ But now, it’s becoming a primary problem for retailers.”

The main reason why return management does not have a well-defined solution is that it can be seen as a "double-edged sword." While flexible consumer return policies increase our valuation and willingness-to-pay for the product (a positive effect), they also lead to more returned units, which are ultimately sold by retailers at a loss (negative effect).

Therefore, we see a wide range of return policies in the industry. Recently, more and more brands have been tightening up their return policies. For example, Banana Republic, J. Crew, Old Navy and Gap have shortened their grace period to one month; in the U.K., Zara recently started charging for items returned through third-party drop-off points. But it remains to be seen whether online customers, with their penchant for comparison shopping, will accept this austerity drive, or flock to competitors with more permissive policies.

As Altug’s published research shows, the strategic buying behaviors of online customers further complicate retailers’ returns dilemma. His 2016 paper in Manufacturing & Service Operations Management (co-authored by Tolga Aydinliyim of Baruch College) details how offering lenient refunds can entice buyers to purchase items earlier, instead of waiting for them to go on sale. Forward-thinking consumers, however, will anticipate that liberal return policies will lead to more items being returned and marked down for resale. This expectation would increase wait-and-see behavior, as consumers monitor for products to become available at clearance prices. In short, lenient return policies can (again) cut both ways in the presence of strategic customers, either raising or lowering profitability and depending largely on the salvage prices resold items can command through off-price outlets such as TJ Maxx.

These outlets are where department stores turn to offload their unsold inventory, including returned items. And like department stores, they range from low-end (e.g., TJ Maxx and Marshalls) to upscale (e.g. affiliated discount branches such as Saks on Fifth and Nordstrom Rack). Altug’s paper argues that the refund offered for a product is closely related to the resale value of the item, and the partial refund offered should not generally exceed that. The best case for retailers would be a no-returns policy on expensive items with low resale values, since these items would be most subject to the wait-and-see effect among strategic online consumers.

However, retailers are rightfully cautious about the customer defections that may result from restrictive returns policies. Altug’s research finds several alternatives they could explore to preserve profitability in the face of the returns dilemma. For example, if the retailer can enjoy profitable clearance sales (i.e. resale value is greater than the cost of the product) or if the lenient return policies stimulate additional demand, then the retailer may offer full refunds. Similarly, to sustain a lenient return policy, the retailers can lower transaction costs for the consumer by providing return labels in packages. Moreover, in the presence of competition, Altug finds that the retailers’ ability to clear inventory at a higher price (maybe through a reputable clearance partner) will also help them offer higher refunds, stock and sell more at a higher price and make more profits compared to their competitors who do not have the same higher resale value advantage.

The 2016 paper assumes that consumers are making choices in good faith. But free and easy returns also invite opportunists whose intent is to use online retailers as a de facto rental service. Though this class of bad actors is relatively small–responsible for about 11 percent of returns nationwide, according to NRF estimates–it can do significant damage within the low-margin retail industry.

Altug’s 2021 paper in Management Science (co-authored by Tolga Aydinliyim and Aditya Jain of Baruch College) tests the respective profitability of two approaches retailers employ to manage opportunistic returns. First, some retailers use data analytics to identify renters based on their buying and return behaviour and target them through more restrictive returns policies. While mostly effective, this solution carries a risk of false positives, which could unfairly target valued customers. Making inferences about consumer intent from purchase and return information alone can be a dicey prospect. Second, online retailers can deploy price/refund pairing as a sorting mechanism, charging less for an item (e.g., a three-percent discount)–if the buyer agrees to waive free returns. That way, renters self-select to pay up front for their intended temporary use of the product and the honest majority are rewarded, at least initially, with a lower price.

Again, resale price surfaced as a pivotal factor in Altug’s findings alongside renters’ valuation. The self-selection approach works just as well or better than data-driven targeting in a market with low-rental-value potential or when the retailer sells returned units at a low resale price even in a medium-rental-revenue-potential market such as electronics or fashion products.

While there are no easy answers for retailers, Altug’s research maps out the main elements they should consider when setting their returns policy. “There are several issues that need to be considered in this context and still not very well-defined solutions that could address all of them,” Altug says. “One thing is for sure, you can’t just give maximum flexibility and forget it. Retailers need to find the right balance and that is not a trivial task.”