Rethinking Returns in a Data-Driven Retail World

Everyone knows product returns are a problem, but now the National Retail Federation (NRF), in research with Appriss Retail, has spelled it out in stark terms: total returns for the retail industry amounted to $743 billion in merchandise in 2023. In addition, the total return rate as a percentage of sales in 2023 was 14.5 percent. Of that figure, the research shows that $101 billion are fraudulent returns, or 13.7 percent of total returns.

Retailers have done much over the years to mitigate the problem — e.g., investment in circular schemes that see returned goods offered for sale secondhand. However, there's as yet no evidence that they offset lost margin, even allowing for the high cost of setting up and running these schemes. Or "Try Before You Buy" services, available to Amazon Prime members, which may be attractive to customers but aren't profitable and may influence customers to use it as a short-term rental service. Both these schemes are also affected by the fact that up to 70 percent of products will never return to stock in the first place, and so are not available for reuse.

Even charging for returns will not cover all potential losses; after all, the outward delivery cost cannot be recovered, and the returned goods are either left with the buyer, disposed of, or resold, but usually for less than the original price. Moreover, in a survey by SAP Emarsys, 88 percent of U.S. consumers stopped shopping with a retailer when it introduced a paid returns policy.

The bottom line is that retailers still lack a cross-divisional returns strategy that can see and add up all the costs and impacts. Meanwhile, their costs keep climbing as they have to make returns easy for customers to compete; 54 percent of customers said the return policy now influences their decisions about where to shop.

Change begins with a review of current processes. For instance, are serial returners loyal and high-net-worth customers, or are they one-off buyers? Unless this information is known, it becomes impossible to create a sliding scale for treating each customer cohort appropriately and to continue giving valuable customers an even better returns experience while discouraging the casual purchaser from taking advantage of overgenerous returns policies.

When all aspects of returns are considered, it's clear they're a significant margin killer. This is made even worse during an economic period when the customer is more price-conscious, yet the retailer’s cost of service continues to rise due to inflation.

A major part of the answer lies in retailers’ hands and the data they already hold. They must have access to more granular returns data, to the level reported by PowerReviews, which looked into why consumers return and found that the top three reasons were: item doesn't fit, item doesn't match description, and don’t like the item(s).

Armed with deep data, retailers can then develop software that can deliver value across the whole IT ecosystem, not just at single points of distress. Once retailers better understand customer preferences, they can then get a much higher reduction in returns from point solutions that measure and profile body size, ensuring that, in a world of nonstandard sizing, customers get the product that they want to wear and keep. Retailers can complement this by providing personalized search and merchandising, filtering to display only relevant products, and improving description quality using generative AI to tackle the problem of returns at source — at the point of product discovery.

These initiatives will then pay dividends on the logistics side, including the development of systems to handle returned stock that can’t be resold, reporting tools for third-party delivery companies in order to speed up the returns process, and possibly an intelligent calculator that increases the price of certain items in recognition of their vulnerability to being returned.

A review of all the top statistical sources will always show that the problem of returns is becoming more burdensome. At the same time, retailers are competing not just with their existing direct competitors and category-agnostic marketplaces, like Amazon.com, but also with new entrants into their home territories who have the resources to buy their way into the market.

A successful result depends on partnering with a third-party technology company with domain expertise, developing cutting-edge, headless solutions that remove the cost and burden of building these capabilities in-house, and a willingness to work with existing apps where their capability can be enhanced rather than replaced.

Alex Kulinchenko is growth enablement director, retail, at Intellias, a global software engineering and digital consultancy company.