How AI Is Changing the Returns Experience

Nobody likes returns. They’re expensive for ecommerce retailers, a vector for fraud, and an environmental mess. Unfortunately, returns are a fact of life for retailers. In 2023, 14.5% of all retail purchases in the U.S. were returned, according to the National Retail Federation. Even though returns are common, they’re usually a hassle for customers. As a result, consumers tend to prefer retailers and websites that make returns easier. In ClearSale’s most recent survey of consumer attitudes on ecommerce, fraud, and CX, 87% said an online store’s return policy is a factor in whether they’ll become a repeat customer. This proves that a favorable return policy can help build long term value and brand loyalty, but on the other hand, an overly restrictive policy could be a customer deterrent. Eighty percent also said a single bad customer support experience can turn them away for good, which would be a huge blow to a retailer’s business and bottom line.  

by Rick Sunzeri

Because customer loyalty is tied to ease of returns, making returns more difficult is not a viable solution for retailers, although some are resorting to adding hurdles to control costs. One survey of apparel companies and consumers found that in 2023, 1/3 of the companies that implemented return fees lost customers as a result. Advances in AI, along with smart return-management strategies, can help retailers reduce returns without creating negative customer experiences.

Head off returns by optimizing purchases

Some brands have been innovating with AI to identify problems with products and descriptions that can lead to returns. For example, H&M analyzes sales and returns to gain insight into which products get returned most often, to help optimize selections for products that customers are more likely to keep. 

One of the biggest challenges with shopping online is that product descriptions may not always give customers the information they need to make the best decision. That’s especially true for clothing, where sizes aren’t standardized across brands and often vary within brands. 

One option is to use an AI model built with product and customer data to generate personalized size selection recommendations as a customer shops online. Amazon recently launched an AI solution to do this, with the goal of reducing returns caused by “bracketing”–the common practice of ordering an item in several sizes and returning those that don’t fit. Another option is to analyze product descriptions for frequently returned items to identify areas where the product descriptions can be improved, so customers are more likely to be satisfied with what they order.

One retailer has found a way to reduce returns–and improve profit margins– using AI to target search ads to customers who are less likely to return products. Dutch fashion retailer Omodo built a model to do this and saw their return rate from ad-driven purchases decrease by 5%, while their profit margin rose 14%. 

Reduce the friction in your return processes

AI also has the potential to make returns easier for customers and less expensive for retailers. Advances in LLMs have dramatically improved the quality and capabilities of AI chatbots for customer service, which can include providing return authorizations and instructions. Sixty-eight percent of apparel customers surveyed by a returns service provider said they prefer getting customer support through live chat rather than by phone–a solution that can dramatically reduce costs for returns-related customer service contacts. 

Another pain point for retailers and customers is the length of the return window. While some retailers are reducing the length of time customers have to return purchases, customers want more time. A recent survey of adult U.S. apparel shoppers found that 42% would like an extended return period as a customer experience reward. That’s more than twice as many as those who want concierge services, and more than any other reward option listed in the survey. Offering extended time for returns is a smart reward strategy. Using AI to optimize the return window for a retailer’s particular customer base and product mix can make that strategy even smarter by balancing customers’ preferences and retailers’ cost-control goals.

Identify and prevent more return fraud

Creating a smarter returns process with AI also opens up new possibilities for preventing return fraud, which is a constant and growing problem for retailers. In 2023, nearly 14% of U.S. returns were fraudulent, costing retailers $101 billion. 

Some of the same strategies that can help to prevent returns can be adapted to prevent returns fraud. For example, analyzing purchase and returns data can identify the items that are most likely to be exploited by return fraudsters. Retailers can use the information to screen orders and return requests for these items more carefully, and to create item- or category-specific return policies designed to deter fraudsters, based on how they’ve been committing fraud. 

AI can also analyze customers’ return histories, individually and in batches, to identify suspicious behavior by individuals as well as similar fraud behavior patterns across groups of shoppers that can signal organized fraud activity. That data can be used in order scoring, to reduce return fraud. 

Managing returns in more effective ways

Fewer returns means lower costs for retailers, better experiences for customers, and more resources available to address the increase in return fraud. However, making returns harder for customers isn’t the solution because a good return process is part of a great customer experience. AI opens the door for retailers to prevent returns, reduce return fraud, and still allow good customers to return purchases in a way that maintains their loyalty.


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