Why it matters:
- Product returns totaled an estimated $849.9 billion in 2025, according to the National Retail Federation.
- Retailers are using AI to steer customers to the right choices before they buy, discourage returns by charging fees in certain instances, minimize losses by optimizing the logistics of product returns, and spot the signals of potential fraud.
- Flux Footwear was able to reduce return rates by as much as 7%, to 15.9%, and apparel retailer Piper & Scoot achieved a 63.5% retention rate—the rate of customers who choose an exchange instead of a refund—with an average of $55 retained per transaction, after both companies deployed new, technology-driven returns solutions.
Retailers are tackling the costs of product returns with policies and technology that seek to prevent returns proactively and identify fraudulent returns before they occur.
Companies including Flux Footwear and Piper & Scoot are among the brands that have implemented solutions that are having an impact on the companies’ performances when it comes to product returns.
Retailers are increasingly using artificial intelligence throughout both the online purchasing and returns processes, leveraging data to make it more likely that customers are buying a product they will keep. In addition, increasingly sophisticated data analytics are helping retailers detect fraud and navigate the most cost-effective options for handling each return.
One of the key ways that brands are seeking to minimize returns is by charging fees—at least to some customers—for returns, although that can be a double-edged sword.
Returns totaled an estimated $849.9 billion in 2025 for retailers, according to the National Retail Federation (NRF).
The outlook for returns going forward remains challenging, as younger consumers are much more likely to return products than older shoppers. Consumers between the ages of 18 and 30 accounted for an estimated 7.7% of online purchase returns in the last 12 months, more than any other generation, according to the NRF.
[Read more: 3 Businesses Reveal How They're Using Ultramodern Tech in Niche Ways to Drive Sales and Efficiency]
Leveraging AI to tackle retail return fraud
NRF research also found that 9% of all returns are fraudulent, and not surprisingly, retailers are turning to technology to combat fraudulent returns. Eighty-five percent of retailers report using AI or machine learning in their returns process to identify and combat fraud, the NRF research found.
AI and machine learning are especially promising in the effort to minimize the cost of returns because of the number of variables involved in each individual return. Starting before the purchase is even completed, technology can help retailers ensure that customers are making the right purchase by analyzing data around that customer’s past purchases and those made by other customers, based on sizing and fit, for example, at least when it comes to apparel.
When the customer does decide to return a product, a host of other variables come into play. The retailer must decide whether it is more cost-effective to have the customer return a product or just have them keep it, and if they do return it, where does the company have the customer send it. Retailers can also decide whether to issue a refund or a credit.
“There is a lot of intelligence around those kinds of things, and retailers are getting smarter about it,” said Robert Johnson, Executive Vice President at ReturnPro.
AI is also helping retailers make decisions about the products they get back, such as whether or not they can be refurbished and resold, resold as is, or discarded, for example.
“We’re finding new ways of reselling [returned] products, so there’s more products that end up getting a second life, and AI really plays into all those different areas,” said Johnson.
As the value of some products—such as seasonal items or fashion—can decline in a matter of days or weeks, AI and machine learning are helping retailers make decisions much more quickly to optimize their potential resale value and minimize the overall costs of handling the return, Johnson said.
[Read more: Retailers and Brands Large and Small Unlock (AI-Aided) Opportunities in a Challenging Pricing Environment]
An AI-enabled ‘customized decision tree’ to personalize the return process
“It's almost an infinite decision tree at the time the customer wants to return something,” said Blaine Nielsen, President of Retailers at Rithum, which provides commerce solutions for businesses. “AI allows you to personalize the [return] process and take all of the criteria into account—the individual who's returning it, the item that’s being returned, the time of day, the location and [other criteria] to have a more customized decision tree.”
Said another way, AI allows retailers and brands to evaluate all of these variables at once to determine the outcome that best balances cost, customer value, and operational impact for that specific return.
Adjusting product return policies in real time to thwart fraud risk
At the same time, retailers seeking to minimize their own costs are also weighing the impact that their returns policies and practices have on customer retention.
And when companies are able to identify some shoppers as fraud risks, they can “create a little more friction in the returns process,” such as charging a fee for returning merchandise, Nielsen said.
“I think we’re still in the early innings, but what's happening is that companies are attempting to make those decisions in real time instead of looking at a month’s worth of returns, and after the fact, trying to identify what was fraud,” he said.
“The ideal is that companies can identify at the time the [customer] is actually purchasing at the shopping cart, what is the risk of the product being returned, and any indications that this is a higher-risk transaction.”
Retailers are using AI to steer customers to the right choices before they buy, discourage returns by charging fees in certain instances, minimize losses by optimizing the logistics of product returns, and spot the signals of potential fraud.
Flux Footwear reduces returns with automation
One example of a company benefitting from automation in the returns process is Flux Footwear, which makes athletic shoes through its own retail website.
The company had been experiencing return rates of 18%–23% but was able to reduce the rate by as much as 7%, to 15.9%, using a solution from ReturnGO that enables the retailer to define which specific products, orders, and customers are eligible for returns under various scenarios, according to a ReturnGO case study.
A key element of the revamped return strategy involves incentivizing product exchanges and discouraging refunds by charging a return fee for refunds. The primary reason that Flux Footwear customers return products is that items do not fit properly, cited by 78.3% of customers who make returns, so encouraging exchanges rather than refunds makes sense, the case study explains. Now about half of all returns (49.4%) are exchanges, rather than returns.
Flux Footwear’s website, which has a dedicated portal for returns, states that it charges a $13 return handling fee to customers who ask for a refund, but product exchanges, or returns that are exchanges for a Flux Footwear gift card, are free. The company encourages customers to try the shoes on while wearing socks and testing them on a carpeted surface to ensure they can be returned in like-new condition.
The strategy of minimizing returns by charging customers a fee has been gaining traction. According to the NRF report, 72% of all merchants surveyed have starting charging customers for at least one return option during the last 12 months, up from 66% in the previous year’s survey.
Charging customers a fee for returns poses some risks for brands, however. While 53% of merchants that have implemented fees for returns reported a reduction in overall return rates, 37% said they have lost customers over return fees, and 24% reported a decrease in sales.
Meanwhile, 71% of shoppers said that if they have a poor returns experience, they are less likely to shop with that retailer again.
Piper & Scoot incentivizes product exchanges to minimize return costs
Apparel brand Piper & Scoot, a fashion startup that specializes in the direct online sale of dresses and other apparel and accessories, seeks to provide customers with options for executing returns. At the same time, however, it guides customers toward exchanges rather than refunds, according to a case study from returns solutions provider Loop.
In order to incentivize product exchanges and discourage refunds, Piper & Scoot offers a 10% bonus credit for customers who choose to exchange an item and charges a fee of $10.95 for refunds. Customers can also select a store credit without paying a fee.
The policy also incorporates some customizations for certain situations. High-value shoppers never pay a fee, even if they request a refund, and refunds are not permitted on heavily discounted orders.
The result has been a 63.5% retention rate—customers who choose an exchange instead of a refund—with an average of $55 retained per transaction, according to the Loop case study.
Appliance brand cuts waste with (AI-powered) resale solutions
Companies are also seeking to optimize revenues through the returns process by using AI-powered analytics to steer product returns toward resale opportunities rather than discarding them.
A small appliance brand that supplies product to a variety of retailers previously had a “destroy in field” policy with some of its retail partners, which called for them to discard product rather than return it.
By implementing a data-driven solution from ReturnPro, the company unlocked $1.6 million in annual net recovery, along with insights that could result in “substantially more,” according to a case study published by ReturnPro.
The appliance company ended its destroy in field policy and instead used cost analytics that considered logistics, refurbishment, and resale. The company was able to route each returned item to the highest-value outcome and resell recovered products through multiple marketplaces. The appliance brand expects to process more than 127,000 items through the new return model in the first year, according to the case study.
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