Efficient and easy product return processes have become an important part of customer loyalty, with many consumers favoring retailers based on how hassle-free the return experience feels. While this can build loyalty, product returns highlight a deeper disconnect between what customers expect and what they actually receive. That gap is costly, but the rise of artificial intelligence is helping businesses transform returns from a painful expense into a powerful driver of growth.
“Across US e-commerce, the average return rate is nearly 17%,” explains Rich McMahon, CEO and founder of cda Ventures. “But in apparel and footwear, it’s much higher, closer to 24 to 26%, with some retailers even seeing 30 to 50% during peak seasons.” In 2023 alone, apparel and footwear returns totaled an estimated $38 billion in the United States, with processing costs running another $25 billion.
One of the most common drivers of returns is “bracketing,” where shoppers order multiple sizes or colors with the intention of sending most of them back. Popularized by Zappos’ early free-return model, this practice is now standard across e-commerce. While convenient for consumers, it leaves retailers absorbing the costs of logistics, restocking, and unsellable merchandise. “Fit, color mismatch, and quality issues remain the top reasons customers send products back,” McMahon explains. “The challenge for retailers is that these issues often stem from upstream problems, whether inconsistent sizing charts, misleading imagery, or gaps in product descriptions. AI gives them a way to finally address those root causes.”
Turning Returns Data into an Asset
McMahon points to new platforms like Newmine, an AI-powered SaaS solution he advises, as an example of how returns data can be leveraged. “It analyzes product data, sales, return codes, and even customer reviews through natural language processing,” he says. “The insights help retailers pinpoint why items are coming back and act on those findings quickly.” The shift is significant: instead of treating returns as a cost center, AI allows retailers to mine them for insight. Patterns in returns data can highlight systemic quality issues, reveal where marketing language is creating false expectations, and even guide packaging redesigns. “Returns data itself is a gold mine if retailers know how to mine it,” says McMahon.
Enhancing the Customer Experience
When shoppers feel more confident in their choices, they are less likely to send products back. Artificial intelligence supports this with smarter product descriptions, personalized sizing guidance, and augmented or virtual reality tools help set clearer expectations. “Reducing returns isn’t just about cutting costs — it’s a growth strategy,” McMahon says. In categories where one in four items comes back, even small improvements can translate into massive margin gains. Retailers who deploy AI to predict, prevent, and learn from returns are building smarter, leaner, and more trusted brands.
From Problem to Growth Strategy
McMahon’s career has been built on transformation, from scaling Bed Bath & Beyond into a $12 billion multi-brand powerhouse to advising early stage AI and fintech ventures. In his view, the retailers that will thrive are those that reframe challenges like product returns as opportunities for innovation. “AI gives retailers the power to predict, prevent, and learn from every return,” he says. “It’s not optional anymore, it’s essential. Returns are one of the best places to start because every improvement has an outsized impact on profitability and customer loyalty.”
To learn more about Rich McMahon’s work, connect with him on LinkedIn or visit his website.
