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Emerging artificial intelligence is promising to reduce the elevated rate of returns online, with the primary focus on diminishing the likelihood of returns before the purchase happens.
According to a study published in the Journal of Retailing, 30% of e-commerce orders are returned by consumers versus 9% for brick-and-mortar stores.
Much of the technology focuses on improving recommendations, supported by a host of virtual try-on features available for products such as apparel, eyewear, footwear, and cosmetics. These features allow consumers to envision how a product will look on them before making a purchase, reducing the risk of disappointment and returns.
A McKinsey study found that a staggering 70% of all online returns across fashion categories are due to size, style, and fit issues.
With the benefit of AI, MySizeID — an omnichannel e-commerce platform — informs online consumers what size will most likely fit their body type.
"If you select large, for example, and we think you need a medium, we alert you that you need a medium,” MySizeID CEO Ronen Luzon recently told Fox Business. “And if you still order the large, we understand that the return is because of size related [issues]. And then we recommend you again next time, and we can alert the retailer as well of the habits of their consumers."
Similarly, Volumental and ShoeAI tap AI to solve fit issues around footwear. The firms use data on the shoes the consumer already wears as well as insights from what’s worked for others. Volumental’s mobile app lets consumers scan their feet with their cell phones.
“With machine learning, you’re training the database to understand the relationship between bits of data done at scale,” Brent Hollowell, Volumental’s chief marketing officer and general manager, told WWD. “We use that information to produce very high-quality recommendations. So if you don’t know what kind of shoe or brand or size you want, we can say, ‘well based on your feet, this is what would fit your foot the best.’”
Improving the accuracy of product descriptions, including better addressing shoppers’ questions and concerns, is another focus to reduce returns via AI. Stitch Fix's AI-driven algorithm analyzes customer data such as style preferences, body type, and size to create customized product descriptions that are tailored to each individual customer. Amazon and Shopify also recently introduced AI tools for sellers to help improve product descriptions to drive conversions and reduce returns.
Robert Tekiela, VP of Amazon Selection and Catalog Systems, shared, “With our new generative AI models, we can infer, improve, and enrich product knowledge at an unprecedented scale and with dramatic improvement in quality, performance, and efficiency. Our models learn to infer product information through the diverse sources of information, latent knowledge, and logical reasoning that they learn.”
AI is further being used to steer search ads away from consumers most likely to make returns. James Poll, chief technology officer at Acorn-i, an e-commerce agency, told the Wall Street Journal, “What we can’t do is stop Amazon letting somebody purchase. But what we can do is boost the targeting for the audiences that we think are less likely to return.”