Artificial Intelligence (AI) is revolutionizing the way eCommerce retailers manage returns, offering smarter, data-driven solutions to reduce costs and enhance customer satisfaction. With AI-powered tools, brands can anticipate return trends, personalize recommendations, and automate customer interactions—leading to fewer returns and higher retention. As AI adoption accelerates, retailers that embrace these innovations will gain a competitive edge in managing returns more efficiently and sustainably.
The AI-enabled eCommerce market size is projected to reach $16.8 billion by 2030, growing at a rate of 15.7% CAGR in the next 8 years. Notably, 78% of eCommerce brands have already implemented AI in their stores or are planning to do so by 2030
Here are some examples of use-cases of AI in the returns space;
Data collection and analysis
AI can help retailers identify frequently returned items due to sizing issues, prompting them to improve size guides and product descriptions. One of the major use cases of AI-supported tools is the way it can support pattern identification and data analysis to find trends quickly. For example, Amazon's investment in robotics and AI has led to a 25% cost reduction in its advanced Shreveport fulfillment center, showcasing the efficiency of AI in operations.
Workflow automation and customer support
Generative AI can also be used to collect customer feedback, pinpointing common reasons for returns and implementing strategies to mitigate them. Chatbots and workflows for email support can give customers answers faster, resulting in faster time to resolve customer questions, reduced strain on customer support teams and can collect segmented data for further trend analysis. Retailers like H&M and Sephora have successfully implemented AI-powered chatbots to interact with customers, offering styling advice and product recommendations, thereby enhancing customer engagement and reducing return rates
Personalization and Targeting
AI-powered personalization can increase customer satisfaction by up to 20% and conversion rates by up to 15%. By tailoring product recommendations and marketing messages to individual preferences, retailers can enhance the shopping experience, leading to higher satisfaction and reduced return rates. Major retailers like Sephora and Nike have successfully implemented AI to create personalized shopping experiences, resulting in increased customer satisfaction and retention.
AI for fit predictors
AI-powered fit tools, Wair, MirrAR, and fitAnalytics are changing the eCommerce game by giving customers an opportunity to gauge size and fit of an item before making a purchase. The impacts of this are huge, given that sizing and fit remain the largest reason for clothing returns in eCommerce.
Andie Swim's work with Wair has shown significant results: 40% adoption on product pages and a 30% reduction in return rates. However, adoption across the industry remains low, with most brands hesitant to experiment with such tools unless ROI is proven. Other concerns around privacy and customer hesitation may have hindered widespread adoption. Despite these challenges, AI-driven virtual fitting tools are being developed to reduce eCommerce returns and provide retailers with data to better target consumers.
AI is actively reshaping how retailers handle returns, from predictive analytics to personalized fit recommendations. As more brands integrate AI-driven solutions, they can reduce costly returns, improve customer satisfaction, and streamline logistics. Investing in AI-powered returns management is a strategic move toward building a more efficient and customer-centric eCommerce experience. Those who leverage AI effectively today will lead the future of seamless, hassle-free returns.
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