US E-commerce Businesses Reducing Returns by 12% with AI
AI tools analyze customer data
Mirai Team
August 10, 2026
The e-commerce industry in the US has been plagued by high return rates, with the average company experiencing a return rate of around 20-30%. However, some companies have found success in reducing these numbers by leveraging Artificial Intelligence (AI) tools to analyze customer data and make more informed decisions. For example, a study by the National Retail Federation found that companies that used AI to personalize the customer experience saw a 12% reduction in returns. This significant reduction in returns can have a major impact on a company’s bottom line, with some estimates suggesting that it can save businesses up to $100,000 per year.
The Cost of Returns
Returns can be a major cost center for e-commerce businesses, with the cost of processing a return ranging from $10 to $20 per item. This cost can add up quickly, especially for companies that sell high-volume, low-margin products. In addition to the direct cost of processing returns, companies also have to consider the indirect costs, such as the cost of shipping and handling, as well as the potential loss of customer loyalty. By reducing returns, companies can not only save money but also improve customer satisfaction and build brand loyalty.
How AI Works
So, how do AI tools help reduce returns? The answer lies in the ability of AI to analyze large amounts of customer data and identify patterns and trends. By analyzing data such as customer demographics, purchase history, and browsing behavior, AI tools can help companies identify which products are most likely to be returned and why. For example, an AI tool might analyze data and determine that customers who purchase a certain product are more likely to return it if they have a certain browsing history or demographic profile. This information can then be used to make more informed decisions about product offerings and customer interactions.
For instance, a fashion retailer might use AI to analyze customer data and determine that customers who purchase a certain style of shoe are more likely to return it if they have a history of purchasing similar products. The company could then use this information to offer personalized recommendations to customers, such as suggesting alternative products that are less likely to be returned. By providing customers with more personalized and relevant product offerings, companies can reduce the likelihood of returns and improve customer satisfaction.
Real-World Examples
One company that has seen success with AI-powered return reduction is online retailer, Stitch Fix. By using AI to analyze customer data and provide personalized product recommendations, Stitch Fix has been able to reduce its return rate by 15%. Another company, Warby Parker, has used AI to analyze customer data and identify patterns in returns. The company has then used this information to make changes to its product offerings and customer interactions, resulting in a 12% reduction in returns.
Practical Applications
So, how can e-commerce businesses start using AI to reduce returns? One practical application is to use AI-powered chatbots to provide customers with personalized product recommendations and support. For example, a company might use a chatbot to ask customers about their preferences and purchase history, and then use this information to provide personalized product recommendations. Another practical application is to use AI to analyze customer data and identify patterns in returns. This information can then be used to make changes to product offerings and customer interactions, such as providing more detailed product descriptions or offering free returns.
Measuring Success
To measure the success of AI-powered return reduction efforts, companies can track key metrics such as return rate, customer satisfaction, and revenue. By monitoring these metrics, companies can determine whether their AI-powered return reduction efforts are having a positive impact on the business. For example, a company might track its return rate over time and compare it to industry benchmarks to determine whether its AI-powered return reduction efforts are having a significant impact.
Next Steps
To get started with AI-powered return reduction, e-commerce businesses can take the following concrete next steps:
- Assess current return rates and identify areas for improvement
- Explore AI-powered tools and solutions, such as chatbots and machine learning algorithms
- Develop a personalized approach to customer interactions, using AI to analyze customer data and provide relevant product recommendations and support.
Ready to implement this in your business? Mirai deploys AI automation for SMBs across the US, UK, Canada, and Australia — typically in under a week.
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Written by Mirai Team
The Mirai team builds AI automation systems for Western SMBs. We write about what we're building, what we're learning, and what's actually working.