Case Study: Large Fashion Retailer reduces returns and boosts NPS with Chattermill

How a Large Fashion Retailer Uncovered the Real Drivers Behind Product Returns

Large Fashion Retailer, a North American e-commerce and omnichannel apparel brand, was struggling to understand why product returns were rising across more than 1,000 SKUs. Using Chattermill’s text analytics platform to analyze return survey comments and other customer feedback, the team wanted to identify whether fit issues, product design, or post-purchase experience were driving returns.

With Chattermill, the retailer quickly uncovered specific issues behind returns, such as non-adjustable straps and front-panel length problems on top-selling dresses, allowing them to adjust designs and add fit tips on product pages. The impact was significant: analysis of return reasons became 6x faster, return rates fell by an average of 380 BPS per SKU, some styles saw a 50% reduction in return rates, and one flagship store’s NPS improved by 53 points.