Redo's AI Return Reasons feature replaces the standard multiple-choice dropdown with an open-text input, letting customers describe the reason for their return in their own words while Redo's AI automatically categorizes each response into the correct return reason bucket for merchant reporting. This approach captures far more specific and actionable data than a fixed list of options, revealing nuanced patterns such as recurring fit complaints tied to a specific supplier, color accuracy issues with product photography, or sizing inconsistencies within a particular category. Redo recently improved the accuracy of its AI Return Reason bucketing engine, fixing cases where customer responses were being miscategorized, so merchants can trust the analytics driving their product and inventory decisions.
On the merchant side, return reasons are surfaced in the Returns analytics dashboard grouped by product, category, and return type, making it easy to spot which SKUs generate the most fit complaints or quality issues without manually reviewing individual submissions. The AI analytics chat feature also lets merchants ask plain-language questions about their return data and receive instant trend summaries, so teams can act on insights without building custom reports. For brands managing large SKU catalogs or seasonal assortments, having accurate, AI-powered return reason data helps identify product improvements, optimize exchange recommendations, and reduce long-term return rates by addressing root causes at the source.