Return Reason Classification uses AI to assign each product return to a standardised root-cause category from the customer's free-text reason and product data, enabling accurate returns analytics and supplier accountability, by classifying free-text return descriptions against a controlled taxonomy of defect and buyer-remorse categories, across returns management and quality workflows.
Returns management teams record customer-provided return reasons in free text, which is inconsistent across agents and markets and cannot be aggregated for product quality reporting or supplier claims. Root causes (manufacturing defect, description mismatch, sizing error) are only identified after manual sampling of a small fraction of returns.
The AI reads the customer's return reason text, the SKU, and available product attributes and assigns a root-cause category (product defect, incorrect description, size or fit issue, buyer's remorse, damaged in transit) from a controlled taxonomy. The classified stream feeds quality dashboards and supplier reporting.
Returns root-cause classification coverage increases; time to identify a systematic product defect signal decreases.
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