Create an analytics application that helps online sellers discover why products are returned and which changes could prevent avoidable returns.
Allow users to import order records, product descriptions, customer feedback, return reasons, support tickets, and refund amounts. Group return patterns by SKU, product variant, size, supplier, shipping method, listing version, and customer-reported issue.
Detect mismatches between product descriptions and actual customer expectations, confusing variant names, recurring defects, sizing inconsistencies, packaging problems, and misleading imagery.
For each issue, estimate the financial impact and propose a testable intervention, such as a clearer size guide, additional product photographs, improved packaging, or a supplier quality check.
Include cohort comparisons, root-cause evidence, experiment tracking, and a return-cost calculator that includes shipping, handling, lost product value, and refund fees.
Do not assume every return is preventable or blame customers without evidence. Clearly distinguish observed correlations from validated causes and allow teams to measure whether interventions actually reduce returns.
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