This white paper reframes retail returns as an upstream decisioning problem rather than only a reverse-logistics issue. It explains how returns erode margins, consume labor and capacity, distort inventory and performance metrics, increase environmental impact, and weaken customer trust. The proposed approach uses conversational and learning-based AI to improve pre-purchase guidance by combining product attributes, customer history, behavior, inventory, and return outcomes. MongoDB provides a unified operational data layer for real-time context, vector search, predictive models, and decision services embedded in customer journeys. Continuous learning from outcomes can reduce avoidable returns while improving conversion, loyalty, forecasting, sustainability, and operational resilience.

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