Retailer Increases Returns Recovery by 700% with AI

A multinational discount retailer faced growing challenges recovering value from returned products purchased through overseas supplier networks. Without the data, infrastructure, or intelligence needed to make informed disposition decisions, many returned products were liquidated, discarded, or routed through low-value recovery channels.

These limitations resulted in millions of dollars in unrealized recovery value, ongoing disposal costs, and increased waste generation. As the retailer expanded its sustainability initiatives and pursued long-term zero-waste goals, leadership recognized the need for a more intelligent and profitable approach to returns management.

For years, the retailer relied on limited recovery options for returned products. Many items were grouped into broad liquidation categories or sent to disposal because teams lacked the data needed to determine the most profitable recovery path for each product.

Without detailed product-level intelligence, higher-value items were frequently mixed with lower-value inventory, reducing resale potential and limiting recovery outcomes. This approach not only constrained financial performance but also contributed to growing waste volumes and unnecessary disposal costs.

The retailer needed a scalable strategy capable of improving recovery performance while supporting broader sustainability objectives.

The retailer recognized the need to improve returns recovery by introducing greater intelligence into how returned products were evaluated, segmented, and routed. Leadership sought a solution capable of distinguishing between high-, mid-, and low-value inventory so that each product could be directed to the recovery channel with the greatest financial return.

Improving inventory segmentation alone would not be enough to maximize recovery performance. Greater visibility into the factors that influence recovery outcomes would help drive better inventory decisions, recover more value from returned products, and further support the organization’s long-term sustainability objectives.

ReturnPro implemented its intelligent disposition engine to evaluate millions of UPCs and determine the optimal recovery path for returned products. Using product-level attributes such as condition, retail value, processing costs, transportation costs, and handling requirements, the platform automatically identified the highest-value disposition strategy for each item.

Rather than routing products through broad liquidation streams, returned inventory could be segmented into high-, mid-, and low-value categories and directed to the most appropriate resale, recommerce, or liquidation channel.

This intelligent routing approach enabled the retailer to capture significantly more value from returned products while reducing reliance on disposal and low-value liquidation programs.

The retailer transformed its approach to returns recovery through intelligent product-level disposition decisions. Recovery rates improved from approximately 3–4% to as much as 45%, dramatically increasing the value recovered from returned inventory.

By separating higher-value products from general merchandise streams and routing inventory to the most profitable resale and recommerce channels, the retailer generated more than $18M in annual financial benefit. The intelligent disposition strategy also reduced landfill dependence, lowered disposal-related costs, and supported broader sustainability objectives.

The result was the ability to recover significantly more value from returns while reducing waste and supporting long-term sustainability.

Key Results

• Improved product-level disposition accuracy
• Reduced landfill and disposal dependency
• Increased resale and recommerce opportunities

Following implementation, ReturnPro continued refining its intelligent disposition capabilities by expanding product intelligence, enhancing recovery algorithms, and increasing visibility into recovery performance across product categories.

As additional UPC-level data was incorporated into the platform, disposition decisions became increasingly precise, enabling the retailer to uncover additional recovery opportunities while further reducing waste. Over time, the retailer continued uncovering additional recovery value while reducing waste and disposition accuracy across millions of returned products.