13-05-2025
Beyond Blanket Markdowns: Smart Pricing For Returned Fashion Items
Arun Rasika Karunakaran is a retail product management leader @TCS specializing in AI-led transformation, merchandising & store operations. getty
A 2023 study by Coresight Research estimated that the average return rate for clothing ordered online was 24.4%—that's approximately $38 billion in returns, with $25 billion in processing costs for apparel retailers.
What makes fashion returns particularly complex is the variable lifecycle of products. From "fad" items that remain relevant for mere weeks to "classics" that sell steadily for years, fashion retailers struggle to properly price returned merchandise based on its remaining market value.
The traditional method of applying blanket markdown percentages to returned items leaves billions on the table. The one-size-fits-all strategy fails to account for product-specific value retention. A returned black dress in classic styling, for example, might retain 85% of its value, while a returned neon statement piece from last season might be worth just 15% of its original price. This disparity highlights the need for more sophisticated pricing models that account for a product's position in its lifecycle.
A data-driven approach could offer retailers a sophisticated solution to this problem. By analyzing the complex interplay between product attributes, lifecycle patterns and logistics costs, retailers could optimize pricing for returned fashion items.
The innovation lies in how to break down products into "attribute components"—fundamental characteristics that influence consumer preferences, rather than looking at individual items in isolation. For example, instead of analyzing a polka dot black blouse individually, the focus should be on recognizing patterns in how consumers value the "polka dot pattern" combined with "black color" and "blouse style" across the entire product catalog. Leveraging Attributes-Based Intelligence
With the item attributes, retailers could come up with an attribute relationship repository for each item group or category (such as women's tops or men's suits). This repository potentially captures the association between various product attributes and consumer purchasing patterns.
By applying machine learning methods, retailers could identify the lifecycle length of different attribute combinations and estimate how much value a returned product retains based on:
• The current stage in its lifecycle
• The specific attribute components that make up the product
• Logistics costs associated with processing the return
• Current inventory levels
• Promotional history ROI Of Intelligent Return Pricing
Research found that less than half of returned goods are resold at full price. Fashion retailers could implement an attribute-based pricing approach to significantly improve the recovery value from returned merchandise. As reported in a CNBC article, Tobin Moore, the CEO of Optoro , said, 'A lot of retailers can add 5% to their bottom line by better optimizing the management and resale of their returns.'
The attribute-driven approach is particularly valuable for multibrand retailers and department stores managing diverse product assortments with varying lifecycle patterns. For these businesses, even a modest 5% improvement in return recovery can translate to millions in recaptured revenue annually.
This could prove most effective during seasonal transitions and holiday return waves when retailers traditionally struggle with processing high volumes of returns while maintaining price integrity. By dynamically adjusting prices based on product attributes rather than calendar dates, retailers can maximize recovery value during these critical periods. Strategic Application Beyond Pricing
The benefits extend beyond optimizing the price point for returned items. The attribute relationship mapping creates valuable applications throughout the retail ecosystem:
• Merchandise Planning: Retailers can analyze which attribute combinations have longer lifecycles to make more informed buying decisions. This enables merchandise planners to balance trend-driven pieces with more durable attribute combinations that retain value longer.
• Assortment Strategy: By understanding the relationship between attribute combinations and return rates, retailers can refine their assortment strategy to minimize returns while maximizing sales potential.
• Supplier Negotiations: Data on attribute performance can inform supplier negotiations by identifying which manufacturing partners consistently produce items with attributes that retain value longer after returns.
• Product Development: For retailers with private label programs, the system provides invaluable feedback on which design elements and attribute combinations to incorporate into future collections for optimal lifecycle value. The Future Of Returns Management
As e-commerce continues to grow, with global online fashion sales projected to reach $1.2 trillion by 2025, according to Statista, efficient returns management becomes increasingly critical to profitability.
Data-driven return analysis reveals crucial customer behavior patterns while creating opportunities for inventory optimization. Retailers leveraging this intelligence can transform return challenges into strategic advantages that boost profitability.
By pricing returned items optimally, retailers can ensure items re-enter the marketplace quickly rather than ending up in landfills—a crucial sustainability consideration when the fashion industry contributes roughly 92 million tons of textile waste each year. Adopting Attribute-Based Return Pricing
For retailers looking to adopt an attribute-based approach, I suggest starting with:
• Comprehensive data collection from all touchpoints
• Integration at the individual transaction level
• Attribute mapping across the product catalog
• Machine learning model development to recognize patterns
• Continuous analysis of lifecycle patterns for different attribute combinations
While the above steps require sophisticated data infrastructure, the return on investment can be substantial. For a mid-sized fashion retailer processing $10 million in returns annually, even a 10% improvement in recovery value represents $1 million in recaptured revenue.
In an industry with notoriously thin margins, effectively managing returns is no longer optional—it's essential. The future of fashion retail belongs to those who can turn the challenge of returns into an opportunity for data-driven optimization.
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