Leveraging the Power of Images in Managing Product Return Rates

In online channels, products are returned at high rates. Shipping, processing, and refurbishing are so costly that a retailer's profit is extremely sensitive to return rates. In many product categories, such as the $500 billion fashion industry, direct experiments are not feasible because the fashion season is over before sufficient data are observed. We show that predicting return rates prior to product launch enhances profit substantially. Using data from a large European retailer (over 1.5 million transactions for about 4,500 fashion items), we demonstrate that machine-learning methods applied to product images enhance predictive ability relative to the retailer’s benchmark (category, seasonality, price, and color labels). Custom image-processing features (RGB color histograms, Gabor filters) capture color and patterns to improve predictions, but deep-learning features improve predictions significantly more. Deep learning appears to capture color-pattern-shape and other intangibles associated with high return rates for apparel. We derive an optimal policy for launch decisions that takes prediction uncertainty into account. The optimal deep-learning-based policy improves profits, achieving 40% of the improvement that would be achievable with perfect information. We show that the retailer could further enhance predictive ability and profits if it could observe the discrepancy in online and offline sales.