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. Using a large data set from a European apparel retailer, we observe that return rates for fashion items bought online range from 13% to 96%, with an average of 53%; many items are not profitable. Because fashion seasons are over before sufficient data on return rates are observed, retailers need to anticipate each item’s return rate prior to launch. We use product images and traditional measures available prelaunch to predict individual item return rates and decide whether to include the item in the retailer’s assortment. We complement machine-based prediction with automatically extracted image-based interpretable features. Insights suggest how to select and design fashion items that are less likely to be returned. Our illustrative machine-learning models predict well and provide face-valid interpretations; the focal retailer can improve profit by 8.3% and identify items with features less likely to be returned. We demonstrate that other machine-learning models do almost as well, reinforcing the value of using prelaunch images to manage returns.