Using transactions data to improve consumer returns forecasting

Although generous return policies have been shown to have marketing benefits, such as a higher willingness to pay and a higher purchase frequency, counterbalancing these benefits is an increased volume of consumer returns, which presents significant operational challenges for both retailers and original equipment manufacturers (OEMs). Since accurate return forecasts are inputs into strategic and tactic decision support tools for operations managers, advancements in better forecast accuracy can yield significant savings from the returns management practice. We propose a forecasting approach that incorporates transaction‐level data, such as purchase and return timestamps, and predicts future return quantities using a two‐step “predict‐aggregate” process. To enhance the generalizability of our framework, we test it on two distinct datasets provided by a bricks‐and‐mortar electronics retailer and an online jewelry retailer. We find that our approach demonstrates significant forecasting error reduction, in the range of 10–20%, over benchmark models constructed from common industry practices and the existing literature. As our approach leverages the same data inputs as existing models, it can be easily adapted by practitioners. We also consider a number of extensions to generalize our approach into contexts such as restricted return time windows, new product returns, and inflated same‐day returns. Last, we discuss broad implications of return forecast accuracy improvements in the areas such as inventory management, staffing level, reverse logistics, and return recovery decisions.