Predicting product return volume using machine learning methods

In 2015, U.S. consumers returned goods worth $261 billion and the return rates for online sales sometimes exceeded 30%. Manufacturers and retailers have an interest in predicting return volume to address operational challenges in managing product returns. In this paper, we develop and test data-driven models for predicting return volume at the retailer, product type and period levels using a rich data set comprised of detailed operations on each product, and retailer information. The goal is to achieve a good prediction accuracy out of sample. We consider main effects and detailed interaction effects models using various machine learning methods. We find that Least Absolute Shrinkage and Selection Operator (LASSO) yields a predictive model achieving the best prediction accuracy for future return volume due to its ability to select informative interaction terms out of more than one thousand possible combinations. The LASSO model also turns in consistent performance based on several robustness tests and is easy to implement in practice. Our work provides a general predictive model framework for manufacturers to track product returns.