Assessing customer return behaviors through data analytics

Abstract Retailers often provide lenient, consumer‐friendly return policies to reduce customers' perceived shopping risk and increase demand. As an unfortunate side effect for retailers, empirical findings demonstrate that lenient return policies lead some customers to abuse those return policies through opportunistic and even fraudulent behaviors. Customers can abuse return policies by making purchases with the full intention of returning the products or by returning a product long after extracting most of the product's market value. In doing so, abusive customers extract utility—physical, experiential, or financial—from these purchases, at little or no cost to themselves. However, retailers incur significant costs from such return abuse, with estimates topping $5.6 billion annually in the United States alone. Identifying customers who perpetrate return abuse remains a critical topic. Yet, as a construct, return abuse is difficult to define and to quantify. In contrast, legitimate returner and nonreturner customers exhibit different return behaviors with distinctly different transactional behaviors and profitability outcomes. To investigate these diverse returner behaviors, this study empirically analyzes a transactional secondary data set of over 1 million customers with over 75 million transactions from a national U.S.‐based retailer. The analysis generates empirical insights that characterize observable customer actions related to abusive returners, legitimate returners, and nonreturners. This study introduces a set of predictive models that enable actionable managerial intervention and presents the opportunity to recapture significant returns costs that might otherwise be lost to avoidable return abuse. The analysis also highlights the need for a more holistic perspective toward predicting, managing, and preventing returns.