Order Basket Contents and Consumer Returns

Abstract

Although lenient return policies can drive sales and customer loyalty, they have also resulted in enormous returns volumes and reverse logistics costs. Online retailers often feel compelled to offer free returns, but are then faced with numerous operational challenges, ranging from accurately forecasting returns volumes to identifying pre-sales strategies to reduce the likelihood that a (costly) return occurs. In this research we consider how the complementarity of the products within an order basket is related to consumer returns. By developing an understanding of the link between basket contents and returns, we can improve order-level returns forecasts, while also providing insights into the effect of basket recommendations on the expected return rate. We take a multi-method approach to this problem. First, we use a stylized model to generate theoretical predictions regarding how within-basket complementarity should influence return probability. Next, we propose a data-driven measure of complementarity, degree of co-purchase (DCP), which is based on the machine learning concept of association rule and is implementable using standard retail sales data. Finally, utilizing a unique dataset provided by a leading online specialty retailer, we implement the DCP measure and test the predictions of our analytical model. We find, as expected, that there is a decreasing relationship between within-basket complementarity and return probability. However, we also show that this decrease is convex, indicating that the return probability impact is more notable when the complementarity is increased from a lower base. Our results have practical implications for both reverse logistics planning and online product recommendations.

Keywords: Online Retailing, Product Returns, Order Basket, Association Rule, Multi-Method Research