Guangzhi Shang

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 presales 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 multimethod 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 copurchase (DCP), which is based on the machine learning concept of association rule and is implementable using standard retail sales data. Finally, utilizing a unique data set 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.

It is well known that the waiting time a customer experiences in a service system is determined by the service processing time of preceding customers, among other factors. We argue that a directionally opposite effect, which diffuses from waiting time to her own service time, also exists in co‐productive service contexts where a significant fraction of the service time is contributed by the customer. Multiple underlying customer behavioral mechanisms lead us to hypothesize that waiting's impact is dependent on the service stage and magnifies as the service process approaches completion. Our empirical analysis uses a unique operational data set that combines server log information with instant‐messaging transcripts collected from a live‐chat contact center. We show that pre‐service waiting accelerates customer engagement—one dimension of customer instigated service time—only at the beginning of the conversation and then exhibits a slowdown effect as the conversation proceeds. In contrast, in‐service waiting consistently slows down customer responses—another dimension of customer instigated service time, the magnitude of which is higher in later episodes of the agent‐customer message exchanges. We discuss the practical implications of our findings on operational policies employed in contact centers.