A contingent free shipping (CFS) policy offers free shipment of an order only if it satisfies a pre-specified threshold amount. Such a policy may induce customers to pad below-threshold orders to meet the threshold. On the one hand, such padded orders economize the retailer’s logistics cost; on the other hand, it exposes the retailer to enhanced return costs as customers may engage in bubble purchases—orders with spuriously padded items that are later returned. A retailer designing the policy’s terms— threshold and shipping fee—should attempt to balance these competing trade-offs. We study how the selection of these CFS terms is moderated by the retailer’s returns policy and associated customers’ ease-of-return experience. We collaborate with a retailer who switched across multiple CFS policies over time. Our empirical strategy builds on the quasi-natural experiments induced by these switches, and location-based variation in the retailer’s returns policy. We find that in markets with a convenient ease-of-return process, customers pad 15.7% to 23.0% of below-threshold demand, and that 2.9% to 18.5% of these padded orders are bubble purchases. Interestingly, we find that, in markets with modest inconveniences in the returns process, the beneficial order padding is prevalent (13.2% to 20.3%); however, bubble purchases are altogether eliminated. Our counterfactual analysis illustrates that ignoring this moderating role of ease-of-return experience when selecting a CFS policy can result in the selection of suboptimal terms, with a loss of 13.2% in profits. Our study documents a novel determinant of optimal CFS terms: ease-of-return experience. To reflect its impact on the CFS policy’s embedded trade-offs, a manager shall apply the following counterintuitive adjustment; set lenient (resp. stringent) CFS terms when the customer return process is convenient (resp. inconvenient).
About the Speaker:
Dr. Ashish Kabra is an Assistant Professor at the Robert H. Smith School of Business at the University of Maryland. He conducts empirical and theoretical research using causal inference, structural estimation, game theory, and optimization methods. His research focuses on studying the interplay between platform operations, consumer behavior, and sustainability in the domains such as e-commerce, online B2B platforms, retail stores, and urban transportation. His research work has been published in top journals and has won several prestigious best paper awards. His teaching has been recognized with the prestigious Allen J. Krowe teaching award at the Smith School. Prior to joining Maryland, he completed his graduate studies in Operations Management at INSEAD, France, and undergraduate studies in Computer Science at BITS-Pilani, India.