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Sooner or Later? Promising Delivery Speed in Online Retail

Ruomeng Cui¹; Zhikun Lu¹; Tianshu Sun^2,3; Joseph M. Golden⁴

¹ Goizueta Business School, Emory University, Atlanta, Georgia, 30322 · ² Department of Technology and Operations, Cheung Kong Graduate School of Business, Beijing 100738, China; · ³ Marshall School of Business, University of Southern California, Los Angeles, California 90089 · ⁴ PerfectRec.com, Seattle, Washington 98119

Abstract. Problem definition: Online retailers have to provide customers with an estimate of how fast an order can be delivered before they decide to make the purchase. Retailers can strategically adjust this delivery speed promise online without changing offline infrastructure, and doing so may fundamentally impact business outcomes. It can influence consumers’ purchasing decisions and postpurchase experiences, often in the opposite direction. On one hand, an aggressive (i.e., faster) delivery estimate could ensure that more customers meet their deadlines and thus, may increase their purchases ex ante. On the other hand, an aggressive estimate tends to overpromise, potentially leading to a longer than expected wait time, which can lower customer satisfaction and increase product returns ex post. In this research, we estimate the causal effect of retailers’ delivery speed promise on customer behaviors and business performance. Methodology/results: Collaborating with Collage.com , an online retailer that sells customized photo products across the United States, we exogenously varied the disclosed delivery speed estimates online while keeping the physical delivery speed unchanged. Using the difference-in-differences identification strategy, we find that a faster promise increases sales and profits, but it also increases product returns and reduces customer retention. In addition, we propose a data-driven model that uses the estimated parameters to optimize delivery promises to maximize customer lifetime value. Managerial implications: Our findings provide managerial insights and a data-driven policy that retailers can leverage to optimize and customize their delivery promises. Funding: T. Sun acknowledges research support from CKGSB Research Institute. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0174 .

DOI

10.1287/msom.2021.0174

Volume

26 (1)

Pages

233-251

Language

en

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BibTeX

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