Impatient or Selective? Estimation of Customer Preference in Scheduling Attended Home Delivery
Posted: 5 Jul 2023 Last revised: 28 Jul 2023
Date Written: July 2, 2023
Problem definition: Retailers spend billions of dollars providing faster service. Yet those costs hurt profits, and associated logistical challenges do not always yield higher revenue. One example is attended home delivery (AHD), whereby customers must be present for delivery. In this setting, customer availability may be more important than faster service. We look to understand customer preferences to improve this last-mile delivery process.
Methodology/results: We develop a model to estimate customer delivery date preferences where the selection of a delivery date is affected by the lead time, the day of the week of candidate delivery days, and the day of the week of their purchase—and interactions among these factors. Using data from a major furniture company, we derive insights into customer preferences that can help companies improve scheduling for AHD. We find that customers do not always want the earliest available delivery—a later date might be more convenient—and that assuming so unnecessarily constrains scheduling decisions. Moreover, the day of the week of the in-store purchase is a clue to the best delivery day; for instance, customers shopping on a Wednesday are more likely to be available for home delivery on a Wednesday and other weekdays than weekend shoppers. We explore how retailers could leverage this signal to balance customer satisfaction with delivery costs. Building on insights from our model, we quantify the value of implementing scheduling strategies that reallocate delivery capacity across days compared to policies that ignore the effects we identify.
Managerial implications: In AHD, faster delivery is not always better (contrary to what is typically assumed), and recognizing this can benefit retailers and their customers. When customers visit the store, they reveal a day-of-week preference that can be used to improve operations. Overall, our research can guide retailers to better understand cost/service trade-offs.
Keywords: last mile delivery, scheduling, customer preferences, brick-and-mortar and showroom channels, empirical retail
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