Why Making Customers Wait Pays Off for Online Retailers
Research explains how a brief delay can yield big benefits.
Why Making Customers Wait Pays Off for Online Retailers
At the E-Commerce Warehouse, a Distribution Dilemma
- September 03, 2024
- CBR - Operations Management
Online shoppers often place two or more orders in quick succession—an initial one, and then a follow-up one when they remember something else they meant to buy. To counter the rising costs this multiordering creates, some e-commerce platforms hold orders before fulfilling them, but this welcomes its own complications. Chicago Booth PhD student Mohammad Reza Aminian, Columbia’s Will Ma, and Booth’s Linwei Xin built a decision-making model to determine in real time which orders companies should hold.
Some orders are straightforward to fulfill, but other orders present a trade-off.
Imagine a retailer that can hold one order at a time for a maximum of three hours. As another customer’s order comes in, the retailer must decide whether to continue holding the first order or dispatch it in favor of delaying the new one.
Three algorithms for holding orders
Orders that have a high chance of consolidation and those that have more time in the system before being dispatched are good candidates for holding. But these criteria can conflict, creating a trade-off. When this happens, retailers can follow one of three algorithms:
- Reward-rate algorithm: Hold the orders with the highest reward rate.
- Remaining-reward algorithm: Hold the orders with the largest remaining reward, which is the reward rate multiplied by the remaining time to dispatch.
- Exponential algorithm: Hold the orders with the largest value from an equation that considers both the reward rate and the remaining reward (calculated by multiplying the reward rate by an exponential function of the remaining time to dispatch).
The best option depends on the retailer’s capacity constraints and the platform’s busyness. But in most cases, the exponential algorithm generates the best performance relative to a benchmark. Here’s how the algorithms perform among platforms with different holding capacity:
Mohammad Reza Aminian, Will Ma, and Linwei Xin, “Real-Time Personalized Order Holding,” Working paper, November 2023.
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