Real-Time Delivery Time Forecasting and Promising in Online Retailing: When Will Your Package Arrive?

Abstract

Providing fast and reliable delivery services is key to running a successful online retail business. To achieve a better delivery time guarantee policy, we study how to estimate and promise delivery time for new customer orders in real time. Delivery time promising is critical to managing customer expectations and improving customer satisfaction. Simply over-promising or under-promising is undesirable due to their negative impacts on short-term/long-term sales. We are the first to develop a data-driven framework to predict the distribution of order delivery time and set promised delivery time to customers in a cost-effective way. We adapt regression tree and quantile regression forests to generate distributional forecasts by exploiting the complicated relationship between delivery time and relevant predictors, which include the queue-length predictors to model the distribution center operations. We further propose a cost-sensitive classification decision rule to decide the promised delivery day from the predicted distribution. Tested on a real-world data set shared from JD.com, our proposed machine learning based models deliver superior forecasting performance. In addition, we demonstrate that our framework has the potential to provide better promised delivery time in terms of both cost and accuracy, as compared to the conventional promised time set by JD.com. Through a more accurate estimation of the delivery time distribution, online retailers can strategically set the promised time to maximize customer satisfaction and boost sales. Our data-driven framework reveals the importance of modeling fulfillment operations in delivery time forecasting, which sheds light on further improvement directions.