Examining the dynamic engagement process of passengers in online car-hailing system: a view of user value

Abstract

The online car-hailing system developed rapidly in recent years. This paper introduces the concept of user value and develops a machine learning framework for modeling the dynamic transformation of passenger value in online car-hailing system. Specifically, during the engagement process, four user value types representing the engagement status were captured based on both consumption and travel attributes. Additionally, we modeled user value conversion prediction throughout the engagement process, seamlessly integrating the benefits of features enhancement and time series prediction. Experiments using real order data demonstrated that the proposed machine learning framework has higher predictive accuracy than other methods. The value categories of online car-hailing users change over time. High-value users have high retention rates, while low-value users are the opposite, and medium-value users have the greatest potential. The results provide an important basis for modeling the engagement process of online car-hailing passengers, and for promoting the customer maintenance and management in online car-hailing systems.

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