Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach

Despite their current growth and future promise, ride-hailing companies struggle with brand loyalty. As a result, they spend heavily on various marketing tools, especially promotional offers, to encourage passenger engagement, which is often measured by how frequently passengers ride through their platforms. Although extensive research has investigated the passenger intention to continue using ride-hailing services, research that explicitly models the dynamics of passenger engagement is very scarce. In this research, we propose to capture passenger engagement dynamics in ride-hailing services and the factors contributing to them. We combine a heterogeneous hidden Markov model framework with Poisson regression models to probabilistically analyze the transition processes of passenger engagement. Specifically, we capture the influences of various promotional offers on the engagement transition probabilities. We conduct numerical experiments using real-world ride-hailing data. Results show that our model identifies inactive, occasional, and active engagement levels. Our coefficient estimates and sensitivity analysis show that giving moderately more promotional offers to inactively and occasionally engaged passengers would efficiently activate them. More importantly, we derive information about which promotional offers have more significant impacts on the passengers of different engagement levels.