Preemptive management of customer cancellations on online booking platforms
(A2021-93948)

AUTHORS

Marco Kotschedoff, KU Leuven; Fahad Malik, Goethe University Frankfurt

ABSTRACT

The emergence of online booking platforms has changed the way how consumers search and book hotel stays. They are also essential for hotel operators to acquire and manage new customers. Cancellation rates are, however, significantly higher for reservations made through booking platforms than for reservations made through direct sales channels. High cancellation rates threaten the profitability in the hospitality industry. Strategic overbooking is a conventional approach to reduce the negative impact of cancellations on profits. In contrast, we propose to preemptively manage customer cancellations by targeting customers at different stages of the booking journey. First, we apply machine learning methods to predict customer cancellation risk scores from past booking requests. Second, we show how the predictive performance of our model improves as we move further along the booking journey. Third, we suggest to tailor marketing-mix variables such as price components, offerings and hotel visibility to our cancellation risk scores during the booking journey. Our managerial implications explicitly address the unique features of booking platforms and can be implemented into their design for customer targeting.