Machine Learning in Hospitality: Interpretable Forecasting of Booking Cancellations

Abstract

The phenomenon of cancellations in hotel bookings is one of the main pain points in the hospitality sector as it skews demand signals and can result in revenue losses estimated at about 20 %. Yet, forecasting booking cancellations remains an underresearched area, particularly in the understanding of the behavioral drivers of cancellations. This paper addresses this gap by proposing a new approach to predicting hotel booking cancellations rooted in stacked generalization and Explainable Artificial Intelligence (XAI). Specifically, the combination of linear, tree-based, non-linear and deep learning models into a single meta-model resulted in an increased accuracy rate to 96 %. In addition, this work focuses on interpretability, identifying the driving behavioral factors of cancellation as location, type of room, and customer segments. This approach can provide hoteliers with both highly accurate predictions as well as marketing intelligence that would allow them to drive strategy to minimize loss resulting from cancellations. The results of the research provide an effective solution to the challenges involved in forecasting booking cancellations, balancing forecast prediction accuracy with the ability to provide actionable insights.