Using machine learning and big data for efficient forecasting of hotel booking cancellations. (August 2020)

• Record Type: Journal Article
• Authors: Sánchez-Medina, Agustín J.; C-Sánchez, Eleazar
• Is Part Of: International journal of hospitality management. Volume 89(2020)
• Publication Date: 2020-08
• Subjects: Cancellation forecasting -- Hotel booking -- Artificial neural network -- Genetic algorithm -- Tree decision algorithm; Hotel management -- Periodicals; Restaurant management -- Periodicals; Food service management -- Periodicals; Hôtels -- Gestion -- Périodiques; Restaurants -- Gestion -- Périodiques; Services alimentaires -- Gestion -- Périodiques; Food service management; Hotel management; Restaurant management; Periodicals; 647.94
• DOI: 10.1016/j.ijhm.2020.102546
• Languages: English
• ISSNs: 0278-4319

Abstract

Highlights: Hotel cancellation forecasts using optimised artificial intelligence techniques. Primary PNR booking data to forecast hotel cancellations. Identification of guests likely to cancel a hotel booking. Abstract: Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.