Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/73164
Title: Using machine learning and big data for efficient forecasting of hotel booking cancellations
Authors: Sánchez-Medina, Agustín J. 
Caballero Sánchez, Eleazar 
UNESCO Clasification: 120304 Inteligencia artificial
531290 Economía sectorial: turismo
Keywords: Artificial neural network
Cancellation forecasting
Genetic algorithm
Hotel booking
Tree decision algorithm
Issue Date: 2020
Journal: International Journal of Hospitality Management 
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.
URI: http://hdl.handle.net/10553/73164
ISSN: 0278-4319
DOI: 10.1016/j.ijhm.2020.102546
Source: International Journal of Hospitality Management [ISSN 0278-4319], v. 89, (Agosto 2020)
Appears in Collections:Artículos
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