Using Data Science to Predict Hotel Booking Cancellations

Abstract

Booking cancellations in the hospitality industry not only generate revenue loss and affect pricing and inventory allocation decisions, but they also, in overbooking situations, have the potential to affect the hotel's online social reputation. By employing data sets from four resort hotels and addressing this issue as a classification problem in the scope of data science, the authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This research also demonstrates that despite what was alleged by Morales and Wang (2010), it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to act on bookings with high cancellation probability and contain the associated revenue losses, produce better net demand forecasts, improve overbooking/cancellation policies, and have more assertive pricing and inventory allocation strategies.

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

“…The authors intended to describe a model to optimise overbooking and fare class allocation instead of building a forecasting or prediction model. The works of Antonio et al (2017bAntonio et al ( , 2017c, Gayar et al (2011), were the only ones focused on hotels while Lan et al (2011), Lemke et al (2009Lemke et al ( , 2013, Morales & Wang (2010), and Pulugurtha & Nambisan (2003) worked with airlines or employ airline data. Azadeh et al (2013), Cirillo et al (2018), and Tsai (2011) worked with railways.…”

“…All of the previous works employed different methods: time series-based techniques (Lemke et al, 2009(Lemke et al, , 2013, economics-based techniques (Cirillo et al, 2018;Tsai, 2011), and more modern techniques usually applied in machine learning problems, like genetic algorithms, neural networks, or other advanced classification techniques (Antonio et al, 2017b(Antonio et al, , 2017cAzadeh et al, 2013;Morales & Wang, 2010;Pulugurtha & Nambisan, 2003). Except the works by Antonio et al (2017bAntonio et al ( , 2017c and Morales & Wang (2010), who considered bookings cancellation estimation a classification problem, all other authors considered it a regression problem. The advantage of considering the problem a classification problem is that it is possible to calculate the cancellation rate (the regression measure) from the prediction of each booking´s likelihood to be cancelled (Antonio et al, 2017c).…”

“…Except the works by Antonio et al (2017bAntonio et al ( , 2017c and Morales & Wang (2010), who considered bookings cancellation estimation a classification problem, all other authors considered it a regression problem. The advantage of considering the problem a classification problem is that it is possible to calculate the cancellation rate (the regression measure) from the prediction of each booking´s likelihood to be cancelled (Antonio et al, 2017c). Although Morales & Wang (2010) argued that predicting the probability of a customer to cancel with high accuracy is difficult and that it would have no practical implications, Antonio et al (2017bAntonio et al ( , 2017c have shown the opposite.…”

“…The advantage of considering the problem a classification problem is that it is possible to calculate the cancellation rate (the regression measure) from the prediction of each booking´s likelihood to be cancelled (Antonio et al, 2017c). Although Morales & Wang (2010) argued that predicting the probability of a customer to cancel with high accuracy is difficult and that it would have no practical implications, Antonio et al (2017bAntonio et al ( , 2017c have shown the opposite. The authors showed that, in practice, the identification of bookings with a high likelihood of cancelling allows hotels to take measures to avoid the identified cancellations.…”

“…Overall, from the 12 documents related to travel or tourism industries, the documents from Antonio et al (2017bAntonio et al ( , 2017c and Morales & Wang (2010) show the most promising paths for research in booking cancellation prediction, that is, predicting each booking cancellation's likelihood and identifying cancellation drivers. Antonio et al (2017bAntonio et al ( , 2017c showed that by using advanced machine learning techniques, it is possible to reach an accuracy of 80% to 90% for the prediction of a booking's cancellation outcome. Moreover, Antonio et al (2017c) and Morales & Wang (2010) showed that some of the features are of higher explanatory value than others (e.g.…”