Forecasting Hotel-booking Cancelations Using Personal Name Records: An Artificial Intelligence Approach

Abstract

Booking cancelation has a significant impact on hotel management and on the hospitality industry in general. According to some authors, an important portion of hotel revenue loss stems from not considering cancelations in booking systems. Besides, very little is still known about the reasons why customers cancel their bookings, or how this can be effectively avoided from happening. Keeping this in mind, personal name record (PNR) data, which were collected by a hotel partner, were used to design a model to forecast hotel-booking cancelations, which achieved 74% of accuracy. The benefits of the proposed method go beyond such good rate of detection: It only considers 13 different, independent variables, which is a reduced number compared to previous works in the field. Moreover, the included variables coincide with those requested from customers during the hotel reservation process. This is an advantage for hospitality establishments, given that these variables are often the only ones available. Our method allows knowing the cancelation rate with good accuracy, but it can also identify those customers who are likely to cancel their bookings. This approach could be an asset for organizations, as it assists them in improving their action protocols regarding incoming tourists.