Using Data Science to Predict Hotel Booking Cancellations

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.

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.