‘‘PREDICTING HOTEL BOOKING CANCELLATIONS USING MACHINE LEARNING: A DATA-DRIVEN STUDY OF CITY AND RESORT HOTELS’’
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Abstract
In the face of hotel booking cancellations, there is significant uncertainty regarding the hotel properties' occupancy planning, revenue management, staffing, and inventory control. To analyse the cancellation patterns at City Hotels and Resort Hotels, and to assess several machine learning models to forecast the risk of booking cancellations. This analysis will consider characteristics related to the behaviour of the customer, transactional characteristics, and reservation characteristics such as lead time, deposit type, customer type, market segment, changes to reservations, special requests, previous cancellations, repeated-guest status and changes to room assignments. The analysis revealed that the cancellation rate was higher when booking a City Hotel compared to booking a Resort Hotel, and that cancelled bookings had on average, a higher lead time and more positive prior cancellation indicators. Large differences were also found between the categories of deposits, groups of customers, market segments and room change. In the context of recall, Random Forest achieved the best result, suggesting its use in cases where the primary goal is to capture as many potentially cancellable items as possible. Room changes, booking channels, parking needs, past cancellations, and special requests were among the other variables that emerged as secondary predictors, with deposit-related variables being the strongest.
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References
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