Big Data in Hotel Revenue Management: Exploring Cancellation Drivers to Gain Insights Into Booking Cancellation Behavior

Abstract

In the hospitality industry, demand forecast accuracy is highly impacted by booking cancellations, which makes demand-management decisions difficult and risky. In attempting to minimize losses, hotels tend to implement restrictive cancellation policies and employ overbooking tactics, which, in turn, reduce the number of bookings and reduce revenue. To tackle the uncertainty arising from booking cancellations, we combined the data from eight hotels’ property management systems with data from several sources (weather, holidays, events, social reputation, and online prices/inventory) and machine learning interpretable algorithms to develop booking cancellation prediction models for the hotels. In a real production environment, improvement of the forecast accuracy due to the use of these models could enable hoteliers to decrease the number of cancellations, thus, increasing confidence in demand-management decisions. Moreover, this work shows that improvement of the demand forecast would allow hoteliers to better understand their net demand, that is, current demand minus predicted cancellations. Simultaneously, by focusing not only on forecast accuracy but also on its explicability, this work illustrates one other advantage of the application of these types of techniques in forecasting: the interpretation of the predictions of the model. By exposing cancellation drivers, models help hoteliers to better understand booking cancellation patterns and enable the adjustment of a hotel’s cancellation policies and overbooking tactics according to the characteristics of its bookings.

20 years of Nordic tourism economics research: a review and future research agenda

“…Social media data is an extensive source of information that can be quantified. For example, big data is currently being used to forecast both hospitality (Antonio et al, 2019) and tourism demand (Zhang et al, 2020). This appears to be another fertile ground for hospitality and tourism research.…”

A Novel Cancellation Protection Service in Online Reservation System

“…The authors examined the performance of the existing cancellation forecasting models and proposed new promising ones based on Support Vector Machines (SVM). Authors in [16] combined the data from 8 hotels' property management systems with data from several sources and used machine learning algorithms to develop booking cancellation prediction models for the hotels.…”

“…QoE Cancellation service Data mining Decision aid AHP Arline industry Revenue management [6] --- [7], [8] ---- [9] --- [10], [11], [12] --- [13], [14] ----- [15], [16], [17] ---- [18], [19] ---- [20], [21] ---- [22], [23] -…”

Overbooking as the Instrument of Revenue Management

“…The prerequisites for forming conclusions about the peculiarities of forming overbooking as a hotel revenue management optimization method in an environment where market segment prices are optimized via demand curves ahead of a planning horizon [1]. Theoretical and methodological understanding of booking cancellation patterns and enable the adjustment of a hotel's cancellation policies and overbooking tactics according to the characteristics of its bookings were analyzed in [2].…”