An Automated Machine Learning Based Decision Support System to Predict Hotel Booking Cancellations
DOI:
https://doi.org/10.5334/dsj-2019-032Keywords:
A/B testing, data science, decision support systems, machine learning, predictive analytics, revenue managementAbstract
Booking cancellations negatively contribute to the production of accurate forecasts, which comprise a critical tool in the hospitality industry. Research has shown that with today’s computational power and advanced machine learning algorithms it is possible to build models to predict bookings cancellation likelihood. However, the effectiveness of these models has never been evaluated in a real environment. To fill this gap and investigate how these models can be implemented in a decision support system and its impact on demand-management decisions, a prototype was built and deployed in two hotels. The prototype, based on an automated machine learning system designed to learn continuously, lead to two important research contributions. First, the development of a training method and weighting mechanism designed to capture changes in cancellations patterns over time and learn from previous days’ predictions hits and errors. Second, the creation of a new measure – Minimum Frequency – to measure the precision of predictions over time. From a business standpoint, the prototype demonstrated its effectiveness, with results exceeding 84% in accuracy, 82% in precision, and 88% in Area Under the Curve (AUC). The system allowed hotels to predict their net demand and thus making better decisions about which bookings to accept and reject, what prices to make, and how many rooms to oversell. The systematic prediction of bookings with high probability of being canceled allowed hotels to reduce cancellations by 37 percentage points by acting to avoid their cancellation.
Published
Issue
Section
License
Copyright (c) 2019 The Author(s)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms. If a submission is rejected or withdrawn prior to publication, all rights return to the author(s):
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Submitting to the journal implicitly confirms that all named authors and rights holders have agreed to the above terms of publication. It is the submitting author's responsibility to ensure all authors and relevant institutional bodies have given their agreement at the point of submission.
Note: some institutions require authors to seek written approval in relation to the terms of publication. Should this be required, authors can request a separate licence agreement document from the editorial team (e.g. authors who are Crown employees).