Hotel Bookings Cancellation Prediction

Priberam

Abstract:
Booking cancellations contribute negatively to the production of accurate forecasts, a critical tool in the hospitality industry. To lessen this influence, hotels implement rigid cancellation policies and overbooking strategies, which in turn can negatively impact revenue and the hotel’s social reputation. To tackle the uncertainty arising from booking cancellations, we combined data from eight hotels’ bookings management systems with data from other sources (weather, holidays, events, social reputation, and online prices/inventory) to develop booking cancellation prediction models. To test these models in a real production environment and assess the models impact on business, an automated machine learning system prototype was built and deployed in two hotels. In this talk, we will present how the models and the prototype were developed and deployed, as well as the results obtained.
Bio:
Nuno is currently pursuing a PhD degree in Computer Science at ISCTE-IUL, Portugal. Nuno holds a Masters in Hotel Administration and Management from the School of Management, Hospitality and Tourism of the University of the Algarve, Portugal and a degree in Software Engineering from ISMAT/Lusófona, Portugal. He is actually Chief Technology Officer at Itbase/WareGuest, a software development company specialized in the hospitality and retail industries. He is also an invited lecturer at the School of Management, Hospitality and Tourism of the University of the Algarve, Portugal. Nuno is certified in Business Intelligence, specialization of Business Analytics by TDWI – The Data-Warehousing Institute. He is a certified ScrumMaster and member of the Scrum Alliance and, he is also certified as Project Management Associate by IPMA - International Project Management Association and a member of APOJEP – Portuguese project managers’ association.
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Instituto Superior Técnico

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Hotel Bookings Cancellation Prediction

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Hotel Bookings Cancellation Prediction

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