An automated machine learning based decision support system to predict hotel booking cancellations
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/18427 |
Resumo: | 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. |
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An automated machine learning based decision support system to predict hotel booking cancellationsA/B testingData scienceDecision support systemsMachine learningPredictive analyticsRevenue managementBooking 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.Ubiquity Press2019-07-10T10:32:04Z2019-01-01T00:00:00Z20192019-07-10T11:31:26Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/18427eng1683-147010.5334/dsj-2019-032António, N.de Almeida, A.Nunes, L.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:59:33Zoai:repositorio.iscte-iul.pt:10071/18427Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:31:18.004042Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An automated machine learning based decision support system to predict hotel booking cancellations |
title |
An automated machine learning based decision support system to predict hotel booking cancellations |
spellingShingle |
An automated machine learning based decision support system to predict hotel booking cancellations António, N. A/B testing Data science Decision support systems Machine learning Predictive analytics Revenue management |
title_short |
An automated machine learning based decision support system to predict hotel booking cancellations |
title_full |
An automated machine learning based decision support system to predict hotel booking cancellations |
title_fullStr |
An automated machine learning based decision support system to predict hotel booking cancellations |
title_full_unstemmed |
An automated machine learning based decision support system to predict hotel booking cancellations |
title_sort |
An automated machine learning based decision support system to predict hotel booking cancellations |
author |
António, N. |
author_facet |
António, N. de Almeida, A. Nunes, L. |
author_role |
author |
author2 |
de Almeida, A. Nunes, L. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
António, N. de Almeida, A. Nunes, L. |
dc.subject.por.fl_str_mv |
A/B testing Data science Decision support systems Machine learning Predictive analytics Revenue management |
topic |
A/B testing Data science Decision support systems Machine learning Predictive analytics Revenue management |
description |
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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-10T10:32:04Z 2019-01-01T00:00:00Z 2019 2019-07-10T11:31:26Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/18427 |
url |
http://hdl.handle.net/10071/18427 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1683-1470 10.5334/dsj-2019-032 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Ubiquity Press |
publisher.none.fl_str_mv |
Ubiquity Press |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134874752253952 |