Predicting hotel booking cancellations to decrease uncertainty and increase revenue
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://tmstudies.net/index.php/ectms/article/view/1000 |
Resumo: | Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation.Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled.Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies. |
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Predicting hotel booking cancellations to decrease uncertainty and increase revenueData scienceHospitality industryMachine learningPredictive modelingRevenue managementBooking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation.Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled.Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.University of Algarve2017-04-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/zipapplication/zipapplication/zipapplication/zipapplication/ziphttps://tmstudies.net/index.php/ectms/article/view/1000Revista Encontros Científicos - Tourism & Management Studies; v. 13 n. 2 (2017); 25-39Tourism & Management Studies; Vol. 13 N.º 2 (2017); 25-39Tourism & Management Studies; Vol. 13 No. 2 (2017); 25-39Revista Encontros Científicos - Tourism & Management Studies; Vol. 13 Núm. 2 (2017); 25-392182-8466reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttps://tmstudies.net/index.php/ectms/article/view/1000https://tmstudies.net/index.php/ectms/article/view/1000/pdf_51https://tmstudies.net/index.php/ectms/article/view/1000/2187https://tmstudies.net/index.php/ectms/article/view/1000/2188https://tmstudies.net/index.php/ectms/article/view/1000/2189https://tmstudies.net/index.php/ectms/article/view/1000/2190https://tmstudies.net/index.php/ectms/article/view/1000/2191Copyright (c) 2017 Tourism & Management Studiesinfo:eu-repo/semantics/openAccessAntonio, Nunode Almeida, AnaNunes, Luis2024-01-24T12:54:32Zoai:ojs.pkp.sfu.ca:article/1000Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:56:27.472026Repositório 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 |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
title |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
spellingShingle |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue Antonio, Nuno Data science Hospitality industry Machine learning Predictive modeling Revenue management |
title_short |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
title_full |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
title_fullStr |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
title_full_unstemmed |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
title_sort |
Predicting hotel booking cancellations to decrease uncertainty and increase revenue |
author |
Antonio, Nuno |
author_facet |
Antonio, Nuno de Almeida, Ana Nunes, Luis |
author_role |
author |
author2 |
de Almeida, Ana Nunes, Luis |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Antonio, Nuno de Almeida, Ana Nunes, Luis |
dc.subject.por.fl_str_mv |
Data science Hospitality industry Machine learning Predictive modeling Revenue management |
topic |
Data science Hospitality industry Machine learning Predictive modeling Revenue management |
description |
Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on revenue and reputation.Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled.Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-04-30 |
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 |
https://tmstudies.net/index.php/ectms/article/view/1000 |
url |
https://tmstudies.net/index.php/ectms/article/view/1000 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://tmstudies.net/index.php/ectms/article/view/1000 https://tmstudies.net/index.php/ectms/article/view/1000/pdf_51 https://tmstudies.net/index.php/ectms/article/view/1000/2187 https://tmstudies.net/index.php/ectms/article/view/1000/2188 https://tmstudies.net/index.php/ectms/article/view/1000/2189 https://tmstudies.net/index.php/ectms/article/view/1000/2190 https://tmstudies.net/index.php/ectms/article/view/1000/2191 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2017 Tourism & Management Studies info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2017 Tourism & Management Studies |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/zip application/zip application/zip application/zip application/zip |
dc.publisher.none.fl_str_mv |
University of Algarve |
publisher.none.fl_str_mv |
University of Algarve |
dc.source.none.fl_str_mv |
Revista Encontros Científicos - Tourism & Management Studies; v. 13 n. 2 (2017); 25-39 Tourism & Management Studies; Vol. 13 N.º 2 (2017); 25-39 Tourism & Management Studies; Vol. 13 No. 2 (2017); 25-39 Revista Encontros Científicos - Tourism & Management Studies; Vol. 13 Núm. 2 (2017); 25-39 2182-8466 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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1799136449324384256 |