Predicting hotel booking cancellations to decrease uncertainty and increase revenue
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/13341 |
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. |
id |
RCAP_7d1604837797f0a1413571e3057c74f4 |
---|---|
oai_identifier_str |
oai:repositorio.iscte-iul.pt:10071/13341 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
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.Escola Superior de Gestão, Hotelaria e Turismo. Universidade do Algarve2017-05-15T09:00:20Z2017-01-01T00:00:00Z20172019-04-01T12:48:30Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/13341por2182-845810.18089/tms.2017.13203Antonio, 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:RCAAP2023-11-09T18:01:00Zoai:repositorio.iscte-iul.pt:10071/13341Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:32:28.586446Repositório 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, N. 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, N. |
author_facet |
Antonio, 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 |
Antonio, N. de Almeida, A. Nunes, L. |
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 |
2017-05-15T09:00:20Z 2017-01-01T00:00:00Z 2017 2019-04-01T12:48:30Z |
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/10071/13341 |
url |
http://hdl.handle.net/10071/13341 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
2182-8458 10.18089/tms.2017.13203 |
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 |
Escola Superior de Gestão, Hotelaria e Turismo. Universidade do Algarve |
publisher.none.fl_str_mv |
Escola Superior de Gestão, Hotelaria e Turismo. Universidade do Algarve |
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 |
repository.mail.fl_str_mv |
|
_version_ |
1799134886104137728 |