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The theory-practice research gains from big data

Bibliographic Details
Main Author: Rita, Paulo
Publication Date: 2023
Other Authors: Tiago, Maria Teresa Borges, Caetano, Joana
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/153045
Summary: Rita, P., Tiago, M. T. B., & Caetano, J. (2023). The theory-practice research gains from big data: Evidence from hospitality loyalty programs. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-05-2022-0646 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, research grants UIDB/04521/2020 of the Advance/CSG, ISEG – Lisbon School of Economics and Management; and UIDB/00685/2020 of the Centre of Applied Economics Studies of the Atlantic, School of Business and Economics of the University of the Azores. ---%ABS3%
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spelling The theory-practice research gains from big dataEvidence from hospitality loyalty programsLoyaltyHotel loyalty programsCustomer segmentationClusteringk-meansBig dataTourism, Leisure and Hospitality ManagementRita, P., Tiago, M. T. B., & Caetano, J. (2023). The theory-practice research gains from big data: Evidence from hospitality loyalty programs. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-05-2022-0646 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, research grants UIDB/04521/2020 of the Advance/CSG, ISEG – Lisbon School of Economics and Management; and UIDB/00685/2020 of the Centre of Applied Economics Studies of the Atlantic, School of Business and Economics of the University of the Azores. ---%ABS3%Purpose The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs. Design/methodology/approach Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers. Findings This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers. Practical implications Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies. Originality/value As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRita, PauloTiago, Maria Teresa BorgesCaetano, Joana2023-05-22T22:17:29Z2023-11-082023-11-08T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/153045eng0959-6119PURE: 57080747https://doi.org/10.1108/IJCHM-05-2022-0646info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-12-30T01:34:15Zoai:run.unl.pt:10362/153045Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:42:06.713056Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv The theory-practice research gains from big data
Evidence from hospitality loyalty programs
title The theory-practice research gains from big data
spellingShingle The theory-practice research gains from big data
Rita, Paulo
Loyalty
Hotel loyalty programs
Customer segmentation
Clustering
k-means
Big data
Tourism, Leisure and Hospitality Management
title_short The theory-practice research gains from big data
title_full The theory-practice research gains from big data
title_fullStr The theory-practice research gains from big data
title_full_unstemmed The theory-practice research gains from big data
title_sort The theory-practice research gains from big data
author Rita, Paulo
author_facet Rita, Paulo
Tiago, Maria Teresa Borges
Caetano, Joana
author_role author
author2 Tiago, Maria Teresa Borges
Caetano, Joana
author2_role author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Rita, Paulo
Tiago, Maria Teresa Borges
Caetano, Joana
dc.subject.por.fl_str_mv Loyalty
Hotel loyalty programs
Customer segmentation
Clustering
k-means
Big data
Tourism, Leisure and Hospitality Management
topic Loyalty
Hotel loyalty programs
Customer segmentation
Clustering
k-means
Big data
Tourism, Leisure and Hospitality Management
description Rita, P., Tiago, M. T. B., & Caetano, J. (2023). The theory-practice research gains from big data: Evidence from hospitality loyalty programs. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-05-2022-0646 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, research grants UIDB/04521/2020 of the Advance/CSG, ISEG – Lisbon School of Economics and Management; and UIDB/00685/2020 of the Centre of Applied Economics Studies of the Atlantic, School of Business and Economics of the University of the Azores. ---%ABS3%
publishDate 2023
dc.date.none.fl_str_mv 2023-05-22T22:17:29Z
2023-11-08
2023-11-08T00:00:00Z
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/10362/153045
url http://hdl.handle.net/10362/153045
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0959-6119
PURE: 57080747
https://doi.org/10.1108/IJCHM-05-2022-0646
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instacron_str RCAAP
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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