Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/113175 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
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Customer segmentation on hotel loyalty programs: leveraging loyalty with data miningLoyaltyHotel Loyalty programsData miningCustomer SegmentationClusteringK-meansDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAlthough topics of segmentation and loyalty programs are of central importance in the hospitality industry, research about alternative ways to segment loyalty program members is limited and managers tend to rely on traditional segmentation techniques. This study aims to provide a new customer segmentation solution for hotels’ loyalty programs using a data mining approach for identifying and classifying the customers into segments through clustering processes. Guests profiles were assessed with data about 498.655 loyalty members’ of Pestana Hotel Group. The K-means algorithm was applied in order to group the similar guests based on the monetary value of clients and consumption behavior. The goal is to compare the data-driven segments which are based on the customer’s monetary value, brand preferences and demographic data with the loyalty program tiers. The results demonstrate that the widely used tier-based loyalty programs are not optimal and are hiding important features that could be used to improve clients’ segmentation. Findings suggest that some high tier members generate comparatively less revenue for the hotel than lower tier ones. Hence, more efforts should be focused on truly valuable clients. Loyalty programs are not equally suitable for all guests neither for all brands within a hotel group, therefore additional levels of segmentation would be appropriate to match the distinct guests’ behavior. Data mining technologies can be extremely useful in order to support hotel managers in designing a more efficient and valuable loyalty program with tailored strategies and rewards.Rita, Paulo Miguel Rasquinho FerreiraCardoso, Elizabete Margarida FigueiredoRUNCaetano, Joana Maria Pereira Lupi2023-01-07T01:30:50Z2020-01-072020-01-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113175TID:202662187enginfo: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:RCAAP2024-03-11T04:56:23Zoai:run.unl.pt:10362/113175Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:17.454831Repositório 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 |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
title |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
spellingShingle |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining Caetano, Joana Maria Pereira Lupi Loyalty Hotel Loyalty programs Data mining Customer Segmentation Clustering K-means |
title_short |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
title_full |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
title_fullStr |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
title_full_unstemmed |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
title_sort |
Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining |
author |
Caetano, Joana Maria Pereira Lupi |
author_facet |
Caetano, Joana Maria Pereira Lupi |
author_role |
author |
dc.contributor.none.fl_str_mv |
Rita, Paulo Miguel Rasquinho Ferreira Cardoso, Elizabete Margarida Figueiredo RUN |
dc.contributor.author.fl_str_mv |
Caetano, Joana Maria Pereira Lupi |
dc.subject.por.fl_str_mv |
Loyalty Hotel Loyalty programs Data mining Customer Segmentation Clustering K-means |
topic |
Loyalty Hotel Loyalty programs Data mining Customer Segmentation Clustering K-means |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-07 2020-01-07T00:00:00Z 2023-01-07T01:30:50Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/113175 TID:202662187 |
url |
http://hdl.handle.net/10362/113175 |
identifier_str_mv |
TID:202662187 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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.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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799138034414780416 |