Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining

Detalhes bibliográficos
Autor(a) principal: Caetano, Joana Maria Pereira Lupi
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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spelling 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:RCAAP2023-07-10T15:58:47ZPortal AgregadorONG
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
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repository.mail.fl_str_mv
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