Customer lifetime value (ClV) modeling in retail banking

Detalhes bibliográficos
Autor(a) principal: Santos, Tomás de Almeida dos
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/36367
Resumo: Based on regression models, simple customer’s attributes (age, income, assets and debt) - which banks usually use to identify who their most valuable customers are - were found not to be very effective at explaining and predicting customer’s Gross Income. Thus, banks are recommended to consider alternative methods. A CLV estimation model based on Markov Chains is presented and tested as a potential alternative, even though our application is still rather conceptual, with limitations which would have to be addressed in future research. Also, another methodology based on retention cohort analysis is presented, aimed at estimating CLV for individual products.
id RCAP_b1f84a5dd13bd127f240ddace4e63ba5
oai_identifier_str oai:run.unl.pt:10362/36367
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Customer lifetime value (ClV) modeling in retail bankingCustomer lifetime valueRetail bankingMarkov chainsRetention cohort analysisDomínio/Área Científica::Ciências Sociais::Economia e GestãoBased on regression models, simple customer’s attributes (age, income, assets and debt) - which banks usually use to identify who their most valuable customers are - were found not to be very effective at explaining and predicting customer’s Gross Income. Thus, banks are recommended to consider alternative methods. A CLV estimation model based on Markov Chains is presented and tested as a potential alternative, even though our application is still rather conceptual, with limitations which would have to be addressed in future research. Also, another methodology based on retention cohort analysis is presented, aimed at estimating CLV for individual products.Rocha, GonçaloRUNSantos, Tomás de Almeida dos2021-01-20T01:30:28Z2018-01-202018-01-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/36367TID:201862050enginfo: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:RCAAP2024-03-11T04:19:44Zoai:run.unl.pt:10362/36367Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:30:26.982503Repositório 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 lifetime value (ClV) modeling in retail banking
title Customer lifetime value (ClV) modeling in retail banking
spellingShingle Customer lifetime value (ClV) modeling in retail banking
Santos, Tomás de Almeida dos
Customer lifetime value
Retail banking
Markov chains
Retention cohort analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Customer lifetime value (ClV) modeling in retail banking
title_full Customer lifetime value (ClV) modeling in retail banking
title_fullStr Customer lifetime value (ClV) modeling in retail banking
title_full_unstemmed Customer lifetime value (ClV) modeling in retail banking
title_sort Customer lifetime value (ClV) modeling in retail banking
author Santos, Tomás de Almeida dos
author_facet Santos, Tomás de Almeida dos
author_role author
dc.contributor.none.fl_str_mv Rocha, Gonçalo
RUN
dc.contributor.author.fl_str_mv Santos, Tomás de Almeida dos
dc.subject.por.fl_str_mv Customer lifetime value
Retail banking
Markov chains
Retention cohort analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Customer lifetime value
Retail banking
Markov chains
Retention cohort analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Based on regression models, simple customer’s attributes (age, income, assets and debt) - which banks usually use to identify who their most valuable customers are - were found not to be very effective at explaining and predicting customer’s Gross Income. Thus, banks are recommended to consider alternative methods. A CLV estimation model based on Markov Chains is presented and tested as a potential alternative, even though our application is still rather conceptual, with limitations which would have to be addressed in future research. Also, another methodology based on retention cohort analysis is presented, aimed at estimating CLV for individual products.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-20
2018-01-20T00:00:00Z
2021-01-20T01:30:28Z
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/10362/36367
TID:201862050
url http://hdl.handle.net/10362/36367
identifier_str_mv TID:201862050
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
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_ 1799137928596684800