Uplift modeling: telemarketing campaigns cost optimisation for Vodafone

Detalhes bibliográficos
Autor(a) principal: Alves, Ângelo Daniel Teixeira de Sousa
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/10362/102628
Resumo: To decrease churn Telcos have been using machine learning techniques to target the most propense customers to churn. Propensity to churn (predictive model) should not be the focus of marketeers. It is with prescriptive models that marketing campaigns can be optimized, as the focus is in finding the clients that will benefit the most from the campaigns and not the most propense to accept. The application of uplift modelling, by targeting the right customers, is shown in this thesis to increase profit by 15% and to halve the churn rate in the mobile post-paid Vodafone customers.
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spelling Uplift modeling: telemarketing campaigns cost optimisation for VodafoneUplift modellingMarketing optimisationDomínio/Área Científica::Ciências Sociais::Economia e GestãoTo decrease churn Telcos have been using machine learning techniques to target the most propense customers to churn. Propensity to churn (predictive model) should not be the focus of marketeers. It is with prescriptive models that marketing campaigns can be optimized, as the focus is in finding the clients that will benefit the most from the campaigns and not the most propense to accept. The application of uplift modelling, by targeting the right customers, is shown in this thesis to increase profit by 15% and to halve the churn rate in the mobile post-paid Vodafone customers.Qiwei HanRUNAlves, Ângelo Daniel Teixeira de Sousa2023-08-20T00:30:58Z2020-01-142020-08-202020-01-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/102628TID:202495426enginfo: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:RCAAP2024-03-11T04:48:22Zoai:run.unl.pt:10362/102628Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:45.157480Repositório 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 Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
title Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
spellingShingle Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
Alves, Ângelo Daniel Teixeira de Sousa
Uplift modelling
Marketing optimisation
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
title_full Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
title_fullStr Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
title_full_unstemmed Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
title_sort Uplift modeling: telemarketing campaigns cost optimisation for Vodafone
author Alves, Ângelo Daniel Teixeira de Sousa
author_facet Alves, Ângelo Daniel Teixeira de Sousa
author_role author
dc.contributor.none.fl_str_mv Qiwei Han
RUN
dc.contributor.author.fl_str_mv Alves, Ângelo Daniel Teixeira de Sousa
dc.subject.por.fl_str_mv Uplift modelling
Marketing optimisation
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Uplift modelling
Marketing optimisation
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description To decrease churn Telcos have been using machine learning techniques to target the most propense customers to churn. Propensity to churn (predictive model) should not be the focus of marketeers. It is with prescriptive models that marketing campaigns can be optimized, as the focus is in finding the clients that will benefit the most from the campaigns and not the most propense to accept. The application of uplift modelling, by targeting the right customers, is shown in this thesis to increase profit by 15% and to halve the churn rate in the mobile post-paid Vodafone customers.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-14
2020-08-20
2020-01-14T00:00:00Z
2023-08-20T00:30:58Z
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/10362/102628
TID:202495426
url http://hdl.handle.net/10362/102628
identifier_str_mv TID:202495426
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
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