Customer Segmentation: An application to dental medicine patients

Detalhes bibliográficos
Autor(a) principal: Gonçalves, Tiago Nobre Caldeira
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/149947
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Customer Segmentation: An application to dental medicine patientsClusteringCustomer SegmentationRFMK-MeansSelf-organizing mapsInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceCustomer segmentation allows to divide a company’s customers into multiple market segments, enabling the development of customized marketing actions based on each segment’s characteristics. This work describes the application of a customer segmentation approach to the patients of a Portuguese dental company. The approach taken to select the feature subset for the final model was mostly based on the LRFM (length, recency, frequency, and monetary) model, and the monetary variable was split into multiple variables according to the treatment category where the amount was spent. K-Means and Self-organizing maps were used to cluster the company’s patients using these variables, and the results returned by both algorithms are compared. The final solution was obtained with K-Means, and 7 clusters of patients were identified. An overview of the 7 clusters is provided, and possible marketing actions are suggested based on their main characteristics. The results allowed the company to understand how its turnover was distributed across segments, and to develop an initiative to contact the patients belonging to a segment where most of them did not have an appointment in one of the company’s clinics for a long time.Pinheiro, Flávio Luís PortasRUNGonçalves, Tiago Nobre Caldeira2023-03-03T11:39:17Z2023-01-262023-01-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/149947TID:203240669enginfo: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:RCAAP2024-03-11T05:31:50Zoai:run.unl.pt:10362/149947Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:54.914386Repositório 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: An application to dental medicine patients
title Customer Segmentation: An application to dental medicine patients
spellingShingle Customer Segmentation: An application to dental medicine patients
Gonçalves, Tiago Nobre Caldeira
Clustering
Customer Segmentation
RFM
K-Means
Self-organizing maps
title_short Customer Segmentation: An application to dental medicine patients
title_full Customer Segmentation: An application to dental medicine patients
title_fullStr Customer Segmentation: An application to dental medicine patients
title_full_unstemmed Customer Segmentation: An application to dental medicine patients
title_sort Customer Segmentation: An application to dental medicine patients
author Gonçalves, Tiago Nobre Caldeira
author_facet Gonçalves, Tiago Nobre Caldeira
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Gonçalves, Tiago Nobre Caldeira
dc.subject.por.fl_str_mv Clustering
Customer Segmentation
RFM
K-Means
Self-organizing maps
topic Clustering
Customer Segmentation
RFM
K-Means
Self-organizing maps
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-03-03T11:39:17Z
2023-01-26
2023-01-26T00:00:00Z
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/10362/149947
TID:203240669
url http://hdl.handle.net/10362/149947
identifier_str_mv TID:203240669
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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