A mi predictive model for customer churn at a b2b software company

Bibliographic Details
Main Author: Bossé, Aliénor Alexia Désirée
Publication Date: 2023
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/172303
Summary: This paper proposes a machine learning model predicting customer churn at the studied B2B SaaS company. The model is based on a real-life dataset of the studied company’s active customers. The dataset considers various factors specific to B2B customers. The model is trained on this dataset and its performance is evaluated. The evaluation shows that data helps understanding some factors impacting the studied company’s customers’ behavior to churn. However, the quality of the available live data is questioned and will have to be tested in further iterations. Additionally, a new practice for customer churn management is derived from this analysis.
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling A mi predictive model for customer churn at a b2b software companyCustomer churnChurn managementChurn predictionMachine learningDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis paper proposes a machine learning model predicting customer churn at the studied B2B SaaS company. The model is based on a real-life dataset of the studied company’s active customers. The dataset considers various factors specific to B2B customers. The model is trained on this dataset and its performance is evaluated. The evaluation shows that data helps understanding some factors impacting the studied company’s customers’ behavior to churn. However, the quality of the available live data is questioned and will have to be tested in further iterations. Additionally, a new practice for customer churn management is derived from this analysis.RUNBossé, Aliénor Alexia Désirée2024-09-24T13:25:19Z2023-01-242023-01-242023-01-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/172303TID:203316436enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-09-30T01:42:17Zoai:run.unl.pt:10362/172303Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:54:18.989714Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv A mi predictive model for customer churn at a b2b software company
title A mi predictive model for customer churn at a b2b software company
spellingShingle A mi predictive model for customer churn at a b2b software company
Bossé, Aliénor Alexia Désirée
Customer churn
Churn management
Churn prediction
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short A mi predictive model for customer churn at a b2b software company
title_full A mi predictive model for customer churn at a b2b software company
title_fullStr A mi predictive model for customer churn at a b2b software company
title_full_unstemmed A mi predictive model for customer churn at a b2b software company
title_sort A mi predictive model for customer churn at a b2b software company
author Bossé, Aliénor Alexia Désirée
author_facet Bossé, Aliénor Alexia Désirée
author_role author
dc.contributor.none.fl_str_mv RUN
dc.contributor.author.fl_str_mv Bossé, Aliénor Alexia Désirée
dc.subject.por.fl_str_mv Customer churn
Churn management
Churn prediction
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Customer churn
Churn management
Churn prediction
Machine learning
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This paper proposes a machine learning model predicting customer churn at the studied B2B SaaS company. The model is based on a real-life dataset of the studied company’s active customers. The dataset considers various factors specific to B2B customers. The model is trained on this dataset and its performance is evaluated. The evaluation shows that data helps understanding some factors impacting the studied company’s customers’ behavior to churn. However, the quality of the available live data is questioned and will have to be tested in further iterations. Additionally, a new practice for customer churn management is derived from this analysis.
publishDate 2023
dc.date.none.fl_str_mv 2023-01-24
2023-01-24
2023-01-24T00:00:00Z
2024-09-24T13:25:19Z
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/10362/172303
TID:203316436
url http://hdl.handle.net/10362/172303
identifier_str_mv TID:203316436
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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