Predicting customer churn using Machine Learning

Detalhes bibliográficos
Autor(a) principal: Dias, João Pedro Rolim
Data de Publicação: 2023
Outros Autores: António, Nuno
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/160911
Resumo: Dias, J. P. R., & António, N. (2023). Predicting customer churn using Machine Learning: A case study in the software industry. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00269-9 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
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spelling Predicting customer churn using Machine LearningA case study in the software industryData MiningCustomer Churn PredictionMachine LearningSupervised LearningSaaSEconomics, Econometrics and Finance (miscellaneous)Strategy and ManagementStatistics, Probability and UncertaintyMarketingSDG 8 - Decent Work and Economic GrowthDias, J. P. R., & António, N. (2023). Predicting customer churn using Machine Learning: A case study in the software industry. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00269-9 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMSCustomer churn can be defined as the phenomenon of customers who discontinue their relationship with a company. This problem is transversal to many industries, including the software industry. This study uses Machine Learning to build a predictive model to identify potential churners in a Portuguese software house. Six popular Machine Learning models: Random Forest, AdaBoost, Gradient Boosting Machine, Multilayer Perceptron Classifier, XGBoost, and Logistic Regression, were developed to assess which one would have a better performance. The experimental results show that boosting techniques such as XGBoost present the best predictive performance. The XGBoost model presents a Recall of 0.85 and a ROC AUC of 0.86. Additionally to the model performance, the study of the model's feature importance revealed that some factors, such as the time to solve a support ticket, the type of application, the license age, and the number of incidents, significantly influence customer churn. These insights can help the software industry key drivers of churn and prioritize retention efforts accordingly.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNDias, João Pedro RolimAntónio, Nuno2023-12-022024-12-02T00:00:00Z2023-12-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18application/pdfhttp://hdl.handle.net/10362/160911eng2050-3318PURE: 74998880https://doi.org/10.1057/s41270-023-00269-9info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:43:41Zoai:run.unl.pt:10362/160911Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:16.150918Repositório 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 Predicting customer churn using Machine Learning
A case study in the software industry
title Predicting customer churn using Machine Learning
spellingShingle Predicting customer churn using Machine Learning
Dias, João Pedro Rolim
Data Mining
Customer Churn Prediction
Machine Learning
Supervised Learning
SaaS
Economics, Econometrics and Finance (miscellaneous)
Strategy and Management
Statistics, Probability and Uncertainty
Marketing
SDG 8 - Decent Work and Economic Growth
title_short Predicting customer churn using Machine Learning
title_full Predicting customer churn using Machine Learning
title_fullStr Predicting customer churn using Machine Learning
title_full_unstemmed Predicting customer churn using Machine Learning
title_sort Predicting customer churn using Machine Learning
author Dias, João Pedro Rolim
author_facet Dias, João Pedro Rolim
António, Nuno
author_role author
author2 António, Nuno
author2_role author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Dias, João Pedro Rolim
António, Nuno
dc.subject.por.fl_str_mv Data Mining
Customer Churn Prediction
Machine Learning
Supervised Learning
SaaS
Economics, Econometrics and Finance (miscellaneous)
Strategy and Management
Statistics, Probability and Uncertainty
Marketing
SDG 8 - Decent Work and Economic Growth
topic Data Mining
Customer Churn Prediction
Machine Learning
Supervised Learning
SaaS
Economics, Econometrics and Finance (miscellaneous)
Strategy and Management
Statistics, Probability and Uncertainty
Marketing
SDG 8 - Decent Work and Economic Growth
description Dias, J. P. R., & António, N. (2023). Predicting customer churn using Machine Learning: A case study in the software industry. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00269-9 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS
publishDate 2023
dc.date.none.fl_str_mv 2023-12-02
2023-12-02T00:00:00Z
2024-12-02T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/160911
url http://hdl.handle.net/10362/160911
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2050-3318
PURE: 74998880
https://doi.org/10.1057/s41270-023-00269-9
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv 18
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
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