Predicting customer churn: A case study in the software industry

Detalhes bibliográficos
Autor(a) principal: Dias, João Pedro Rolim
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/10362/159898
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
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spelling Predicting customer churn: A case study in the software industryData MiningCustomer ChurnChurn PredictionMachine LearningSupervised LearningSaaSDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and AnalyticsCustomer churn can be defined as the phenomenon of customers that 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 features’ 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.António, Nuno Miguel da ConceiçãoRUNDias, João Pedro Rolim2023-10-242025-10-24T00:00:00Z2023-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/159898TID:203385721enginfo: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:RCAAP2024-03-11T05:42:24Zoai:run.unl.pt:10362/159898Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:57:45.857940Repositório 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: A case study in the software industry
title Predicting customer churn: A case study in the software industry
spellingShingle Predicting customer churn: A case study in the software industry
Dias, João Pedro Rolim
Data Mining
Customer Churn
Churn Prediction
Machine Learning
Supervised Learning
SaaS
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Predicting customer churn: A case study in the software industry
title_full Predicting customer churn: A case study in the software industry
title_fullStr Predicting customer churn: A case study in the software industry
title_full_unstemmed Predicting customer churn: A case study in the software industry
title_sort Predicting customer churn: A case study in the software industry
author Dias, João Pedro Rolim
author_facet Dias, João Pedro Rolim
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Dias, João Pedro Rolim
dc.subject.por.fl_str_mv Data Mining
Customer Churn
Churn Prediction
Machine Learning
Supervised Learning
SaaS
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Data Mining
Customer Churn
Churn Prediction
Machine Learning
Supervised Learning
SaaS
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
publishDate 2023
dc.date.none.fl_str_mv 2023-10-24
2023-10-24T00:00:00Z
2025-10-24T00:00:00Z
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/10362/159898
TID:203385721
url http://hdl.handle.net/10362/159898
identifier_str_mv TID:203385721
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
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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
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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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