Churn Prediction in Online Newspaper Subscriptions

Detalhes bibliográficos
Autor(a) principal: Belchior, Lúcia Madeira
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/10362/148914
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Churn Prediction in Online Newspaper SubscriptionsChurn PredictionClassificationData MiningInterpretabilityOnline SubscriptionsMachine LearningDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn today's connected world, people turn more to the web to be informed of the news. Newspapers in an online environment profit from employing subscription models. Despite that Portugal remains one of the countries with higher levels of trust in news, readers present a low propensity to subscribe. Hence, online newspapers' existing customers are a valuable asset. Therefore, it is in the best interest of such businesses to monitor these customers to identify potential churn signs down the line. Customer churn prediction models aim to identify customers most prone to attrite, allowing businesses that leverage them to improve their customer retention campaigns' efficiency and reduce costs associated with churn. Two different research approaches, namely prediction power and comprehensibility, have been at the core of churn prediction literature. Businesses need accurate models to target customers' right subset. However, many models are black-boxes and present reduced interpretability. On the other hand, understanding what drives customers to churn can support managers in making better-informed decisions. This project report presents the development of a plan to tackle churn prediction in a Portuguese newspaper with an online subscription model using Machine Learning methods. The models' performance was evaluated in two experiments. One experiment assessed the performance for all types of subscriptions and another considered only non-recurring subscriptions. The results of the first experiment were tempered by an unplanned marketing campaign that run simultaneously with the experiment on top of the contrasting contexts in which the model was trained and evaluated. On the other hand, the second experiment's results suggest that for non-recurring subscriptions, a phone call from the call centre proved to be an adequate retention measure for probable churning subscribers. Additionally, models' predictors were analysed and it was found that users with lower fidelity rates and few subscriptions present a higher propensity to cancel their subscriptions. The same occurs with users whose product is annual or longer-lasting. These findings shed light on how to minimize churn and improve reader engagement. Based on the models' results, and predictors' analysis, the newspaper decided to implement a re-engagement newsletter to keep users engaged and prevent future churn.António, Nuno Miguel da ConceiçãoFernandes, Elizabeth SilvaRUNBelchior, Lúcia Madeira2023-02-09T14:36:38Z2023-01-242023-01-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/148914TID:203219171enginfo: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:RCAAP2024-03-11T05:30:41Zoai:run.unl.pt:10362/148914Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:33.076054Repositório 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 Churn Prediction in Online Newspaper Subscriptions
title Churn Prediction in Online Newspaper Subscriptions
spellingShingle Churn Prediction in Online Newspaper Subscriptions
Belchior, Lúcia Madeira
Churn Prediction
Classification
Data Mining
Interpretability
Online Subscriptions
Machine Learning
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Churn Prediction in Online Newspaper Subscriptions
title_full Churn Prediction in Online Newspaper Subscriptions
title_fullStr Churn Prediction in Online Newspaper Subscriptions
title_full_unstemmed Churn Prediction in Online Newspaper Subscriptions
title_sort Churn Prediction in Online Newspaper Subscriptions
author Belchior, Lúcia Madeira
author_facet Belchior, Lúcia Madeira
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
Fernandes, Elizabeth Silva
RUN
dc.contributor.author.fl_str_mv Belchior, Lúcia Madeira
dc.subject.por.fl_str_mv Churn Prediction
Classification
Data Mining
Interpretability
Online Subscriptions
Machine Learning
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Churn Prediction
Classification
Data Mining
Interpretability
Online Subscriptions
Machine Learning
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 Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2023
dc.date.none.fl_str_mv 2023-02-09T14:36:38Z
2023-01-24
2023-01-24T00:00:00Z
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/10362/148914
TID:203219171
url http://hdl.handle.net/10362/148914
identifier_str_mv TID:203219171
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
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