Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/34411
Autoria: Belchior, L. M.
António, N.
Fernandes, E.
Data: 2024
Título próprio: Online newspaper subscriptions: Using machine learning to reduce and understand customer churn
Título da revista: Journal of Media Business Studies
Volume: 21
Número: 4
Paginação: 364 - 387
Referência bibliográfica: Belchior, L. M., António, N., & Fernandes, E. (2024). Online newspaper subscriptions: Using machine learning to reduce and understand customer churn. Journal of Media Business Studies, 21(4), 364–387. https://doi.org/10.1080/16522354.2024.2343638
ISSN: 1652-2354
DOI (Digital Object Identifier): 10.1080/16522354.2024.2343638
Palavras-chave: Churn prediction
Online subscriptions
Data mining
Digital journalism
Reader engagement
Resumo: Modelling customer loyalty has been a central issue in customer relationship management, particularly in digital subscription business models. To guarantee news media sustainability, publishers implemented subscription models that need to define successful retention strategies. Thus, churn management has become pivotal in the media subscription business. The present study aims to understand what drives subscribers to churn by performing a Machine Learning approach to model the propensity to churn of online subscribers of a Portuguese newspaper. Two models were developed, tested, and evaluated in two timeframes. The first one considered all Business to Consumer (B2C) subscriptions, and the second only the B2C non-recurring subscriptions. The experimental results revealed important patterns of churners, which allowed the marketing and editorial teams to implement churn prevention and retention measures.
Arbitragem científica: yes
Acesso: Acesso Aberto
Aparece nas coleções:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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