Predicting B2B customer churn and measuring the impact of machine learning-based retention strategies

Predicting B2B customer churn and measuring the impact of machine learning-based retention strategies (2025)

Authors:
Autor USP: MAZO, REYNOLD NAVARRO - ICMC
Unidade: ICMC
DOI: 10.5220/0013436300003929
Subjects: HIDROLOGIA; HIDROLOGIA; PARADIGMA CIENTÍFICO; APRENDIZADO COMPUTACIONAL; PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS); COMPORTAMENTO DO CONSUMIDOR
Keywords: Customer Churn; Churn Prediction; B2B Churn; Support Vector Machines (SVM); Feature Selection
Language: Inglês
Imprenta:
- Publisher: SciTePress
- Publisher place: Setúbal
- Date published: 2025
Source:
- Título: Proceedings
- ISSN: 2184-4992
Conference titles: International Conference on Enterprise Information Systems - ICEIS

Informações sobre o DOI: 10.5220/0013436300003929 (Fonte: oaDOI API)

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ABNT

OLIVEIRA, Victória Emanuela Alves et al. Predicting B2B customer churn and measuring the impact of machine learning-based retention strategies. 2025, Anais.. Setúbal: SciTePress, 2025. Disponível em: https://doi.org/10.5220/0013436300003929. Acesso em: 08 fev. 2026.
APA

Oliveira, V. E. A., Guimarães, A. C. da C., Quadros, A. R. S. de, Mazo, R. N., Gaspar, R. L. de S., Vieira, A., & Brandão, W. C. (2025). Predicting B2B customer churn and measuring the impact of machine learning-based retention strategies. In Proceedings. Setúbal: SciTePress. doi:10.5220/0013436300003929
NLM

Oliveira VEA, Guimarães AC da C, Quadros ARS de, Mazo RN, Gaspar RL de S, Vieira A, Brandão WC. Predicting B2B customer churn and measuring the impact of machine learning-based retention strategies [Internet]. Proceedings. 2025 ;[citado 2026 fev. 08 ] Available from: https://doi.org/10.5220/0013436300003929
Vancouver

Oliveira VEA, Guimarães AC da C, Quadros ARS de, Mazo RN, Gaspar RL de S, Vieira A, Brandão WC. Predicting B2B customer churn and measuring the impact of machine learning-based retention strategies [Internet]. Proceedings. 2025 ;[citado 2026 fev. 08 ] Available from: https://doi.org/10.5220/0013436300003929