Machine learning-based classification models for customer churn prediction in telecommunications
DOI:
https://doi.org/10.37711/repiama.2025.2.2.4Keywords:
Customer churn prediction, Customer retention, Classification modelsAbstract
Customer loss is the key challenge in the telecommunications industry, a sector known for its intense competition and a changing user dynamic. Machine learning models have been successfully used to predict churn and improve retention approaches. This work presents a bibliometric review and a scientometric analysis of studies published during 2020 and 2025 that implement classifcation models to predict churn in
call center retention campaigns. The PRISMA methodology was applied, and the literature search covered the Scopus, IEEE Xplore, arXiv, and ScienceDirect databases. The data contents were processed using Google Colab and Python to discover trends, authors involved in the analysis, and influential algorithms. The results indicated that Random Forest, XGBoost, and Neural Networks were the most used approaches, with
performances above 90 %, and explainable artifcial intelligence was increasingly used to improve model transparency. In summary, machine learning approaches perform better than traditional methods, but some challenges remain for metric standardization and realistic applications.
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Copyright (c) 2025 Merjory Grisell Candela Sevilla (Autor/a)
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