The telecommunications sector has evolved in recent years, resulting in intense competition and high customer acquisition costs. As a result, retaining customers has become a key concern for telecom operators. In this work, we propose the design and implementation of a complete customer churn prediction system that combines data science, machine learning and business intelligence approaches. The methodology is structured into five main steps: exploratory data analysis, development of an ETL pipeline, feature engineering, predictive modeling using a Random Forest algorithm, and the creation of decision-support dashboards in Power BI. Random Forest demonstrated higher performance with AUC-ROC of 0,85 and the results demonstrated that the main predictors of churn are monthly charges, contract type, and customer tenure. Our approach, which validated by a confusion matrix, offers decision-makers an operational tool to anticipate departures and implement targeted loyalty actions. This study proposes a reproducible methodological framework for companies facing the problem of churn and contributes to the use of machine learning in relationship marketing.
Koulou et al. (Thu,) studied this question.