Reproducible Prediction Framework of Customer Churn Using Machine Learning, Advanced Data Science and Business Intelligence Techniques

Abstract

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.