Comparative Analysis of Machine Learning Models for Predicting Customer Churn

Thesis Abstract

Customer churn remains a critical challenge for service providers across various industries, significantly impacting revenue streams and customer retention strategies. This study addresses the need for robust predictive models capable of accurately identifying customers at risk of attrition, thereby enabling targeted intervention measures. The primary aim is to perform a comprehensive comparative analysis of prevalent machine learning techniques to determine their effectiveness in predicting customer churn within the telecommunications sector. The specific objectives include evaluating the predictive performance of algorithms such as logistic regression, decision trees, support vector machines (SVM), random forests, gradient boosting machines, and neural networks; examining their interpretability and computational efficiency; and identifying the most suitable model for deployment in real-time customer retention systems. The research adopts a quantitative, cross-sectional research design, employing a retrospective data analysis approach. The population comprises customer records from a major telecommunications company over a two-year period, totaling approximately 50,000 customer profiles. A stratified random sampling technique is utilized to select a representative sample of 10,000 customers, ensuring balanced inclusion across demographic groups, service plans, and usage patterns. Data sources include company CRM databases and billing systems, with data collection instruments encompassing customer demographic information, service usage metrics, billing history, customer interaction records, and churn status. Prior to analysis, data undergo extensive preprocessing, including cleaning, handling missing values, feature extraction, and normalization. The study employs a suite of machine learning algorithms, implemented via Python’s scikit-learn library, to develop predictive models. Performance evaluation hinges on metrics such as accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic (ROC) curve. Model comparison incorporates cross-validation, hyperparameter tuning through grid search, and statistical significance testing via ANOVA and post-hoc analysis to ascertain differences in predictive efficacy. Additionally, interpretability techniques, including feature importance analysis and partial dependence plots, are used to elucidate model insights. The study also explores the theoretical frameworks of the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) to contextualize customer behavioral dynamics influencing churn. Expected findings suggest that ensemble methods like random forests and gradient boosting machines will outperform traditional models such as logistic regression and decision trees in predictive accuracy, with neural networks demonstrating potential for high performance but at the cost of interpretability. The analysis is anticipated to reveal key predictors of churn, including contract type, customer service interactions, billing issues, and usage patterns. The study aims to establish a ranking of models based on a trade-off between predictive performance and operational feasibility, offering practical guidance for scalable deployment in customer retention systems. This research contributes to knowledge by systematically comparing machine learning algorithms within a real-world telecommunications context, filling existing gaps around model interpretability and operational deployment. It offers valuable insights into the relative strengths and limitations of various models, grounded in empirical evidence, and provides a framework for future studies on predictive analytics in customer relationship management. The conclusions emphasize the importance of selecting models aligned with organizational needs, data characteristics, and computational resources. Recommendations include adopting ensemble learning techniques for improved churn prediction, integrating interpretability tools for managerial decision-making, and developing dynamic, real-time churn prediction dashboards. It also advocates for further research into hybrid models and the inclusion of customer sentiment analysis to enhance predictive robustness. Overall, the study advances knowledge in applied data science and supports telecommunications firms in optimizing customer retention strategies through data-driven decision-making.