Thesis Abstract

Abstract
Customer churn, the phenomenon where customers discontinue their services or switch to a competitor, is a critical challenge faced by companies in the telecommunications industry. To address this issue, predictive modeling techniques leveraging machine learning algorithms have emerged as valuable tools for identifying customers at risk of churn and implementing proactive retention strategies. This thesis focuses on the application of machine learning techniques to develop a predictive model for customer churn in the telecommunications industry. The research begins with a comprehensive review of existing literature on customer churn prediction, machine learning algorithms, and their applications in the telecommunications sector. The literature review highlights the significance of accurate churn prediction models in reducing customer attrition rates and enhancing business profitability. The methodology chapter outlines the research design, data collection process, feature selection techniques, model development, and evaluation methods. The research employs a dataset containing historical customer data, including demographic information, usage patterns, and churn status, to train and test the predictive model. Feature engineering and selection processes are implemented to identify the most relevant predictors of churn. The findings chapter presents the results of the predictive modeling process, including model performance metrics, feature importance analysis, and insights gained from the analysis. The predictive model demonstrates promising performance in identifying customers at risk of churn, with high accuracy, precision, and recall rates. The analysis also reveals the key factors influencing customer churn in the telecommunications industry, such as service quality, pricing strategies, and competition. In conclusion, the study highlights the importance of leveraging machine learning techniques for customer churn prediction in the telecommunications industry. The developed predictive model serves as a valuable tool for telecom companies to proactively address customer attrition, enhance customer retention strategies, and improve overall business performance. The research contributes to the existing body of knowledge on customer churn prediction and provides practical insights for industry practitioners and researchers. Overall, this thesis demonstrates the effectiveness of machine learning algorithms in predictive modeling of customer churn and emphasizes the significance of proactive churn management strategies in the telecommunications industry. The insights gained from this research can help telecom companies optimize their customer retention efforts, reduce churn rates, and foster long-term customer relationships.

Thesis Overview