Churn Prediction and Customer Behavior Analysis in Telecommunications

The telecommunications industry, a linchpin of the global economy, confronts challenges, with customer churn emerging as a critical concern. This study employs a multifaceted approach to scrutinize customer churn in telecommunications, integrating demographic analyses, account information impacts, and churn predictors. Key findings underscore the nuanced relationship between customer tenure and churn, indicating the influence of contract variations on retention. Our methodology encompasses a comprehensive exploration of existing literature, an analysis of methodologies and metrics in churn prediction, and an investigation into governance and architecture within the telecommunications sector. The study leverages a diverse dataset from Kaggle, focusing on predictive modeling using XGBoost, yielding a test accuracy of 79.4%. Practical implications encompass tailored contract offerings, targeted customer engagement, and the integration of predictive modeling into customer relationship management. These insights provide telecom companies with actionable strategies to mitigate churn, foster customer loyalty, and navigate a dynamic industry landscape. However, acknowledging study limitations, such as data constraints and model assumptions, is crucial for interpreting results judiciously. Future work should explore qualitative insights, dynamic modeling considering temporal trends, and cross-industry comparative studies. As we delve deeper into the complexities of customer churn, we anticipate contributing to the resilience and adaptability of the telecom industry.