CUSTOMER BEHAVIOUR ANALYSIS AND CHURN PREDICTION IN COMMUNICATIONS AND AI

Abstract

Customer churn has emerged as a key worry for the telecommunications industry, which is a crucial sector of the global economy. This study uses a multimodal method that incorporates churn predictors, account information implications, and demographic analysis to examine telecommunications customer turnover. Important discoveries highlight the complex relationship between client tenure and churn and show how contract modifications affect retention.

Our approach consists of a thorough review of the literature, a study of churn prediction techniques and indicators, and an examination of the architecture and governance of the telecom industry. Utilizing a wide range of Kaggle datasets, the research focuses on predictive modeling with XGBoost and achieves 79.4% test accuracy.

Tailored contract offers, focused client interaction, and incorporating predictive modeling into CRM are examples of practical consequences. Telecom firms may use these data to develop customer loyalty, reduce attrition, and successfully negotiate a changing sector.

It is imperative to recognize the limits of the study, including data constraints and model assumptions, in order to appropriately interpret the findings. Future research should investigate cross-industry comparative studies, dynamic modeling taking temporal trends into account, and qualitative findings. We hope to strengthen the telecom industry's agility and resilience as we continue to explore the intricacies of customer turnover.