Churn analysis is a key technique used to understand and reduce this customer attrition. It involves examining customer data to identify patterns and reasons behind customer departures. By using advanced data analytics and machine learning, businesses can predict which customers are at risk of leaving and understand the factors driving their decisions. This knowledge allows companies to take proactive steps to improve customer satisfaction and loyalty.

Project Target

This project focuses on customer churn analysis for a telecom firm, showcasing end-to-end implementation from data extraction to actionable insights. It involves creating an ETL process in SQL Server, building interactive Power BI dashboards for demographic, geographic, and service usage analysis, and developing a machine learning model using Random Forest in Python to predict churn. The project equips businesses with tools to identify at-risk customers, understand churn drivers, and implement data-driven strategies to improve customer retention.