Customer Engagement And Retention Utilization Analytics For Retention Strategy

Customer churn is one of the major challenges faced by banking institutions as it directly impacts profitability and long-term customer relationships. This research focuses on analysing customer behaviour and predicting churn using machine learning techniques. Exploratory Data Analysis (EDA) was performed to identify patterns related to customer engagement, product usage, and financial activity. Among various models, XGBoost achieved strong performance with an accuarcy of approximately 87% and ROC-AUC score of 0.86. An interactive Streamlit dashboard was developed to visualize insights and support decision-making.