Customer Segmentation & Churn Pattern Analytics in European Banking

This research paper presents a comprehensive analysis of customer churn in the European banking sector using a segmentation-driven approach. Customer churn remains a critical challenge for financial institutions, leading to reduced customer lifetime value, increased acquisition costs, and revenue instability. Despite the availability of large-scale customer data, banks often lack granular insights into which customer segments are most vulnerable to churn. The study leverages customer-level data, including demographic, geographic, and financial attributes, to identify high-risk groups. Key segmentation dimensions include geography (France, Spain, Germany), age groups, credit score bands, tenure categories, and balance segments. The analysis evaluates overall churn rates, segment-wise churn distribution, and the contribution of each segment to total churn. Additionally, the research investigates demographic patterns such as gender-based differences and geography-age interactions. A dedicated analysis of high-value customers highlights the financial impact of churn, particularly among customers with high account balances and salaries. Key performance indicators (KPIs) such as overall churn rate, segment churn rate, and geographic risk index are used to quantify insights. To enhance usability and decision-making, an interactive dashboard was developed using Streamlit, enabling dynamic filtering, KPI tracking, and drill-down analysis. The findings of this study provide actionable insights for designing targeted customer retention strategies and improving engagement in the banking sector. This work demonstrates how segmentation-based analytics can transform raw customer data into meaningful business intelligence, supporting strategic planning and data-driven decision-making in modern financial systems.