🏦 Bank Customer Churn & Retention Analysis

Analyzing 10,000+ customer records to predict account closures and improve long-term profitability.

📸 Dashboard Preview

🎯 The Business Challenge

The primary objective was to determine why customers were leaving the bank. By identifying high-risk segments (churners), the bank can proactively offer incentives to retain them.

🧠 Key Analytical Insights

• Demographic Risk: Discovered that customers aged 45-60 have a 25% higher churn rate, suggesting a gap in retirement or long-term wealth products.
• Product Utilization: Customers with only one product (e.g., just a savings account) are the most likely to leave. Cross-selling a credit card or loan reduces churn by 18%.
• Activity Levels: "Inactive" members account for the majority of exits, regardless of their account balance.

🛠️ Technical Stack & Skills

• Tools: Microsoft Excel (Advanced), Power Query.
• Methods: Data Normalization, Correlation Analysis, and Trend Forecasting.
• Logic: Built interactive slicers for Credit Score, Geography, and Gender to allow for deep-dive segment analysis.

🚀 How to Use This Project

1. View the Dashboard: Click the "Website" link in the About section to see the full analysis.
2. Review Logic: Check the data_dictionary.md to see the features used for churn prediction.
3. Download: The raw (anonymized) dataset is available in the /data folder for further testing.