In the rapidly evolving banking sector, customer retention has become a critical concern. Banks are increasingly seeking to understand the factors that influence customer decisions to stay with or leave their banking service provider. This project focuses on analyzing a dataset containing various attributes of bank customers to identify key predictors of customer churn. By leveraging data analytics, we aim to uncover patterns and insights that could help devise strategies to enhance customer retention and reduce churn rates.
- [Numpy]
- [Pandas]
- [Matplotlib]
- [Seaborn]
- Expand Marketing Efforts in Germany and Spain: Since 50% of customers are from France, focus marketing campaigns on Germany and Spain to boost customer acquisition in these regions.
- Develop Targeted Offers for Female Customers: Introduce specific products or offers aimed at attracting more female customers to balance the customer demographics.
- Enhance After-Sales Service: Address the fact that almost 99% of customers who filed complaints have left the bank by significantly improving the aftersales service experience.
- Create Retention Strategies for Multi-Product Holders: Implement targeted retention strategies for customers with three or more products, as they have a higher churn rate.
- Engage Zero Balance Account Holders: Investigate why approximately 3,000 accounts have zero balance and develop offers or incentives to engage these customers and encourage account usage.
- Financial Counseling for At-Risk Customers: Analyze factors influencing customer exit versus retention and offer financial counseling to customers in vulnerable salary brackets to reduce churn.