Welcome to the Bank Customer Churn Analysis repository! 🚀 This project dives into the factors affecting customer churn in the banking sector, utilizing a Power BI dashboard to visualize and analyze key features from our dataset.
Customer churn is a critical issue for banks, and understanding the underlying factors can help develop effective retention strategies. Our dashboard explores how various features impact the churn rate, including:
- Age
- Tenure
- Has Credit Card
- Number of Products
- Balance
- Credit Score
- Geography
- Exited
- Gender
- Customer ID
The dataset is available in this repository for you to explore! 🗂️
Our analysis provides answers to vital questions concerning customer churn, highlighting trends and areas for improvement:
-
Churn by Location 🌍
The analysis reveals that Germany has a significantly higher churn rate compared to France and Spain, indicating potential issues in that region that require immediate attention. -
Churn by Gender 🚻
Female customers exhibit a higher churn rate (0.25) compared to males (0.16). This suggests the need for gender-specific engagement strategies to improve retention among female customers. -
Churn by Tenure ⏳
While there is generally high churn across different tenures, a significant drop occurs at 7 years. This suggests that customer incentives and loyalty points could effectively reduce churn rates among long-term customers. -
Churn by Age 🎂
A visible positive correlation exists between age and churn, with the age group 50-64 experiencing the highest churn rate of 53%. Targeted services and strategies for this age group could enhance customer retention. -
Churn by Credit Score 💳
Customers with lower credit scores tend to churn more than those with higher scores, possibly due to financial instability. Tailored retention strategies for customers with lower credit scores are essential. -
Churn by Number of Products 📦
Customers with 1-2 products generally have lower churn rates compared to those with 3-4 products. This may indicate varying service-related issues that need addressing.
Feel free to check out the dataset and the Power BI dashboard included in this repository. Dive into the data, explore the insights, and let's work together to develop strategies that encourage retention and reduce churn!