Bank Customer Churn Analysis

Brief Overview

📊 Completed a Power BI Desktop project focused on analyzing customer churn rates. The project involved creating an interactive dashboard to visualize and analyze churn patterns based on various customer demographics and financial metrics.

Problem Statement

🛠️ The project aimed to solve several key business challenges:

• Data Cleaning and Preprocessing: Using Power BI Query to clean and preprocess the data for accurate analysis.
• Visualizing Churn Patterns: Creating visualizations to understand churn rates by credit score, country, gender, age, tenure, and account balance.
• Developing Key Performance Indicators (KPIs): Calculating KPIs such as the number of customers, number of customers lost, and churn rate using DAX commands.
• Interactive Data Analysis: Implementing slicers based on various demographics for dynamic data analysis.

🔍 This project provided insights into customer churn trends, aiding in better risk management and decision-making.

Project Structure

The project on customer churn rate analysis included the following tasks:

1. Introduction
2. Use first row as header: Set the first row of the dataset as the header.
3. Remove useless column: Removed columns that are not useful for the analysis, such as 'estimated_salary'.
4. Rename columns: Renamed columns for clarity in visualizations.
5. Prepare data types: Ensured that data types are correctly assigned for each column.
6. Add column from example: Created a new column for product names (e.g., Products Prod 1, Prod 2, etc.).
7. Replace values:
   • Replaced credit card status values with 'Owned' (1) or 'Not Owned' (0).
   • Replaced activity status values with 'Active' (1) or 'Inactive' (0).
8. Add conditional columns: Created new columns for Age Groups, Credit Scores, and Account Balance categories.
9. Create measures: In the report section, created measures for:
   • Number of customers (Customer)
   • Number of customers lost (Customer lost)
   • Churn rate
10. Data Modeling: Modeled the data for analysis and visualization.
11. Model View: Reviewed and edited relationships between tables.
12. Data Analysis: Created measures using Data Analysis Expressions (DAX) to derive insights and metrics relevant to customer churn.
13. Data Visualization: Selected and formatted visual elements to represent the data effectively.
14. Enhance Report: Used and customized themes to enhance the report's visual appeal.
15. Save & Publish: Saved the report and published it to Power BI Service for sharing and collaboration.

Key Achievements

📈 Successfully completed a comprehensive customer churn rate analysis project using Power BI Desktop. 📊 Created various visualizations, including bar plots for churn rates by credit score, country, gender, age, tenure, and account balance. 📉 Developed key performance indicators (KPIs) using DAX commands to measure the number of customers, number of customers lost, and churn rate. 🎛️ Implemented interactive slicers based on various demographics to enhance data exploration. 🛠️ Cleaned and preprocessed data using Power BI Query for accurate analysis. 🌟 This hands-on project provided practical experience in using Power BI for financial data analysis, equipping me with valuable skills for professional data analysis roles.

GitHub Repository

Dashboard