Customer Churn Analysis Using Power BI

Abstract

1. Introduction

Predictive Churn Modeling: Identify at-risk customers before they churn, enabling proactive interventions and retention strategies.

Customer Segmentation: Group customers based on shared characteristics and churn risk, allowing for targeted marketing and engagement efforts.

2. Objective

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

This project demonstrates how Power BI can be applied to a real-world Data Science problem.

4. Technology Stack

• Power BI Desktop

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/DataVisualizationExpert/Customer-Churn-Analysis-using-Power-BI.git
cd Customer-Churn-Analysis-using-Power-BI

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by DataVisualizationExpert and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.