Telco Customer Churn Analysis
📌 Project Overview
This project analyzes customer churn data from a telecom company using Power BI. The goal is to identify key factors influencing customer churn and provide actionable insights through interactive visualizations.
📊 Features of the Dashboard
Total Customers & Churn Rate: Displays the overall number of customers and the percentage of churned customers.
Churn Breakdown by Contract Type: Shows how different contract types affect customer retention.
Churn by Payment Method: Highlights which payment methods are associated with higher churn rates.
Internet Service & Churn Correlation: Analyzes the impact of different internet service types on churn.
Senior Citizen Churn Analysis: Visualizes how senior citizens are more likely to churn.
Monthly Charges vs. Churn: Investigates the relationship between customer bills and churn likelihood.
Interactive Filters: Users can filter the dashboard by Payment Method, Contract Type, Senior Citizen Status, and Internet Service Type.
🔍 Key Insights
✔️ The churn rate is 26.58%. ✔️ Month-to-month contracts have the highest churn rate. ✔️ Senior citizens are more likely to churn compared to younger customers. ✔️ Fiber optic internet users churn more than DSL or non-internet users. ✔️ Electronic check payment method has the highest churn rate. ✔️ Higher monthly charges increase the chances of churn. ✔️ Customers with low tenure are at greater risk of churn.
🛠️ Tools & Technologies Used
Power BI – Data visualization and dashboard creation.
Python (optional) – For data preprocessing and transformation.
Excel/CSV – Source data format.
📂 Dataset
The dataset contains customer demographics, contract details, payment information, and internet service usage. You can find the dataset here(https://www.kaggle.com/datasets/mexwell/telecom-customer-churn).
🚀 How to Use This Dashboard
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Open the Power BI file (.pbix).
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Interact with the visualizations using the provided filters.
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Gain insights from the key metrics and trends.
🏆 Why This Project Matters
Customer churn is a significant concern for telecom companies. This analysis helps businesses understand the main reasons for customer attrition and develop strategies to improve customer retention.
📌 Repository Structure
📂 Telco-Customer-Churn-Analysis │-- 📊 PowerBI_Dashboard.pbix # Power BI dashboard file │-- 📁 Data/ # Raw and processed datasets │-- 📜 README.md # Project documentation │-- 📁 Screenshots/ # Dashboard images
📸 Screenshots
🤝 Contributing
Feel free to contribute by improving the dashboard, adding more insights, or suggesting better visualization techniques.
📧 Contact
For any queries or suggestions, feel free to reach out via LinkedIn or GitHub Issues.
⭐ If you found this project useful, give it a star on GitHub! ⭐
This project is a personal learning project.
It is NOT open source and is not licensed for public or third-party use.
You may NOT use, copy, modify, distribute, or reproduce any part of this project or its contents for any purpose.
All rights reserved.
Unauthorized use is strictly prohibited and may lead to legal consequences.
for any usage or collaboration request , please contract me via Github profile