Interactive Power BI dashboard designed to analyze customer churn patterns, identify retention opportunities, and provide actionable business insights using telecom customer data.
Customer churn is one of the most critical business metrics for subscription-based companies. This project analyzes customer behavior, contract information, payment methods, tenure, and service usage to identify factors contributing to customer churn.
The dashboard provides business intelligence insights that help organizations improve customer retention strategies and reduce customer attrition.
- Analyze customer churn trends
- Identify high-risk customer segments
- Understand customer retention patterns
- Evaluate contract and payment method impact
- Track business KPIs
- Generate actionable business insights
Dataset: Telco Customer Churn Dataset
Total Records:
- 7,043 Customers
Features:
- Customer Demographics
- Contract Type
- Monthly Charges
- Total Charges
- Internet Services
- Payment Methods
- Tenure Information
- Churn Status
- Total Customers
- Active Customers
- Churned Customers
- Churn Rate
- Revenue Insights
- Gender Analysis
- Senior Citizen Analysis
- Partner & Dependents Analysis
- Month-to-Month Contracts
- One-Year Contracts
- Two-Year Contracts
- Electronic Check
- Credit Card
- Bank Transfer
- Mailed Check
- Internet Services
- Phone Services
- Streaming Services
- Security Services
- Customer Retention Trends
- Churn Drivers
- High-Risk Customer Groups
- Power BI
- Python
- Pandas
- Excel
- Data Analytics
- Business Intelligence
- Data Visualization
customer-churn-analysis-dashboard
│
├── dashboard
│ └── Telco_Customer_Churn_Analysis.pbix
│
├── dataset
│ └── Telco_Customer_Churn_Dataset.csv
│
├── screenshots
│ ├── dashboard-overview.png
│ ├── churn-analysis.png
│ ├── customer-segmentation.png
│ └── kpi-dashboard.png
│
├── README.md
└── LICENSE
- Month-to-month contract customers show significantly higher churn rates.
- Long-term contract customers demonstrate stronger retention.
- Electronic check users are more likely to churn.
- Higher tenure customers tend to remain loyal.
- Service usage patterns directly influence customer retention.
- Machine Learning Churn Prediction
- Customer Lifetime Value Analysis
- Retention Recommendation System
- Real-Time Analytics Dashboard
- Advanced Predictive Modeling
Syed Shahed
AI Engineer | Data Analyst | Machine Learning Enthusiast
GitHub: https://github.com/Syed-SS
LinkedIn: https://linkedin.com/in/syedshahed-ai
This project is licensed under the MIT License.