Customer churn is one of the most critical challenges for telecom companies, as retaining existing customers is often more cost-effective than acquiring new ones.
This project presents a multi-page Power BI dashboard built to analyze customer churn behavior, identify high-risk customer segments, evaluate service-related churn drivers, and quantify the revenue impact of customer attrition.
The dashboard transforms raw telecom customer data into actionable business insights using Power BI, DAX measures, and interactive visualizations.
- Analyze overall customer churn performance.
- Identify demographic segments associated with higher churn.
- Evaluate service-related factors influencing customer retention.
- Measure revenue impact caused by customer churn.
- Provide business recommendations to improve customer retention.
The dataset contains telecom customer information including:
- Customer demographics
- Contract details
- Internet service subscriptions
- Support and security services
- Monthly and total charges
- Customer churn status
| Metric | Value |
|---|---|
| Total Customers | 7,032 |
| Churned Customers | 1,869 |
| Retained Customers | 5,163 |
| Overall Churn Rate | 26.58% |
- Power BI Desktop
- Power Query
- DAX (Data Analysis Expressions)
- Data Visualization
- Business Intelligence
Provides a high-level summary of customer churn.
Key Metrics:
- Total Customers
- Churned Customers
- Retained Customers
- Churn Rate %
Insights:
- Overall churn rate is 26.58%.
- Majority of customers are retained.
- Month-to-month contracts form the largest customer segment.
Analyzes churn behavior across customer demographics.
Factors Analyzed:
- Gender
- Senior Citizen Status
- Partner Status
- Dependents
- Customer Tenure
Key Findings:
- Senior citizens show higher churn rates.
- Customers without partners are more likely to churn.
- Customers without dependents exhibit increased churn.
- Newer customers show higher churn compared to long-tenure customers.
Examines the relationship between telecom services and customer churn.
Factors Analyzed:
- Internet Service Type
- Contract Type
- Online Security
- Tech Support
- Payment Methods
Key Findings:
- Fiber Optic customers show higher churn.
- Month-to-month contracts are the riskiest.
- Customers without Online Security churn significantly more.
- Lack of Tech Support increases churn likelihood.
Quantifies the financial impact of customer churn.
Metrics:
- Total Revenue
- Revenue Lost
- Revenue Loss %
- Average Monthly Charges
Key Findings:
- Revenue Lost: 2.86M
- Revenue Loss Percentage: 17.83%
- Fiber Optic customers contribute the highest revenue and revenue loss.
- Reducing churn in high-value customer segments could significantly improve revenue retention.
- Customer churn is strongly associated with contract type.
- Service quality and customer support influence retention.
- New customers require stronger onboarding and engagement strategies.
- High-value Fiber Optic customers represent the largest revenue risk.
- Retention strategies focused on high-risk segments can significantly reduce revenue loss.
- Download the repository.
- Open the
.pbixfile using Power BI Desktop. - Explore dashboard pages and interact with visualizations.
- Analyze customer churn drivers and revenue impact.
- Data Cleaning
- Data Modeling
- DAX Calculations
- KPI Design
- Dashboard Development
- Business Analytics
- Customer Segmentation
- Revenue Analysis
- Data Storytelling
Developed as part of a data analytics portfolio project to demonstrate Power BI, business intelligence, and customer churn analysis skills.