End-to-end Telecom Customer Churn Analysis using Python for data cleaning and Power BI for interactive dashboards to uncover churn drivers, revenue loss, and retention insights.
Customer churn is a major challenge in the telecom industry. This project analyzes customer data to understand why customers leave, identify high-risk segments, and measure the financial impact of churn.
The project follows a complete analytics workflow:
- Python for data cleaning and preprocessing
- Power BI for interactive dashboards and business insights
- Source: Telecom Customer Churn Dataset
The project uses a star-schema-inspired relational model optimized for Power BI performance.
- Calculate overall customer churn rate
- Identify top churn reasons
- Analyze churn by contract, tenure, payment method, and internet type
- Measure revenue lost due to churn
- Compare pricing behavior of churned vs retained customers
-
Python (Jupyter Notebook)
- Pandas
- NumPy
- Data Cleaning & Feature Engineering
-
Power BI
- Data Modeling
- DAX Measures
- Interactive Dashboards & Slicers
The dataset was first cleaned and prepared using Python to ensure high-quality data before visualization. Data Cleaning Steps:
- Fixed encoding and formatting issues.
- Converted numeric columns (Avg Monthly Long Distance Charges,Avg Monthly GB Download).
- Standardized categorical columns (Offer, Multiple Lines, Internet Type, Online Security, Online Backup, Device Protection Plan, Premium Tech Support, Streaming TV, Streaming Movies, Streaming Music, Unlimited Data).
Some important DAX measures created for the analysis :
DAX -
Total Revenue = SUM(telecom_customer_churn_data[Total Revenue])
Total Customers = DISTINCTCOUNT(telecom_customer_churn_data[Customer ID])
Churned Customers = CALCULATE([Total Customers], telecom_customer_churn_data[Customer Status] = "Churned")
Active Customers = CALCULATE([Total Customers], telecom_customer_churn_data[Customer Status] = "Stayed")
Churn Rate (%) = DIVIDE([Churned Customers],[Total Customers],0)
Avg Tenure = AVERAGE(telecom_customer_churn_data[Tenure in Months])
Top Churn Reason =
CALCULATE(
SELECTEDVALUE(telecom_customer_churn_data[Churn Reason]),FILTER(ALL(telecom_customer_churn_data[Churn Reason]),[Churn Reason Rank] = 1)
)
KPIs
- Total Customers: 7,043
- Churn Rate: 26.54%
- Total Revenue: $21.37M
- Revenue Lost (Churned): $3.68M
- Avg Monthly Charge: $63.60
- Avg Tenure: 32.39 months
Key Insights
- Churn decreases significantly as customer tenure increases
- Early-tenure customers are most likely to churn
- Retained customers contribute the majority of revenue
Key Findings
- Top Churn Category: Competitor
- Top Churn Reason: Competitor had better devices
- Month-to-month contracts show the highest churn rate (45.84%)
- Fiber Optic customers show the highest churn volume
- Bank Withdrawal payment method has higher churn exposure
Insights
- Two-year contracts generate the highest total revenue
- Churned customers pay a higher average monthly charge
- Revenue grows with tenure, peaking for long-term customers
- Higher monthly charges increase churn risk if value expectations are unmet
- Long-term contracts significantly reduce churn
- Competitive pricing and better device offerings improve retention
- High-value customers require proactive engagement
- Early-stage customers need focused retention strategies
- Data Cleaning & Preprocessing
- Exploratory Data Analysis (EDA)
- Power BI Data Modeling
- DAX Measures & KPIs
- Dashboard Design & Storytelling
- Business Insight Generation
- Telecom companies designing churn reduction strategies
- Data analysts showcasing real-world Power BI projects
- Business stakeholders understanding churn-driven revenue loss