This project analyzes telecom customer churn from both customer-risk and revenue-risk angles. The goal is to identify which customers are more likely to leave, understand the major churn drivers, and estimate which customer groups can create higher monthly revenue exposure.
This dashboard visualizes churn patterns, customer risk levels, and revenue at risk using insights from the ML model.
It highlights key KPIs, contract-wise losses, churn probability distribution, and high-value high-risk customers to support data-driven retention decisions.
Project Flow:
Data Preparation → Churn Analysis → Model Building → Risk Scoring → Revenue Impact → Power BI Dashboard
Tools Used:
Python, Pandas, NumPy, Scikit-learn, Power BI
Models Used:
Logistic Regression, Decision Tree
Built classification models to predict customer churn using Logistic Regression and Decision Tree.
Key features used in the model included contract type, tenure, monthly charges, total charges, payment method, internet service, and service usage.
Logistic Regression was selected as the final model because it gave better recall and a balanced F1-score. In churn analysis, recall is important because missing a likely churn customer can mean missing a retention opportunity.
| Model | Accuracy | Precision | Recall | F1-Score |
|---|---|---|---|---|
| Logistic Regression | 0.750 | 0.518 | 0.826 | 0.636 |
| Decision Tree | 0.745 | 0.512 | 0.791 | 0.622 |
Logistic Regression performed better with higher recall and overall balanced performance, so it was selected as the final model. The model was used to assign churn probabilities, identify high-risk customers (>0.6), and estimate revenue at risk.
- About 26.54% of customers are leaving.
- Senior citizens and month-to-month contract customers leave more often.
- Customers paying by electronic check are more likely to leave.
- Churn happens mostly in the first few months of their subscription.
- Customers with 0–3 services leave more often, while customers with 6 or more services usually stay.
The model was used to estimate risk-weighted monthly revenue exposure.
Risk-weighted exposure combines monthly charges with churn probability. It does not mean the company will definitely lose this amount. It shows where revenue is more exposed to churn risk.
- Estimated risk-weighted monthly revenue exposure is around ₹2,13,073.
- High-value customers contribute around ₹1,51,890 of this exposure.
- Month-to-month contracts have the highest churn risk and expected exposure.
- More likely to leave: high monthly bills, high total charges, using internet services, not having online backup.
- Less likely to leave: long-term contracts, tech support, phone service, longer tenure, online security.
- Focus on month-to-month customers to reduce the largest possible loss.
- Pay attention to high-value customers with high risk.
- Encourage customers to use more services like online backup and tech support.
- Offer discounts or incentives to customers with high bills.
- Engage customers early in their first months to prevent churn.
- Identified customer groups with higher churn risk.
- Connected churn probability with monthly revenue to estimate revenue exposure.
- Created a high-value high-risk customer view for retention priority.
- Supported the Power BI dashboard with model-based churn risk and revenue impact.
Project by Anurag Chauhan