Customer Churn Prediction System – Telecom Case Study

📊 Case Study: Customer Churn Prediction System (Telecom Industry)

🔹 1. Background

Customer churn is a major challenge in industries like telecom, banking, and SaaS.

👉 Studies show:

• Acquiring a new customer costs 5x more than retaining an existing one

• Even a 5% reduction in churn can increase profits by 25–95%

A telecom company was facing high customer attrition due to:

• Competitive pricing

• Poor service experience

• Lack of proactive engagement

🔹 2. Problem Statement

The company observed:

• Monthly churn rate: 18%

• No system to identify at-risk customers

• Marketing campaigns were inefficient and costly

👉 Objective:
Develop a predictive model to:

• Identify customers likely to churn

• Enable targeted retention strategies

• Improve customer lifetime value (CLV)

🔹 3. Dataset Description

The dataset included 10,000+ customers with features such as:

✔ Customer Demographics

• Gender

• Age

• Senior citizen (Yes/No)

✔ Account Information

• Tenure (months with company)

• Contract type (Monthly / Yearly)

• Payment method

✔ Services Used

• Internet service

• Streaming services

• Tech support

✔ Billing Information

• Monthly charges

• Total charges

✔ Target Variable

• Churn (Yes / No)

🔹 4. Data Preprocessing

• Handling missing values

• Encoding categorical variables (One-Hot Encoding)

• Feature scaling (Standardization)

• Removing outliers

• Train-test split (80:20)

🔹 5. Exploratory Data Analysis (EDA)

Key insights:

• Customers with month-to-month contracts had higher churn

• High monthly charges → higher churn probability

• Customers with no tech support churned more

• New customers (low tenure) were more likely to leave

🔹 6. Machine Learning Models Used

✔ Logistic Regression

• Baseline model

• Easy to interpret

✔ Random Forest

• Captures non-linear patterns

• High accuracy

✔ XGBoost (Best Performer)

• Handles complex relationships

• Robust to overfitting

🔹 7. Model Evaluation

The performance of the models was evaluated using key classification metrics including Accuracy, Precision, Recall, and F1-Score.

✔ Logistic Regression

The Logistic Regression model served as the baseline. It achieved an accuracy of 82%, indicating a decent overall performance. The precision was 78%, meaning most predicted churn cases were correct. However, the recall was 75%, suggesting that some actual churn cases were missed. The F1-score of 76% reflects a balanced but moderate performance.

✔ Random Forest

The Random Forest model significantly improved performance over the baseline. It achieved an accuracy of 88%, showing better prediction capability. The precision increased to 85%, indicating fewer false positives. The recall improved to 87%, meaning the model was able to detect more actual churn cases. The F1-score of 86% demonstrates a strong balance between precision and recall.

✔ XGBoost (Final Model)

XGBoost emerged as the best-performing model. It achieved an accuracy of 91%, the highest among all models. The precision was 89%, ensuring reliable identification of churn cases. The recall reached 90%, indicating excellent detection of actual churn customers. The F1-score of 89% confirms that the model provides a well-balanced and highly effective performance.

✔ Final Selection Justification

Based on the evaluation:

• XGBoost provided the highest accuracy and recall, which is critical in churn prediction

• It minimized both false positives and false negatives

• It demonstrated strong generalization capability

👉 Therefore, XGBoost was selected as the final model for deployment.

🔹 8. Key Features Influencing Churn

• Contract type

• Monthly charges

• Tenure

• Tech support availability

• Payment method

🔹 9. System Workflow

1. Customer data is collected

2. Preprocessed and fed into model

3. Model predicts churn probability

4. High-risk customers flagged

5. Retention strategies triggered (offers, discounts, support calls)

🔹 10. Business Impact

After implementation:

• Churn reduced from 18% → 11%

• Retention campaign efficiency improved by 40%

• Revenue increased by ₹12 Crores annually

• Marketing cost reduced significantly

🔹 11. Challenges Faced

• Imbalanced dataset (fewer churn cases)

• Feature selection complexity

• Model interpretability (especially XGBoost)

• Data privacy and compliance

🔹 12. Future Enhancements

• Real-time churn prediction using streaming data

• Integration with CRM systems

• Use of Deep Learning (Neural Networks)

• Personalized retention strategies using AI

🔹 13. Conclusion

The Customer Churn Prediction System helped the company:

• Proactively retain customers

• Optimize marketing spend

• Increase profitability

👉 This case highlights the power of AI-driven decision-making in business strategy.