A data-driven solution that combines machine learning and business analytics to help telecommunication companies reduce customer churn and optimize retention strategies. This project leverages both supervised and unsupervised learning techniques to identify at-risk customers and develop targeted retention strategies.
- Identified distinct customer segments with churn rates between 25-27%
- Developed cluster-specific retention strategies based on customer behavior patterns
- Uncovered key churn drivers including support quality and competitive pricing
- Created actionable insights for improving customer retention across different market segments
- Customer Segmentation: Utilized K-Means clustering with optimization through the Elbow method
- Churn Prediction: Implemented Random Forest and Logistic Regression models
- Advanced Data Processing:
- Feature engineering
- Handling class imbalance through SMOTE
- Dimensionality reduction using PCA
- Comprehensive Evaluation Metrics:
- Silhouette scoring for cluster validation
- Precision-recall metrics
- ROC-AUC analysis
| Cluster | Churn Rate | Primary Churn Drivers | Key Locations |
|---|---|---|---|
| 1 | 26.92% | Support attitude, Competitive speeds | Los Angeles, San Diego |
| 2 | 25.82% | Competitive pricing, Better offers | Fresno, Berkeley |
| 3 | 26.53% | Competitive speeds, Data needs | San Jose, Sacramento |
- Support Enhancement: Implementation of improved customer service training programs
- Competitive Pricing: Development of flexible payment options and targeted promotional offers
- Service Upgrades: Introduction of enhanced data packages and speed improvements
- Loyalty Programs: Creation of segment-specific retention incentives
- Programming Language: Python
- Key Libraries:
- Scikit-learn (Machine Learning)
- Pandas (Data Processing)
- NumPy (Numerical Computing)
- Plotly/Matplotlib/Seaborn (Visualization)
- Real-time dashboard implementation for prediction tracking
- Integration with Flask/Streamlit for model deployment
- Exploration of deeper segmentation patterns
- Development of automated retention program triggers
- Implemented bias reduction techniques
- Ensured model transparency
- Focused on fair feature selection
- Protected customer privacy in analysis
Comprehensive documentation including:
- Project Proposal: Detailed problem statement and methodology
- Technical Documentation: Model architecture and implementation details
- Analysis Reports: Comprehensive insights and findings
- User Guides: Implementation and deployment instructions
- Presentation Materials: Visual summaries and key results
This project demonstrates a robust approach to solving real-world business problems in telecommunications by combining predictive modeling with customer segmentation. The integration of actionable insights ensures that businesses can proactively address churn risks while enhancing customer satisfaction.
Author: Madukoma Blessed C.
Email: [email protected]
Feel free to reach out for collaboration or inquiries!
This project was completed as part of the Programming for Data Analytics (PDAL) course at Carnegie Mellon University.