Predictive Retention: Telecom Churn Analysis
Machine learning solution predicting customer churn and handset changes using 5 years of anonymized data from 500K users for a leading Pakistani telecom company.
The Challenge
One of Pakistan's leading telecommunications companies needed to proactively identify customers likely to churn or switch handsets. With millions of users, manual analysis was impossible — they needed a data-driven approach using their 5 years of anonymized prepaid base data from 500,000 users.
Our Approach
We developed a predictive analytics pipeline from raw data to actionable retention insights.
Data Cleaning & Preprocessing
Cleaned and unified data from multiple sources including device records (MSISDN, IMEI, brand/model), usage patterns (voice calls, SMS counts), subscription data (3G/4G, duration), and ARPU metrics.
Feature Engineering
Extracted key predictive variables including call frequency, SMS usage, data consumption, subscription duration, and ARPU trends to model user behavior patterns.
Prediction Model
Implemented XGBoost to predict both churn and handset changes, leveraging the diverse and feature-rich dataset. The model analyzed behavior metrics across voice, SMS, data, and subscription dimensions.
Model Evaluation
Rigorously evaluated using accuracy, precision, recall, and F1-score to ensure reliability in production predictions.
Technical Stack
- ML: XGBoost, scikit-learn
- Data: Python, Pandas, NumPy
- Visualization: Matplotlib, Jupyter Notebook
Results
- Proactive identification of at-risk customers enabling targeted retention campaigns
- Reduced churn rates through personalized interventions
- Handset change predictions enabling proactive device upgrade offers