Goal:
Use data science to identify customers at risk of leaving and automate targeted retention offers that improve loyalty and revenue.
Impact Keywords:
- Churn Prediction
- Customer Retention
- Behavioral Modeling
- Revenue Optimization
Approach:
LunarTech Lab built an AI-driven customer retention engine that integrates seamlessly with CRM systems.
- Behavioral Data Aggregation:
- Consolidated data from billing, network usage, customer service, and payment patterns.
- Predictive Modeling:
- Built gradient boosting and survival-analysis models to estimate churn likelihood for each subscriber.
- Personalized Offer Engine:
- Used reinforcement learning to select the most effective retention offer based on past outcomes.
- Automated Campaign Activation:
- Integrated with marketing automation to deploy offers in real time via SMS, email, or app notifications.
- Model Interpretability Dashboard:
- Provided management with churn drivers (e.g., data speed, billing issues, service outages) for executive insight.
Summary:
This solution moves telecom retention from reactive to predictive — enabling proactive engagement that reduces churn by 10–15% and increases average revenue per user.
