Lilly Tech Systems
AI Churn Prediction
Learn to build machine learning models that predict customer churn before it happens. Design proactive retention strategies and early warning systems that reduce attrition and maximize customer lifetime value.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understanding churn: types, causes, business impact, and why predicting churn is more valuable than reacting to it.
2. Data & Features
Behavioral signals, usage patterns, engagement metrics, and customer health indicators that predict churn risk.
3. Churn Models
Classification models, survival analysis, and deep learning approaches for predicting customer attrition probability.
4. Early Warning Systems
Building real-time churn risk monitoring, alert triggers, and automated intervention workflows.
5. Retention Strategies
AI-powered retention campaigns, personalized offers, proactive outreach, and win-back programs.
6. Measurement
Measuring retention ROI, model performance tracking, and building a culture of proactive customer success.
What You'll Learn
By the end of this course, you'll be able to:
Predict Churn
Build ML models that identify at-risk customers weeks or months before they leave, giving your team time to intervene.
Reduce Attrition
Design targeted retention campaigns powered by churn predictions that keep your most valuable customers engaged.
Detect Early Signals
Build automated early warning systems that monitor customer health in real time and trigger proactive outreach.
Measure Impact
Quantify the ROI of your churn prevention programs and continuously improve model accuracy and retention effectiveness.