Customer Lifetime Value Modeling
Predict and maximize CLV
Build AI models that predict customer lifetime value and optimize acquisition and retention.
Robert Thornton
Distinguished Professor of Data Analytics & Intelligence
PhD (Stanford Statistics), MS (MIT CSAIL), Ex-Amazon/Netflix Head of Data Science
or 7,900 credits
Plan: professional
What You'll Learn
Build CLV models
Optimize customer value
Course Curriculum
5 chapters · 265+ minUnderstanding customer value
Core Concept Fundamentals
Building essential knowledge of fundamental principles and methodologies in this domain.
- Understand core principles and theories
- Apply foundational frameworks correctly
- Build knowledge progressively
- Connect concepts to practical applications
Practical Application Essentials
Translating theoretical knowledge into actionable strategies.
- Connect theory to practice
- Identify application opportunities
- Execute implementations effectively
- Measure application outcomes
Lecture · Chapter 1 of 5
CLV Fundamentals
CLV Fundamentals
This lesson is CLV Fundamentals. Understanding customer value
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Course Project
CLV Prediction System
Build a CLV model.
Prerequisites
- Statistics
- ML basics
Topics Covered
Your Instructor
Robert Thornton
Distinguished
Former Netflix Head of Data Science who built recommendation systems serving 200M+ users. Creator of the RICE+ analytics framework — the definitive methodology for quantifying brand health through AI-driven metrics.
13
Courses
20K
Students
4.9
Rating
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