Module 2: Predicting Customer Lifetime Value and User Attrition

Predicting Customer Lifetime Value (CLV) is an important metric since it helps organisations understand their customer's lifespan, purchasing behaviour, and revenue from that customer. One of the best ways to predict CLV is with machine learning. This allows organisations in predicting customer behaviour, helping them to optimise marketing efforts, initiatives and budgets.

In this module, participants will learn how to answer business analytics questions about customer relationship management. Through supervised machine learning (ML) algorithms, they will learn how to predict CLV as well as translate ML performance metrics into business performance metrics. They will also learn recency, frequency, and monetary (RFM) analysis and survival analysis.

What You'll Learn

At the end of this 2-day course, participants will be able to:

• Estimate customer lifetime value using Beta Geometric (BG)/Negative Binomial Distribution (NBD), Gamma-Gamma, and Machine Learning (ML) models
• Predict customer attrition with supervised learning algorithms
• Translate model (algorithm) performance metrics into business performance metrics

Minimum Entry Requirement

Participants should have some knowledge or experience in Python programming (equivalent to that attained in SMU Academy's Professional Certificate in Python Programming programme).