Full Python Tutorial: Customer Lifetime Value & RFM Analysis using Machine Learning

This is a full python tutorial where we analyze customer purchase behavior to predict their purchases over the next 90-days. This allows us to target customers to prevent churn and increase profitability. We use: - Pandas to create Recency-Frequency-Monetary (RFM) Features - Scikit-Learn XGBoost to create 2 predictive models (90-day spend amount and spend probability) - Plotly Dash to productionalize a solution for Marketing Teams WANT THE CODE? Join Learning Labs PRO: https://university.business-science.io/p/learning-labs-pro Table of Contents 00:00 Customer Lifetime Value with Machine Learning 01:06 Project Workflow: Pandas, Scikit Learn, Plotly Dash 04:47 Plotly Dash Demo: Customer Spend Prediction App 08:08 Business Problem: Non-Contractual Purchase Relationship | CDNOW Customer Transactions 10:47 The 3 Questions We'll Answer Today 14:07 Customer Lifetime Value Modeling: Econometric Approach (Cashflow) 16:44 Customer Lifetime Value: Machine Learning Approach 20:47 Full Code Tutorial | Customer Lifetime Value with ML in Python 20:57 Project Setup: VSCode 25:44 Customer Lifetime Value Analysis 27:46 Data Preparation 30:08 Cohort Analysis 36:53 Machine Learning Feature Engineering 37:55 Time Splitting 40:03 Feature Engineering (RFM) 41:29 Recency (R) Features 44:29 Frequency (F) Features 45:31 Monetary (M) Features 46:21 Combine RFM Features 48:51 Machine Learning CLV Analysis: Spend Amount Model 54:09 Machine Learning CLV Analysis: Spend Probability Model 57:28 Feature Importance: Spend Amount Model 59:21 Feature Importance: Spend Probability Model 1:00:52 Save Work 1:01:42 Dash App 1:04:26 How Can We Use this Information? (3 Questions) 1:06:51 Next Steps: Learning More 1:08:50 Dash App Files 1:10:00 Python Track Roadmap 1:22:12 Q&A