Customer Lifetime Value with Python - Marketing Analytics Tutorial

In this video, we dive deep into the Theory of Customer Lifetime Value (CLV), an essential metric for understanding the long-term value of your customers and optimizing marketing strategies.
You’ll learn how to calculate CLV, explore different modeling approaches for customer retention, and leverage Python’s lifetimes library to enhance your analysis.

We’ll walk you through the process of:
0:00 Introduction
- Data Importing and handling outliers for clean, actionable insights
- Creating the Recency, Frequency, and Monetary Value (RFM) dataset, a key factor in CLV modeling
- Understanding and applying the BG/NBD Model to predict customer purchasing behavior
- Visualizing the Probability Alive Matrix to forecast customer activity
- Calculating the expected number of purchases in the next 6 months using the BG/NBD model
- Implementing the Gamma-Gamma Model to fine-tune your CLV predictions
- Segmenting customers based on CLV to create targeted marketing strategies

Timestamps:
0:00 Introduction
3:35 Data Cleaning
16:15 Recency, Frequency, and Monetary RFM Analysis
21:20 Applying BG/NBD Model
25:15 Probability Alive Matrix
26:40 Predicting number of purchases
28:50 Gamma Gamma Model
31:20 Customer Lifetime Value

Whether you're a marketer, data analyst, or business owner, this video will guide you step-by-step through advanced customer segmentation and marketing analytics techniques using Python.

Learn how to predict future sales, retain high-value customers, and maximize marketing ROI with actionable CLV insights