Predicting Customer Lifetime Value (CLV) with Machine Learning

Customer Lifetime Value (CLV) is a crucial metric for businesses, representing the total revenue a business can reasonably expect from a single customer account throughout their relationship. By understanding and predicting CLV, companies can make informed decisions about marketing, sales, and customer service strategies. In this blog post, we'll walk through a practical example of how to predict CLV using machine learning, leveraging historical transactional data. We'll cover: Data Preprocessing and Cleaning Feature Engineering using RFM (Recency, Frequency, Monetary) and other metrics Splitting data into past and future windows Training various regression models (Linear, Random Forest, XGBoost) Evaluating model performance Segmenting customers based on predicted CLV Step 1: Data Preprocessing and Cleaning Our dataset(Online Retail Dataset(UCI Repository)) consists of transactional data across two years (2009-2010 and 2010-2011). The first step is to load this data, combine it, and pe