Abstract In the fast-changing world of e-commerce, understanding how much value a customer can bring over time is very important for business decisions. Customer Lifetime Value (CLV) is a key metric that helps companies estimate the total revenue they can expect from a customer during their relationship. This study, developed in collaboration with Smile.io, a loyalty platform for Shopify merchants, compares different approaches for calculating and predicting CLV. The focus is on both statistical models and machine learning models. The project begins by reviewing common methods for calculating CLV, including both historical and predictive techniques. Then, it explores and implements a selection of models.