As marketing departments in companies across various industry sectors, such as banking, insurance, and financial services, continue to grow in strategic importance, they have begun to recognize the crucial role that data plays in their success. One of their universal objectives is to improve customer targeting, enhance up-sell and cross-sell opportunities, including the ability to predict the next best offer to give customers – product positioning. The purpose of this article is to illustrate two tree-based approaches of product positioning. One is a statistics based paradigm using “if-then” rules, like CHAID, and the other is a machine learning based paradigm using genetic analysis and modeling, like The GenIQ Model©. I demonstrate the two approaches with case studies from the financial services sector.