Segmentation is not a new technique, as it is time and again used as a product-centric way of dividing a company’s population by focusing on demographics related to the business at hand ("firmographics") that align products to market segments. What is new is the process of dividing a firm's population that shifts to a customer-centric segmentation, where the polestar is on customer attributes of needs and value. The attributes appertain to the relationship between a customer and the firm, and the customer’s lifetime value. A successful customer-value based segmentation (CVS) is one that provides an understanding of when and how a customer is likely to derive value, and how the firm can effectively implement marketing programs to provide that customer value and achieve its marketing goals. The purpose of this article is to illustrate with a financial services organization case study the CVS process: 1) Perform CVS, 2) Perform targeted (response) modeling per segment from the CVS – using the GenIQ Model©, an assumption-free, nonparametric model based on the machine learning paradigm of genetic programming. The GenIQ Model offers a clear advantage over logistic response regression methods, whose performance is dependent on theoretical assumptions, a pre-specified parametric model, and data restrictions, 3) Develop segment-based positioning statements, and marketing strategies that will achieve favorable customer response, and 4) Implement segment-based marketing strategies through marketing communication and sales force activities.