The concept of marketing segmentation emerged in the1950s with simple demographic and psychographic components. With the advent of the information age and intensified competition, marketers have adapted more sophisticated segmentation approaches such as level of customer profitability. While both of these segmentation approaches are widely accepted, researchers have noted the need to combine these methods to enhance the current understanding of customer response models. The goal of this research is to test such a model in order to offer marketers a more efficient and effective way to tailor their marketing campaigns and allocate resources. This study demonstrates the added value of incorporating both hard (actual customer purchase) data with the softer (demographic and psychographic) data. In order to conduct the analyses, a large database was secured of over 175,000 customer purchases over a two-year time span. Within the database, each individual consumer purchase was matched against advertising exposure as well as demographic and psychographic data available from secondary sources. The goal is to not only build an applicable customer response model, but more importantly, to examine comparative models in order to assess the relative importance and contribution of each type of data. The purpose of this analysis is to suggest predictive, more cost effective, customer response profiles for both practitioners and academics struggling to better understand how to predict consumer behavior.