In a typical segmentation study, segments are identified that differ in terms of their needs, attitudes or behaviours. However, these segments rarely differ on demographic or firmographic variables that exist on a customer's database(s). Thus, it is not possible to target the segments via data on the customer's database(s). Targetable Segmentation solves this problem by linking customer data with survey data. In other words, the technique produces segments that differ in terms of their needs, attitudes or behaviours and that are also able to be identified using internal data. Cases studies are presented that show how attitudinally different segments can be identified on databases containing hundreds of thousands of records, thus making the segmentation analysis much more actionable.