This paper describes a new approach for analyzing customer behavior from data collected in customer relationship management systems. The very short response times of the new approach allows one to browse interactively through the variables describing a customer. This allows discovery and understanding of behavior patterns with little effort. The same approach can also provide customer segmentation into segments of similar behavior without the need to define criteria for similarity in advance. Thirdly the approach is able to make real time predictions about user behavior which can be used to personalize web pages, make caller specific offers in call centers, or to target campaigns. A case study from an online computer magazine is presented.