Since we will use learning algorithms that can produce probability and thus can rank examples, we should use the lift instead of the predictive accuracy as the evaluation criterion. Lift analysis has been widely used in database marketing previously (Hughes, 1996). A lift reflects the redistribution of responders in the testing set after the testing examples are ranked. Clearly, if some regularities are found, the ranking of testing examples from most likely to least likely buyers would result in a distribution in which most existing buyers appear in the upper part of the ranked list. Section 5.2 discusses the lift analysis in more detail.