How much is the net benefit of treating an individual: cost-effective, very small, or even negative? To address this question, a methodology for developing an incremental effect model based on randomized test data is provided. The concept of an incremental effect area index is introduced for measuring the model's quality. A new variable screening technique is proposed to identify variables that are decision relevant and preferably time invariant. A piecewise linear function is created for each continuous variable to approximate the relationship between the incremental effect and the variable, based on a binning technique. Finally, a score is created as the weighted sum of a set of functions, with each function being the empirical prediction of the incremental effect based on a variable, wherein weights are chosen to maximize the incremental effect area index. The methodology creates an improved incremental effect model, leading to more cost-effective strategies in business practice.