To evaluate the goodness of models we have a whole lot of KPI with a set of underlying ideas of “what goodness is”. However, the impact on the real-world (when the model is applied there) is very rarely taken systematically into account when the goodness of models is evaluated. Here we show a simple idea to assess the expected improvement (win) an a real-word situation for any classification problem at the example of campaign optimization and credit rating. Of course, this also allows to compare the real-world impact on win of different classifiers.