In many database marketing applications the goal is to predict the customer behavior based on their previous actions. A usual approach is to develop models which maximize accuracy on the training and test sets and then apply these models on the unseen data. We show that in order to maximize business payoffs, accuracy optimization is insufficient by itself, and explore different strategies to take the customer value into account. We propose a framework for comparing payoffs of different models and use it to compare a number of different approaches for selecting the most valuable subset of customers. For the two datasets that we consider, we find that explicit use of value information during the training process and stratified modelling based on value both perform better than post processing strategies.