Direct marketing is an important tool in the promotion mix of companies, amongst which direct mailing is crucial. One approach to improve direct mail targeting is response modeling, i.e. a predictive modeling approach that assigns future response probabilities to customers based on their history with the company. The contributions to the response modeling literature are three-fold. First, we introduce well-known statistical and data-mining classification techniques (logistic regression, linear and quadratic discriminant analysis, naïve Bayes, neural networks, decision trees, including CHAID, CART and C4.5, and the k-NN algorithm) to the direct marketing community. Second, we run a predictive benchmarking study using the above classifiers on four real-life direct marketing datasets. The 10-fold cross-validated area under the receiver operating characteristics curve is used as evaluation metric. Third, we give managerial insights that facilitate the classifier choice based on the trade-off between interpretability and predictive performance of the classifier. The findings of the benchmark study show that data-mining algorithms (CHAID, CART and neural networks) perform well on this test bed, followed by simplistic statistical classifiers like logistic regression and linear discriminant analysis. It is shown that quadratic discriminant analysis, naïve Bayes, C4.5 and the k-NN algorithm yield poor performance.