Model selection for direct marketing : performance criteria and validation methods

Purpose – The purpose of this paper is to assess the performance of competing methods and model selection, which are non-trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward. Design/methodology/approach – This study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k-fold cross-validation. Systematic experiments are conducted to compare their performance. Findings – The results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten-fold cross-validation produces more accurate results than bootstrap validation. Practical implications – To select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures. Originality/value – The study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.