Methods of variable pre-selection for net score modeling

Purpose – This paper aims to focus on different approaches to variable pre-selection for building net score models (also known as uplift modelling or incremental response modelling). The application of these models supports the identification of customers whose response can be traced back to be an effect of the campaign under consideration. Design/methodology/approach – First, a net scoring methodology based on decision trees is presented. Then, derived from research contributions on this subject and analytics performed on real data from the financial sector, different approaches of variable pre-selection are discussed and compared numerically. Findings – Net- χ 2 and net information value as well as the rank lift impact correlation for interval variables would be preferred when performing variable pre-selection for net score models. Simulations showed that the results were relatively stable with respect to the number of cross-validation samples. Practical implications – Variable pre-selection is required since it reduces computational effort that comes along with the complexity of net score models and the availability of a large amount of potential predictors. Some pre-selection methods result in a set of predictors quite close to the application of net scores itself. Originality/value – Despite its lever on the effectiveness of marketing campaigns, only few contributions address net scores up to now and yet fewer authors deal with variable pre-selection for those models. In this regard, this article is the first to develop and compare different approaches.