Author(s): Timm Euler
This paper summarises a successful application of Knowledge Discovery in Databases (KDD) in an Italian telecommunications research lab. The aim of the application was to predict customer churn behaviour. A critical success factor for this application was clever preprocessing of the given data, in particular the construction of derived predictor features. The application was realised in the MiningMart KDD system, whose particular strength is data preprocessing on a conceptual level. Since MiningMart provides a declarative, yet executable model of the presented application, this model could be published in a central repository of KDD models, where it is publicly inspectable, which complements the descriptions in this paper.