[Show abstract][Hide abstract] ABSTRACT: This paper evaluates complete versus partial classification for the problem of identifying customers at risk. We define customers at risk as customers reporting overall satisfaction, but these customers also possess characteristics that are strongly associated with dissatisfied customers. This definition enables two viable methodological approaches for identifying such customers, i.e. complete and partial classification. Complete classification entails the induction of a classification model to discriminate between overall dissatisfied and overall satisfied instances, where customers at risk are defined as overall satisfied customers who are classified as overall dissatisfied. Partial classification entails the induction of the most prevalent characteristics of overall dissatisfied customers in order to discover overall satisfied customers who match these characteristics. In our empirical work, we evaluate complete and partial classification techniques and compare their performance on both quantitative and qualitative criteria. The intent of the paper is not on proving the superiority of partial classification, but rather to provide an alternative and valuable approach that offers new and different insights. In fact, taking predictive accuracy as the performance criterion, results for this study show the superiority of the complete classification approach. On the other hand, partial classification offers additional insights that complete classification techniques do not offer, i.e. it offers a rule-based description of criteria that lead to dissatisfaction for locally dense regions in the multidimensional instance space.