In competitive consumer markets, data mining for customer relationship management faces the challenge of systematic knowledge discovery in large data streams to achieve operational, tactical and strategic competitive advantages. Methods from computational intelligence, most prominently artificial neural networks and support vector machines, compete with established statistical methods in the domain of classification tasks. As both methods allow extensive degrees of freedom in the model building process, we analyse their comparative performance and sensitivity towards data pre-processing in real-world data. In addition to simpler configuration, support vector machines robustly outperformed various neural network paradigms in classification. Consequently, they are recommended as a contemporary method for data mining in analytical customer relationship management.