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Most of the techniques involved in customer clustering and segmentation are based on conventional methods of quantitative analysis or traditional data mining approaches such as the K-Means algorithm. However, clustering approaches based on artificial neural networks (ANNs), evolutionary algorithms, and fuzzy methods can be more efficient since they can reveal nonlinear patterns. They also seem to be more robust in coping with noise-related issues and relevant noise handling operations. They do not make any statistical distributional assumptions regarding the nature of the data. In this article, we develop a hybrid approach based on ANNs and swarm intelligence to reveal the underlying pattern structure of customers of an insurance company in the Republic of Ireland. This model is tailored to the scope of segmenting administrative districts, or “small areas,” given policyholders’ spatial characteristics. To that …