Market segmentation increasingly uses homogeneous groups of consumers determined on the basis of empirical market data as target segments (a posteriori-, data-driven-, post hoc segmentation) rather than splitting individuals according to single, typically socio-demographic or geographic, criteria (a priori-, commen sense segmentation). A vast amount of contributions has been made to improve methodology of identifying or constructing data-based market segments. However, real world data sets often do not contain clearly separated density clusters. Therefore all techniques used in data-based market segmentation can render multiple solutions of similar quality. So far no attempt has been made to construct a framework enabling managers to systematically choose between different segmentation solutions with regard to their practical usefulness. We propose a framework of such kind.