Witschel, Hans FriedrichLoo, SimonRiesen, KasparPerner, Petra2024-12-092017-10-272024-12-092015978-3-319-20910-4978-3-319-20909-810.1007/978-3-319-20910-4_2https://doi.org/10.26041/fhnw-58https://irf.fhnw.ch/handle/11654/49411Customer or market segmentation is an important instrument for the optimisation of marketing strategies and product portfolios. Clustering is a popular data mining technique used to support such segmentation – it groups customers into segments that share certain demographic or behavioural characteristics. In this research, we explore several automatic approaches which support an important task that starts after the actual clustering, namely capturing and labeling the “essence” of segments. We conducted an empirical study by implementing several of these approaches, applying them to a data set of customer representations and studying the way our study participants interacted with the resulting cluster representations. Major goal of the present paper is to find out which approaches exhibit the greatest ease of understanding on the one hand and which of them lead to the most correct interpretation of cluster essence on the other hand. Our results indicate that using a learned decision tree model as a cluster representation provides both good ease of understanding and correctness of drawn conclusions.en004 - Computer Wissenschaften, Internet330 - Wirtschaft04B - Beitrag Konferenzschrift17-31