Using cluster analysis for market segmentation - typical misconceptions, established methodological weaknesses and some recommendations for improvement

Despite the wide variety of techniques available for grouping individuals into market segments on the basis of multivariate survey information, clustering remains the most popular and most widely applied method. Nevertheless, a review of the application of such data-driven partitioning techniques reveals that questionable standards have emerged. For instance, the exploratory nature of partitioning techniques is typically not accounted for, crucial parameters of the algorithms used are ignored, thus leading to a dangerous black-box approach, where the reasons for particular results are not fully understood, pre-processing techniques are applied uncritically leading to segmentation solutions in an unnecessarily transformed data space, etc. This study aims at revealing typical patterns of data driven segmentation studies, providing a critical analysis of emerged standards and suggesting improvements.