Market segmentation is a very popular and broadly accepted way of increasing profitability. The number of reports published on a posteriori market segmentation studies has rapidly increased since Haley’s milestone publication on benefit segmentation in 1968. Nevertheless, it is common practice to use a single segmentation base only and to run a single calculation of a single algorithm, which dramatically increases the chance of building an entire marketing plan on a random solution of the algorithm chosen. The application presented constructs winter vacation styles based on guest survey data, avoiding both weaknesses mentioned before. Through the replicative framework provided by bagged clustering, potentially suboptimal random solutions are avoided. Independent partitioning of vacation activities and travel motives leads to more holistic segments. By looking for overand underrepresentation of all combinations of the behavioral and psychographic segmentation, vacation styles are identified and studied in detail.