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We introduce bagged clustering as a new approach in the field of post hoc market segmentation research and illustrate the managerial advantages over both hierarchical and partitioning algorithms, especially with large binary data sets. The most important improvements are enhanced stability and interpretability of segments based on binary data. One of the main goals of the procedure is to complement more traditional techniques as an exploratory segment analysis tool. The merits of the approach are illustrated using a tourism marketing application.

We introduce bagged clustering as a new approach in the field of post hoc market segmentation research and illustrate the managerial advantages over both hierarchical and partitioning algorithms, especially with large binary data sets. The most important improvements are enhanced stability and interpretability of segments based on binary data. One of the main goals of the procedure is to complement more traditional techniques as an exploratory segment analysis tool. The merits of the approach are illustrated using a tourism marketing application.

We introduce bagged clustering as a new approach in the field of post hoc market segmentation research and illustrate the managerial advantages over both hierarchical and partitioning algorithms, especially with large binary data sets. The most important improvements are enhanced stability and interpretability of segments based on binary data. One of the main goals of the procedure is to complement more traditional techniques as an exploratory segment analysis tool. The merits of the approach are illustrated using a tourism marketing application.

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 Russel Haley’s milestone publication on benefit segmentation in 1968. Nevertheless, it is common practice in market segmentation to use a single segmentation base only, thus choosing the main dimensions of interest a priori, 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 on the basis of Austrian 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 market segments. By looking for over- and under-representation of all combinations of the behavioral and psychographic segmentation, vacation styles are identified and studied in detail.

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 Russel Haley’s milestone publication on benefit segmentation in 1968. Nevertheless, it is common practice in market segmentation to use a single segmentation base only, thus choosing the main dimensions of interest a priori, 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 on the basis of Austrian 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 market segments. By looking for over- and under-representation of all combinations of the behavioral and psychographic segmentation, vacation styles are identified and studied in detail.

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 Russel Haley’s milestone publication on benefit segmentation in 1968. Nevertheless, it is common practice in market segmentation to use a single segmentation base only, thus choosing the main dimensions of interest a priori, 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 on the basis of Austrian 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 market segments. By looking for over- and under-representation of all combinations of the behavioral and psychographic segmentation, vacation styles are identified and studied in detail.

Binary survey data from the Austrian National Guest Survey conducted in the summer season of 1997 were used to identify behavioural market segments on the basis of vacation activity information. Bagged clustering overcomes a number of diffculties typically encountered when partitioning large binary data sets: The partitions have greater structural stability over repetitions of the algorithm and the question of the "correct" number of clusters is less important because of the hierarchical step of the cluster analysis. Finally, the bootstrap part of the algorithm provides means for assessing and visualizing segment stability for each input variable.

Binary survey data from the Austrian National Guest Survey conducted in the summer season of 1997 were used to identify behavioural market segments on the basis of vacation activity information. Bagged clustering overcomes a number of diffculties typically encountered when partitioning large binary data sets: The partitions have greater structural stability over repetitions of the algorithm and the question of the "correct" number of clusters is less important because of the hierarchical step of the cluster analysis. Finally, the bootstrap part of the algorithm provides means for assessing and visualizing segment stability for each input variable.