2015 Volume 4 Issue 1 Pages 3-15
The multi-layer cluster analysis specifies a hierarchical structure which consists of layers specified in advance, where each layer has clusters, and allocates brands to one of the clusters at each layer which minimizes a badness of fit measure based on the sum of squares of deviations in each cluster. The multi-layer cluster analysis is evaluated by analyzing brand switching data among eight soft drink brands practically. The result shows that the difference between two groups; one comprised of Coke and Pepsi which are non-diet cola brands, and the other comprised of all diet brands and non-diet lemon-lime brands, is most important in the brand switching for consumers. This tells that the difference between the two groups is the primary concern of the consumers in the brand switching, suggesting that a marketing strategy which focuses on the difference between the two groups is desirable in the marketing of these eight soft drink brands. This shows that the multi-layer cluster analysis is effective in data analysis practically such as analyzing brand switching data. The multiple-layer cluster analysis can represent relationships among objects of after brand switching based on brands of before brand switching. This seems useful in designing the display shelf. But the multiple-layer cluster analysis cannot directly represent asymmetric relationships among brands in brand switching, as asymmetric multidimensional scaling and asymmetric cluster analysis can.
