Data Reduction
Cluster analysis is used to identify segments; that is, groups of respondents with similar patterns of responses across a set of variables. Several alternative cluster solutions are examined. Our goal is to find segments that are both identifiable and actionable.
Cluster analysis is one of the most subtly complex multivariate procedures. Much of the complexity results from selecting the subset of variables you use to form your clusters and how you transform those variables prior to running the clustering algorithm.
Another data reduction technique that is very useful in segmentation studies is perceptual mapping. Perceptual maps show a two-dimensional picture of the relationship between segment membership and descriptive variables (i.e. attitudinal and behavioral measures). The perceptual map “reduces" the data to the key underlying dimensions. We might find that one segment separates from the others in a way not obvious from the cross-tabulation data. We might also find that certain descriptive variables are related, thus improving our understanding of the segments and their characteristics.