Properly choosing the variables to include in cluster analysis allows analysts to address a set of serious problems that can impair segmentation studies. We'll use artificial data and empirical data sets to test three methods for variable selection: an automatic variable selection algorithm and two manual processes (stepwise discriminant analysis and a stepwise analysis of variance procedure. We'll find out whether any of these methods perform well enough to reduce barriers to successful segmentation.