Two ways of representing market structures in hierarchical forms are discussed and compared. The better-known method, hierarchical clustering of similarity measures derived from aggregate brand-switching probabilities, has three drawbacks. First, the aggregate-level switching probabilities may not be representative of the majority of the individuals; therefore, inferences made about individual or segmental behavior can be misleading. Second, regardless of the level of aggregation, it is not possible to infer the choice process from typical purchase data. Finally, in this example, this method produces a tree that is driven largely by the brands with small market shares. The other method, preference-tree analysis, models brand-choice behavior as a hierarchical elimination process. The parameters of the model are estimated from the way that relative brand-choice probabilities change as the choice set is varied. Preference-tree analysis, which requires paired-comparison preference data, is shown to eliminate the drawbacks observed with hierarchical clustering.