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Conjoint analysis has become a widely accepted tool for preference measurement in marketing research, though its applicability and performance strongly depend on the complexity of the product or service. Therefore, self-explicated approaches are still frequently used because of their simple design, which facilitates preference elicitation when large numbers of attributes need to be considered. However, the direct measurement of preferences, or rather utilities, has been criticized as being imprecise in many cases. Against this background, the authors present a compositional consumer preference measurement approach based on paired comparisons, otherwise known as PCPM. The trade-off character of paired comparisons ensures that the stated judgments are more intuitive than traditional self-explicated preference statements. In contrast to the latter, PCPM accounts for response errors and thus allows for the elicitation of more precise preferences. The authors benchmark PCPM against adaptive conjoint analysis and computer-assisted self-explication of multiattributed preferences to demonstrate its relative validity and predictive accuracy in two empirical studies using complex, high-involvement products. They find that PCPM yields better results than the benchmark approaches with respect to interview length, individual hit rates, and aggregate choice share predictions.