ABSTRACT This paper presents a study of the attributes of margarine, showing the depth of information about consumer perceptions and drivers of liking that emerges from a detailed analysis of relations among attributes. The paper develops three sets of analyses to understand relations among attributes: principal components analysis in order to identify basic dimensions of perception, linear functions relating overall liking to attribute liking or to image ratings in order to identify drivers of liking, and quadratic functions that relate overall liking or image ratings to sensory attribute levels in order to identify optimal sensory levels and to create sensory preference segments. The analyses show how consumer data can generate learning about the consumer perceptions on the one hand, and guidance for product development.