This paper presents the author's approach to synthesizing useful direction from product testing when the stimuli are not systematically varied. The approach presented here comprises a research design and data analysis strategy, rather than a conventional product optimization with subsequent validation. The design steps comprise stimulus selection, attribute selection, and product evaluation. The data analysis comprises univariate modeling to show how sensory attributes drive overall liking, reduction of the matrix to factor scores for multivariate modeling, and then the creation of an integrated product model. The outcome is a set of factor scores that can be translated to sensory attributes and in turn to target products.