Marketing researchers commonly interpret joint-space solutions as if the distances between the points from different sets are meaningful. This is our practice despite appropriate warnings from the authors of joint-space methods that the origin (or metric) of the row objects is not the same as the origin (or metric) of the column objects ? making inter-set distances meaningless. We develop a method of metric unfolding where, given only the inter-set judgments, we still retrieve a joint space in which inter-set distances are meaningful. We illustrate this method using: a) a classic car-preference data typically analyzed with MDPref, b) an example involving children?s wear in which splitting the stimuli into two groups and collecting inter-set similarities substantially reduces the data collection burden, while providing a readily interpretable perceptual map, c) individual level inter-set judgments of soft drinks to obtain individual level perceptual maps, d) adjective-association data for athletic shoes to produce a joint space for brand image, and e) asymmetric switching data from the Japanese beer market to reflect clout and vulnerability. The ability to properly employ inter-set distances as simple distances greatly facilitates interpretation of these joint-space solutions.