Conjoint measurement is a new development in mathematical psychology that can be used to measure the joint effects of a set of independent variables on the ordering of a dependent variable. In this (primarily expository) article, the techniques are applied to illustrative problems in marketing. In addition, a number of possible areas of application to marketing research are discussed, as well as some of the methodology's limitations.

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Over the last decade, numerous methods for the multidimensional scaling (MDS) of perceptions and preferences have been applied by researchers in marketing. However, one notable gap in MDS methodology has been the lack of suitable models for analyzing inherently asymmetric data relationships. Recently, Harshman (Harshman, R. A. 1978. Models for analysis of asymmetrical relationships among objects or stimuli. Paper presented at the First Joint Meeting of the Psychometric Society and the Society for Mathematical Psychology. McMaster University, Hamilton, Ontario, August; Harshman, R. A. 1982a. DEDICOM: A family of models generalizing factor analysis and multidimensional scaling for decomposition of asymmetric relationships. Unpublished manuscript, University of Western Ontario.) has proposed a new family of models—called DEDICOM (DEcomposition into Directional COMponents)—for analyzing data matrices that are intrinsically asymmetric. In this article, the single-domain DEDICOM model is described and applied to two illustrative cases in marketing research. The examples demonstrate that DEDICOM solutions will sometimes make more substantive sense and provide significantly better fits to asymmetric data than solutions obtained by factor analysis or MDS. DEDICOM also provides a novel type of information—a description of asymmetric relations among dimensions or clusters. Such information will often have useful marketing implications.multidimensional scaling, factor analyses

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K-means clustering programs are frequently used to group buyers into market segments. Segment members exhibit similar background profiles, based on such characteristics as psychographics, benefits seeking, conjoint-based partworths, and so on. In addition, researchers also may use data on one or more exogenous variables for the same respondents. The authors describe and apply an algorithm for systematically modifying the original K-means segmentation to enhance prediction of an exogenous variable (either continuous or categorical). The modification is designed to respect a user-specified constraint on the permissible percentage decrease in the initial partition's ratio of among-groups sums of squares to total-groups sums of squares. The authors compare this approach with models drawn from the highly popular clusterwise regression literature.

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