Adaptive Multidimensional Scaling: Brand Positioning Based on Decision Sets and Dissimilarity Judgments

Assessing market structure by deriving a brand positioning map and segmenting customers is essential for supporting brand-related marketing decisions. We propose adaptive multidimensional scaling (ADMDS) for simultaneously deriving a brand positioning map and market segments using customer data on cognitive decision sets and brand dissimilarities. In ADMDS, the judgment task is adapted to the individual customer where dissimilarity judgments are collected only for those brands within a customers’ awareness set. Thus, respondent fatigue and unfamiliarity with the brands are circumvented thereby improving the validity of the dissimilarity data obtained, as well as the multidimensional spatial structure derived from them. Estimation of the ADMDS model results in a spatial map in which the brands and derived segments of customers are jointly represented as points. The closer a brand is positioned to a segment’s ideal brand, the higher the probability that the brand is considered and chosen. An assumption underlying this model representation is that brands within a customers’ consideration set are relatively similar. In an experiment with 200 respondents and 4 product categories, this assumption is validated. We illustrate adaptive multidimensional scaling model on commercial data for 20 midsize car brands evaluated by 212 members of an on-line consumer panel. Potential applications of the method and future research opportunities are discussed.

[1]  Joel Huber.  Book Review: Apm System for adaptive Perceptual Mapping , 1988 .

[2]  Joel Huber,et al.  APM System for Adaptive Perceptual Mapping , 1988 .

[3]  Simon J. Blanchard,et al.  Estimating Multiple Consumer Segment Ideal Points from Context-Dependent Survey Data , 2008 .

[4]  M. Wedel,et al.  Market Segmentation: Conceptual and Methodological Foundations , 1997 .

[5]  Dick R. Wittink,et al.  Commercial use of conjoint analysis in Europe: Results and critical reflections , 1994 .

[6]  Wayne S. DeSarbo,et al.  Deriving joint space positioning maps from consumer preference ratings , 2011 .

[7]  W. DeSarbo,et al.  A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/J data , 1991 .

[8]  Daniel Aloise,et al.  Extracting Summary Piles from Sorting Task Data , 2017 .

[9]  Allen M. Weiss,et al.  Vendor Consideration and Switching Behavior for Buyers in High-Technology Markets , 1995 .

[10]  Indrajit Sinha,et al.  A stochastic multidimensional unfolding approach for representing phased decision outcomes , 1996 .

[11]  James O. Ramsay,et al.  The joint analysis of direct ratings, pairwise preferences, and dissimilarities , 1980 .

[12]  Michel Wedel,et al.  Retrieving Unobserved Consideration Sets from Household Panel Data , 2005 .

[13]  Robert F. Easley,et al.  A Single Ideal Point Model for Market Structure Analysis , 1995 .

[14]  Michel Wedel,et al.  A Comparison of Multidimensional Scaling Methods for Perceptual Mapping , 1999 .

[15]  P. Nedungadi.  Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations , 1990 .

[16]  Allan D. Shocker,et al.  Goal-Derived Categories and the Antecedents of Across-Category Consideration , 1996 .

[17]  Peter M. Bentler,et al.  Improving the efficiency and effectiveness of interactively selected MDS data designs , 1979 .

[18]  Elisabeth Honka,et al.  Advertising, Consumer Awareness, and Choice: Evidence from the U.S. Banking Industry , 2016 .

[19]  Michel Wedel,et al.  Discrete and Continuous Representations of Unobserved Heterogeneity in Choice Modeling , 1999 .

[20]  Suzanne Winsberg,et al.  A latent class approach to fitting the weighted Euclidean model, clascal , 1993 .

[21]  Wagner A. Kamakura,et al.  Life-Style Segmentation with Tailored Interviewing , 1995 .

[22]  Yoshio Takane,et al.  Multidimensional successive categories scaling: A maximum likelihood method , 1981 .

[23]  Michel Wedel,et al.  A Multidimensional Scaling Model Accommodating Differential Stimulus Familiarity , 1998, Multivariate behavioral research.

[24]  Luming Wang,et al.  The Multidimensional Nature of Product Perceptions within Asia , 2015 .

[25]  P. Lenk,et al.  A latent class procedure for the structural analysis of two-way compositional data , 1993 .

[26]  Joseph L. Zinnes,et al.  Probabilistic multidimensional scaling: Complete and incomplete data , 1983 .

[27]  Michael D. Johnson,et al.  On the Perceived Usefulness of Scaling Techniques in Market Analysis , 1996 .

[28]  Barton A. Weitz,et al.  Substitution in Use and the Role of Usage Context in Product Category Structures , 1991 .

[29]  Daniel Klapper,et al.  Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data , 2010 .

[30]  J. Ramsay.  Some Statistical Approaches to Multidimensional Scaling Data , 1982 .

[31]  Wagner A. Kamakura,et al.  A picture is worth a thousand words: translating product reviews into a product positioning map , 2017 .

[32]  Rabikar Chatterjee,et al.  Deriving ultrametric tree structures from proximity data confounded by differential stimulus familiarity , 1994 .

[33]  Peter E. Rossi,et al.  Marketing models of consumer heterogeneity , 1998 .

[34]  Joseph L. Zinnes,et al.  Theory and Methods of Scaling. , 1958 .

[35]  Michel Wedel,et al.  The effects of alternative methods of collecting similarity data for Multidimensional Scaling , 1995 .

[36]  Tammo H. A. Bijmolt,et al.  Multiattribute perceptual mapping with idiosyncratic brand and attribute sets , 2012 .

[37]  J. Douglas Carroll,et al.  Psychometric Methods in Marketing Research: Part II, Multidimensional Scaling , 1997 .

[38]  Wayne S. DeSarbo,et al.  A Clusterwise Bilinear Multidimensional Scaling Methodology for Simultaneous Segmentation and Positioning Analyses , 2008 .

[39]  Haim Mano,et al.  The Effects of Familiarity on Cognitive Maps , 1990 .

[40]  Priyali Rajagopal,et al.  A New Bayesian Spatial Model for Brand Positioning , 2017 .

[41]  Ralph Katz,et al.  Alternative Multidimensional Scaling Methods for Large Stimulus Sets , 1971 .

[42]  Michel Wedel,et al.  An Exponential-Family Multidimensional Scaling Mixture Methodology , 1996 .

[43]  Kalpesh Kaushik Desai,et al.  Descriptive Characteristics of Memory-Based Consideration Sets: Influence of Usage Occasion Frequency and Usage Location Familiarity , 2000 .

[44]  I. Spence,et al.  Single subject incomplete designs for nonmetric multidimensional scaling , 1974 .

[45]  H. Bozdogan.  Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[46]  J. Lattin,et al.  Consideration: Review of Research and Prospects for Future Insights , 1997 .

[47]  David B. MacKay,et al.  Probabilistic Scaling of Spatial Distance Judgments , 1981 .

[48]  Rabikar Chatterjee,et al.  Accommodating the effects of brand unfamiliarity in the multidimensional scaling of preference data , 1992 .

[49]  W. DeSarbo,et al.  Simultaneous multidimensional unfolding and cluster analysis: An investigation of strategic groups , 1991 .

[50]  Ingwer Borg,et al.  Measuring the Similarity of MDS Configurations. , 1985, Multivariate behavioral research.

[51]  Joel Huber,et al.  Using Attribute Ratings for Product Positioning: Some Distinctions among Compositional Approaches , 1979 .

[52]  Wayne S. DeSarbo,et al.  The Spatial Representation of Heterogeneous Consideration Sets , 1995 .

[53]  J. Berge,et al.  Perceptual Mapping Based on Idiosyncratic Sets of Attributes , 1994 .

[54]  Donald W. Jackson,et al.  Examining the Practices of United States and Japanese Market Research Firms , 1994 .

[55]  Greg M. Allenby,et al.  The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis , 2013, Mark. Sci..

[56]  Wagner A. Kamakura,et al.  Tools for Market Segmentation , 2000 .

[57]  Allan D. Shocker,et al.  Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions , 1991 .

[58]  Donald R. Lehmann,et al.  The effects of fatigue on judgments of interproduct similarity , 1990 .

[59]  V. Rao,et al.  An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research , 2015 .

[60]  John Roberts,et al.  Development and Testing of a Model of Consideration Set Composition , 1991 .

[61]  Wayne S. Desarbo,et al.  The Joint Spatial Representation of Multiple Variable Batteries Collected in Marketing Research , 2001 .

[62]  Yuho Chung,et al.  Unfolding large-scale marketing data , 2010 .

[63]  Allan D. Shocker,et al.  Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions , 1991 .

[64]  B. Wernerfelt,et al.  An Evaluation Cost Model of Consideration Sets , 1990 .

[65]  Tammo H. A. Bijmolt,et al.  Judgments of brand similarity , 1998 .

[66]  G. Kalyanaram,et al.  Brand Retrieval, Consideration Set Composition, Consumer Choice, and the Pioneering Advantage , 1993 .

[67]  Rick L. Andrews,et al.  Hierarchical Bayes versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery , 2002 .