A New Spatial Classification Methodology for Simultaneous Segmentation, Targeting, and Positioning (STP Analysis) for Marketing Research

The Segmentation-Targeting-Positioning (STP) process is the foundation of all marketing strategy. This chapter presents a new constrained clusterwise multidimensional unfolding procedure for performing STP that simultaneously identifies consumer segments, derives a joint space of brand coordinates and segment-level ideal points, and creates a link between specified product attributes and brand locations in the derived joint space. This latter feature permits a variety of policy simulations by brand(s), as well as subsequent positioning optimization and targeting. We first begin with a brief review of the STP framework and optimal product positioning literature. The technical details of the proposed procedure are then presented, as well as a description of the various types of simulations and subsequent optimization that can be performed. An application is provided concerning consumers' intentions to buy various competitive brands of portable telephones. The results of the proposed methodology are then compared to a naive sequential application of multidimensional unfolding, clustering, and correlation/regression analyses with this same communication devices data. Finally, directions for future research are given.

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

[2]  J. Hauser,et al.  Profit Maximizing Perceptual Positions: An Integrated Theory for the Selection of Product Features and Price , 1981 .

[3]  Ulf Böckenholt,et al.  Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data , 1991 .

[4]  C. Krumhansl.  Concerning the Applicability of Geometric Models to Similarity Data : The Interrelationship Between Similarity and Spatial Density , 2005 .

[5]  Sönke Albers,et al.  An extended algorithm for optimal product positioning , 1979 .

[6]  R. Luce,et al.  Individual Choice Behavior: A Theoretical Analysis. , 1960 .

[7]  W. DeSarbo,et al.  A Generalized Normative Segmentation Methodology Employing Conjoint Analysis , 2007 .

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Robert J. Meyer,et al.  Empirical Generalizations in the Modeling of Consumer Choice , 1995 .

[10]  Paul E. Green,et al.  Models and Heuristics for Product Line Selection , 1985 .

[11]  W. DeSarbo,et al.  Product Positioning Under Price Competition , 1990 .

[12]  V. Rao,et al.  Research for product positioning and design decisions: An integrative review , 1995 .

[13]  Thomas S. Gruca,et al.  Optimal new product positioning: A genetic algorithm approach , 2003, Eur. J. Oper. Res..

[14]  R. Okafor.  Maximum likelihood estimation from incomplete data , 1987 .

[15]  Forrest W. Young,et al.  Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features , 1977 .

[16]  Y. Wind,et al.  Marketing Strategy: New Directions for Theory and Research , 1983 .

[17]  Michael K. Rich.  Marketing Engineering: : Computer‐Assisted Marketing Analysis and Planning , 1998 .

[18]  Edgar A. Pessemier,et al.  Using Laboratory Brand Preference Scales to Predict Consumer Brand Purchases , 1971 .

[19]  Allan D. Shocker,et al.  Multiattribute Approaches for Product Concept Evaluation and Generation: A Critical Review , 1979 .

[20]  Paul E. Green,et al.  A General Approach to Product Design Optimization via Conjoint Analysis , 1981 .

[21]  Helmuth Späth,et al.  Algorithm 39 Clusterwise linear regression , 1979, Computing.

[22]  Donna L. Hoffman,et al.  Constructing MDS Joint Spaces from Binary Choice Data: A Multidimensional Unfolding Threshold Model for Marketing Research , 1987 .

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

[24]  K. Seltman.  Marketing for management. , 2004, Marketing health services.

[25]  L. McAlister,et al.  Using a Variety-Seeking Model to Identify Substitute and Complementary Relationships among Competing Products , 1985 .

[26]  Ramesh Krishnamurti,et al.  A Heuristic Approach to Product Design , 1987 .

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

[28]  Richard R. Batsell,et al.  A New Class of Market Share Models , 1985 .

[29]  Wayne S. DeSarbo,et al.  A Constrained Unfolding Methodology for Product Positioning , 1986 .

[30]  Martin Natter,et al.  Segmentation-based competitive analysis with MULTICLUS and topology representing networks , 2000, Comput. Oper. Res..

[31]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[32]  W. DeSarbo,et al.  Three-way metric unfolding via alternating weighted least squares , 1985 .

[33]  Rabikar Chatterjee,et al.  Analyzing Constant-Sum Multiple Criterion Data: A Segment-level Approach , 1995 .

[34]  Helmuth Späth,et al.  A fast algorithm for clusterwise linear regression , 1982, Computing.

[35]  Wayne S. DeSarbo,et al.  A simulated annealing methodology for clusterwise linear regression , 1989 .

[36]  Allan D. Shocker,et al.  A Consumer-Based Methodology for the Identification of New Product Ideas , 1974 .

[37]  Lee G. Cooper.  Competitive Maps: The Structure Underlying Asymmetric Cross Elasticities , 1988 .

[38]  Fred S. Zufryden,et al.  ZIPMAP — A Zero-One Integer Programming Model for Market Segmentation and Product Positioning , 1979 .

[39]  Patrick Slater,et al.  THE ANALYSIS OF PERSONAL PREFERENCES , 1960 .

[40]  Paul E. Green,et al.  Recent contributions to optimal product positioning and buyer segmentation , 1989 .

[41]  Jeremy J. Michalek,et al.  Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading , 2005 .

[42]  Ajay K. Manrai,et al.  A New Multidimensional Scaling Methodology for the Analysis of Asymmetric Proximity Data in Marketing Research , 1992 .

[43]  C. Coombs.  A theory of data. , 1965, Psychology Review.

[44]  Suzanne Winsberg,et al.  A latent class vector model for preference ratings , 1993 .

[45]  G. Day.  Strategic Market Planning: The Pursuit of Competitive Advantage , 1984 .

[46]  W. Heiser,et al.  A latent class unfolding model for analyzing single stimulus preference ratings , 1993 .

[47]  R. Bordley.  Determining the Appropriate Depth and Breadth of a Firm's Product Portfolio , 2003 .

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

[49]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[50]  Ajay K. Manrai,et al.  Optimal positioning of new product-concepts: some analytical implications and empirical results , 1992 .

[51]  P. Arabie,et al.  Overlapping Clustering: A New Method for Product Positioning , 1981 .

[52]  Pradeep K. Chintagunta,et al.  Heterogeneous Logit Model Implications for Brand Positioning , 1994 .

[53]  Pierre Hansen,et al.  New Algorithms for Product Positioning , 1994 .

[54]  W. DeSarbo,et al.  Multiclus: A new method for simultaneously performing multidimensional scaling and cluster analysis , 1991 .

[55]  Steven M. Shugan,et al.  A Mathematical Programming Model for Optimal Product Line Structuring , 1977 .

[56]  Daniel Baier,et al.  Optimal product positioning based on paired comparison data , 1998 .

[57]  E. Holman.  The relation between hierarchical and euclidean models for psychological distances , 1972 .

[58]  J. H. Myers.  Segmentation and Positioning for Strategic Marketing Decisions , 1996 .

[59]  Suresh K. Nair,et al.  A new model and solution method for product line design with pricing , 2000, J. Oper. Res. Soc..

[60]  Jerrold H. May,et al.  A Simulation Comparison of Methods for New Product Location , 1983 .

[61]  Sönke Albers,et al.  A procedure for new product positioning in an attribute space , 1977 .

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

[63]  A. Bachem,et al.  A product positioning model with costs and prices , 1981 .

[64]  M. Wedel,et al.  Consumer benefit segmentation using clusterwise linear regression , 1989 .

[65]  Fred S. Zufryden,et al.  An Integer Programming Approach to the Optimal Product Line Selection Problem , 1988 .

[66]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[67]  Helmuth Späth.  Correction to algorithm 39 clusterwise linear regression , 2005, Computing.

[68]  H. Hartley.  Maximum Likelihood Estimation from Incomplete Data , 1958 .

[69]  Russell S. Winer,et al.  A Panel-Data Based Method for Merging Joint Space and Market Response Function Estimation , 1987 .

[70]  Jacques-François Thisse,et al.  Perceptual maps and the optimal location of new products , 1985 .

[71]  Ajay K. Manrai,et al.  Mds Maps for Product Attributes and Market Response: An Application to Scanner Panel Data , 1999 .

[72]  Competitive Market Structure Analysis: A Reply , 1984 .

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

[74]  Leyuan Shi,et al.  An Optimization Framework for Product Design , 2001, Manag. Sci..

[75]  Dan Horsky,et al.  An Approach to the Optimal Positioning of a New Product , 1983 .