The authors present a model that maps competitive market structures by identifying the preference structure of each consumer segment. By marrying two different data types—switching probabilities and attribute ratings—their model divides a market into several homogeneous sub-markets in which consumers consider a distinctive subset of brands (consideration set or competitive group) with a segment-specific rule for attribute evaluations and a segment-specific ideal point. Using data published in Harshman and colleagues’ (1982) work, the authors examine the U. S. car market and find brand-loyal segments for all car types except those favored by first-time buyers, a universal market, and five switching segments that consider car groups differing in the nation of origin, size, and luxury level. Breaking the switching segment into finer partitions gives a better account of the data than the Colombo-Morrison model or an asymmetric generalization of that model. The authors advocate the development of marketing goals with respect to each of the segment maps in the hope that it will lead to more synergistic marketing strategies for brands encountering multifaceted competition.
Get full access to this article
View all access options for this article.
References
1.
AitchinsonJ., and SilveyS. D. (1960), “Maximum-Likelihood Estimation Procedures and Associated Tests of Significance,”Journal of the Royal Statistical Society, 22(1), 154–71.
2.
BettmanJames R. (1979), An Information Processing Theory of Consumer Choice.Reading, MA: Addison-Wesley Publishing Company.
3.
BourgeoisJacques C., HainesGeorge H.Jr., and SommersMontrose S. (1987), “Product Market Structure: Problems, Definitions, and Issues,” in Review of Marketing, HoustonMichael J., ed. Chicago: American Marketing Association, 327–84.
4.
BozdoganHamparsum (1994), “Mixture Model Cluster Analysis Using Model Selection Criteria and a New Informational Measure of Complexity,” in Multivariate Statistical Modeling, Volume 2: Proceedings of the First U.S./Japan Conference on the Frontiers of Statistical Modeling, BozdoganH., ed. Dordrecht, The Netherlands: Kluwer Academic Publishers, 69–113.
5.
BucklinRandolph E., and SrinivasanV. (1991), “Determining Interbrand Substitutability Through Survey Measurement of Consumer Preference,”Journal of Marketing Research, 28(February), 58–71.
6.
CarrollJ. Douglas (1972), “Individual Differences and Multidimensional Scaling,” in Multidimensional Scaling: Theory and Applications in the Behavioral Sciences, Vol. 1 (Theory), ShepardR. N., RomneyA. K., and NerloveS. B., eds. New York: Seminar Press, 106–55.
7.
CarrollJ. Douglas, and ChangJ. J. (1967), “Relating Preference Data to Multidimensional Scaling Solutions Via a Generalization of Coombs Unfolding Model,” Bell Telephone Laboratories Report. Murray Hill, N. J.
8.
ChintaguntaPradeep K. (1994), “Heterogeneous Logit Model Implications for Brand Positioning,”Journal of Marketing Research, 31 (May), 304–11.
9.
ColomboRichard A., and MorrisonDonald G. (1988), “Blacklisting Social Science Departments With Poor Ph.D. Submission Rates,”Management Science, 34(June), 696–706.
10.
ColomboRichard A., and MorrisonDonald G. (1989), “A Brand Switching Model With Implications for Marketing Strategies,”Marketing Science, 8(Winter), 89–99.
11.
Consumer Reports (1979), April, 217–48.
12.
CoombsClyde (1964), A Theory of Data.New York: John Wiley & Sons. Reprint, Ann Arbor, MI: Mathesis Press, 1976.
13.
CooperLee G. (1988), “Competitive Maps: The Structure Underlying Asymmetric Cross Elasticities,”Management Science, 34(June), 707–23.
DayGeorge, ShockerAllan D., and SrivastavaRajendra K. (1979), “Customer-Oriented Approaches to Identifying Product Markets,”Journal of Marketing, 43(Fall), 8–19.
16.
DeSarboWayne S. (1982), “GENCLUS: New Models for General Nonhierarchical Clustering Analysis,”Psychometrika, 47(December), 446–59.
17.
DeSarboWayne S., and CarrollJ. D. (1985), “Three-Way Metric Unfolding Via Alternating Weighted Least Squares,”Psychometrika, 50(September), 275–300.
18.
DeSarboWayne S., and CronW. (1988), “A Conditional Mixture Maximum Likelihood Methodology for Clusterwise Linear Regression,”Journal of Classification, 5(2), 249–82.
19.
DeSarboWayne S., and SoeteGeert De (1984), “On the Use of Hierarchical Clustering for the Analysis of Nonsymmetric Proximities,”Journal of Consumer Research, 11(1), 601–10.
20.
DeSarboWayne S., and ManraiAjay K. (1992), “A New Multidimensional Scaling Methodology for the Analysis of Asymmetric Proximity Data in Marketing Research,”Marketing Science, 11(Winter), 1–20.
21.
DeSarboWayne S., and ManraiAjay K., and ManraiLalita A. (1993), “Non-Spatial Tree Models for the Assessment of Competitive Market Structure: An Integrated Review of the Marketing and Psychometric Literature,” in Handbooks of Operations Research and Management Science: Marketing, Vol. 5, EliashbergJ., and LilienG. L., eds. Amsterdam: North Holland, 193–257.
22.
DeSarboWayne S., and ManraiAjay K., and ManraiLalita A. (1994), “Latent Class Multidimensional Scaling: A Review of Recent Developments in the Marketing and Psychometric Literature,” in Advanced Methods of Marketing Research, BagozziRichard P., ed. Oxford: Basil Blackwell, 190–222.
23.
DeSarboWayne S., and RaoV. R. (1986), “A Constrained Unfolding Methodology for Product Positioning,”Marketing Science, 5(Winter), 1–19.
24.
DeshpandéRohit, and GatignonHubert (1994), “Competitive Analysis,”Marketing Letters, 5(July), 271–88.
25.
DillonWilliam R., KumarAjith, and de BorreroMelinda Smith (1993), “Capturing Individual Differences in Paired Comparisons: An Extended BTL Model Incorporating Descriptor Variables,”Journal of Marketing Research, 30(February), 42–51.
26.
ElrodTerry (1988), “Choice Map: Inferring a Product-Market Map From Panel Data,”Marketing Science, 7(Winter), 21–40.
27.
GolembiewskiRobert T., BillingsleyKeith, and YeagerSamuel (1976), “Measuring Change and Persistence in Human Affairs: Types of Change Generated by OD Designs,”Journal of Applied Behavioral Science, 12(2), 133–57.
28.
GroverRajiv, and DillonWilliam R. (1985), “A Probabilistic Model for Testing Hypothesized Hierarchical Market Structures,”Marketing Science, 4(Fall), 21–40.
29.
GroverRajiv, and SrinivasanV. (1987), “A Simultaneous Approach to Market Segmentation and Market Structuring,”Journal of Marketing Research, 24(May), 139–53.
30.
HarshmanRichard A., GreenPaul E., WindYoram, and LundyMargaret E. (1982), “A Model for the Analysis of Asymmetric Data in Marketing Research,”Marketing Science, 1(Spring), 205–12.
31.
HauserJohn R., and ShuganSteven M. (1983), “Defensive Marketing Strategies,”Marketing Science, 2(Fall), 319–60.
32.
HendricksonA. E., and WhiteP. O. (1964), “PROMAX: A Quick Method for Rotation to Oblique Simple Structure,”British Journal of Statistical Psychology, 17(May), 65–70.
33.
InoueAkihiro (1993), “A System of Models to Identify Competitive Groups With Population Heterogeneity: MIGHT System,” unpublished research paper, John E. Anderson Graduate School of Management, University of California.
34.
JainDipak, BassFrank M., and ChenYu-Min (1990), “Estimation of Latent Class Models with Heterogeneous Choice Probabilities: An Application to Market Structuring,”Journal of Marketing Research, 27(February), 94–101.
35.
JedidiKamel, RamaswamyVenkatram, and DeSarboWayne S. (1993), “A Maximum Likelihood Method for Latent Class Regression Involving a Censored Dependent Variable,”Psychometrika, 58(September), 375–94.
36.
KalwaniManohar U., and MorrisonDonald G. (1977), “A Parsimonious Description of the Hendry System,”Management Science, 23(January), 467–77.
37.
KamakuraWagner A., and RussellGary J. (1989), “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure,”Journal of Marketing Research, 26(November), 379–90.
38.
KannanP. K., and WrightGordon P. (1991), “Modeling and Testing Structured Markets: A Nested Logit Approach,”Marketing Science, 10(Winter), 58–82.
KumarAjith, and SashiC. M. (1989), “Confirmatory Analysis of Aggregate Hierarchical Market Structures: Inferences From Brand-Switching Behavior,”Journal of Marketing Research, 26(November), 444–53.
41.
LuenbergerD. G. (1984), Linear and Nonlinear Programming, 2d ed. Reading, MA: Addison-Wesley Publishing Company.
42.
McCarthyPatrick S., KannanP. K., ChandrasekharanRadha, and WrightGordon P. (1992), “Estimating Loyalty and Switching With an Application to the Automobile Market,”Management Science, 38(October), 1371–93.
43.
McFaddenD. L. (1974), “Conditional Logit Analysis of Qualitative Choice Behavior,” in Frontiers in Econometrics, ZarembkaP., ed. New York: Seminar Press, 105–42.
44.
McLachlanGeoffrey, and BasfordKaye E. (1988), Mixture Models: Inference and Applications to Clustering.New York: Marcel Dekker.
45.
MorrisonDonald G., and SchmittleinDavid C. (1988), “Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort?”Journal of Business and Economic Statistics, 6(April), 145–66.
46.
NovakThomas P. (1993), “Log-Linear Trees: Models of Market Structure in Brand Switching Data,”Journal of Marketing Research, 30(August), 267–87.
47.
NovakThomas P., and StangorCharles (1987), “Testing Competitive Market Structures: An Application of Weighted Least Squares Methodology to Brand Switching Data,”Marketing Science, 6(Winter), 82–97.
48.
RamaswamyVenkatram, and DeSarboWayne S. (1990), “Sculptre: A New Methodology for Deriving and Analyzing Hierarchical Product-Market Structure From Panel Data,”Journal of Marketing Research, 27(November), 418–27.
49.
RaoVithala R., and SabavalaDarius J. (1981), “Inference of Hierarchical Choice Processes From Panel Data,”Journal of Consumer Research, 8(June), 85–96.
50.
RussellGary J. (1992), “A Model of Latent Symmetry in Cross Price Elasticities,”Marketing Letters, 3(2), 157–69.
51.
RussellGary J., and BoltonRuth N. (1988), “Implications of Market Structure for Elasticity Structure,”Journal of Marketing Research, 25(August), 229–41.
52.
RussellGary J., BucklinRandolph E., and SrinivasanV. (1993), “Identifying Multiple Preference Segments from Own- and Cross-Price Elasticities,”Marketing Letters, 4(1), 5–18.
53.
RussellGary J., and KamakuraWagner A. (1994), “Understanding Brand Competition Using Micro and Macro Scanner Data,”Journal of Marketing Research, 31(May), 289–303.
54.
ShockerAllan D., StewartDavid W., and ZahorikAnthony J. (1990), “Market Structure Analysis: Practice, Problems, and Promise,” in The Interface of Marketing and Strategy, DayGeorge, WeitzBarton, and WensleyRobin, eds. Greenwich, CT: JAI Press, 9–56.
55.
ShuganSteven M. (1987), “Estimating Brand Positioning Maps Using Supermarket Scanning Data,”Journal of Marketing Research, 24(February), 1–18.
56.
TitteringtonD. M., SmithA. F. M., and MakovU. E. (1985), Statistical Analysis of Finite Mixture Distributions.New York: John Wiley & Sons.
57.
TuckerLedyard R. (1951), “A Method of Synthesis of Factor Analysis Studies,”Personnel Research Section Report No. 984.Washington, DC: Department of the Army.
58.
TuckerLedyard R. (1971), “Relation of Factor Score Estimates to Their Use,”Psychometrika, 36(December), 427–36.
59.
UrbanGlen L., JohnsonPhilip L., and HauserJohn R. (1984), “Testing Competitive Market Structures,”Marketing Science, 3(Spring), 83–112.
60.
WedelMichel, and DeSarboWayne S. (1994), “A Review of Recent Developments in Latent Class Regression Models,” in Advanced Methods in Marketing Research, BagozziRichard P., ed. Cambridge: Basil Blackwell, 352–88.
61.
WinsbergSuzanne, and de SoeteGeert (1993), “A Latent Class Approach to Fitting the Weighted Euclidean Model, CLASCAL,”Psychometrika, 58(June), 315–30.
62.
ZenorMichael J., and SrivastavaRajendra K. (1993), “Inferring Market Structure With Aggregate Data: A Latent Segment Logit Approach,”Journal of Marketing Research, 30(August), 369–79.
