Market Segmentation: Conceptual and Methodological FoundationsSpringer Science & Business Media, 2012 M12 6 - 382 páginas Modern marketing techniques in industrialized countries cannot be implemented without segmentation of the potential market. Goods are no longer produced and sold without a significant consideration of customer needs combined with a recognition that these needs are heterogeneous. Since first emerging in the late 1950s, the concept of segmentation has been one of the most researched topics in the marketing literature. Segmentation has become a central topic to both the theory and practice of marketing, particularly in the recent development of finite mixture models to better identify market segments. This second edition of Market Segmentation updates and extends the integrated examination of segmentation theory and methodology begun in the first edition. A chapter on mixture model analysis of paired comparison data has been added, together with a new chapter on the pros and cons of the mixture model. The book starts with a framework for considering the various bases and methods available for conducting segmentation studies. The second section contains a more detailed discussion of the methodology for market segmentation, from traditional clustering algorithms to more recent developments in finite mixtures and latent class models. Three types of finite mixture models are discussed in this second section: simple mixtures, mixtures of regressions and mixtures of unfolding models. The third main section is devoted to special topics in market segmentation such as joint segmentation, segmentation using tailored interviewing and segmentation with structural equation models. The fourth part covers four major approaches to applied market segmentation: geo-demographic, lifestyle, response-based, and conjoint analysis. The final concluding section discusses directions for further research. |
Contenido
2 | |
Segmentation Methods | 17 |
Tools for Market Segmentation | 31 |
Clustering Methods | 39 |
Market Segmentation Applications of Clustering | 69 |
The EM Algorithm | 81 |
Limitations of the EM Algorithm | 88 |
Some Consequences of Complex Sampling Strategies for the Mixture | 94 |
Dynamic Segmentation | 159 |
SPECIAL TOPICS IN MARKET | 187 |
Market Segmentation with Tailored Interviewing | 195 |
ModelBased Segmentation Using Structural Equation Models | 217 |
Segmentation Based on Product Dissimilarity Judgements 231 | 230 |
PART 4 APPLIED MARKET SEGMENTATION | 239 |
Values and Lifestyles | 259 |
Responsebased Segmentation | 277 |
Examples of the Mixture Regression Approach | 102 |
EM Estimation | 108 |
Mixture Unfolding Models | 125 |
A General Family of Stochastic Mixture Unfolding Models | 131 |
Marketing Applications | 138 |
Profiling Segments | 145 |
Conjoint Analysis 295 | 294 |
CONCLUSIONS AND DIRECTIONS | 322 |
Directions for Future Research | 335 |
References 345 | 361 |
371 | |
Otras ediciones - Ver todas
Market Segmentation: Conceptual and Methodological Foundations Michel Wedel,Wagner A. Kamakura Vista previa limitada - 2000 |
Market Segmentation: Conceptual and Methodological Foundations Michel Wedel,Wagner A. Kamakura Sin vista previa disponible - 2012 |
Términos y frases comunes
algorithm application approach assumed attributes basis behavior binomial Böckenholt brand choice brand preferences chapter classification cluster analysis clustering methods clusterwise regression coefficients concomitant variables conjoint analysis conjoint segmentation consumers criteria demographic dependent variable described developed dimensions discussed distribution EM algorithm example exponential family finite mixture function fuzzy geo-demographic GLIMMIX groups hierarchical homogeneous household identify segments joint segmentation Journal of Marketing Kamakura latent class latent class models lifestyle linear logit model market segmentation marketing mix Marketing Research matrix maximum likelihood measures mixture models mixture regression models multinomial logit number of segments observed obtained optimal part-worths partitioning Poisson post-hoc posterior probabilities predictive problem procedure profiles psychographic purchase Ramaswamy random response sample scale segment membership segmentation bases segmentation methods segmentation model segmentation research selection simultaneously statistical structural equation model STUNMIX model subjects Table tailored interviewing ultrametric unobserved values variance vector Wedel Wedel and DeSarbo