Abstract
Finite Mixture models are a state-of-the-art technique of segmentation. Next to segmenting consumers or objects based on multiple different variables, Finite Mixture models can be used in conjunction with multivariate methods of analysis. Unlike approaches combining multivariate methods of analysis and cluster analysis, which require a two-step approach, the parameters are then directly estimated at the segment level. This also allows for inferential statistical analysis. This book chapter explains the basic idea of Finite Mixture models and describes some popular applications of Finite Mixture models in market research.
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References
Ailawadi, K. L., Gedenk, K., Langer, T., Ma, Y., & Neslin, S. A. (2014). Consumer response to uncertain promotions: An empirical analysis of conditional rebates. International Journal of Research in Marketing, 31(1), 94–106.
Andrews, R., & Currim, I. (2003a). A comparison of segment retention criteria for finite mixture logit models. Journal of Marketing Research, 40(2), 235–243.
Andrews, R., & Currim, I. (2003b). Retention of latent segments in regression-based marketing models. International Journal of Research in Marketing, 20(4), 315–321.
Bozdogan, H. (1987). Model selection and Akaike’s Information Criterion (AIC). The general theory and its analytical extensions. Psychometrika, 52(3), 345–370.
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293–307.
DeSarbo, W., Howard, D., & Jedidi, K. (1991). MULTICLUS: A new method for simultaneously performing multidimensional scaling and cluster analysis. Psychometrika, 56(1), 121–136.
DeSarbo, W., Wedel, M., Vriens, M., & Ramaswamy, V. (1992). Latent class metric conjoint analysis. Marketing Letters, 3(3), 273–288.
DeSarbo, W., Manrai, A., & Manrai, L. (1994). Latent class multidimensional scaling. A review of recent developments in the marketing and psychometric literature. In R. P. Bagozzi (Ed.), Advanced methods of marketing research (pp. 190–222). Cambridge, MA: Blackwell Publishers.
DeSarbo, W., Ramaswamy, V., & Cohen, S. (1995). Market segmentation with choice-based conjoint analysis. Marketing Letters, 6(2), 137–147.
DeSarbo, W. S., & Wu, J. (2001). The joint spatial representation of multiple variable batteries collected in marketing research. Journal of Marketing Research, 38(2), 244–253.
DeSarbo, W., Di Benedetto, C., Jedidi, K., & Song, M. (2006). Identifying sources of heterogeneity for empirically deriving strategic types: A constrained finite-mixture structural-equation methodology. Management Science, 52(6), 909–924.
Dillon, W. R., & Kumar, A. (1994). Latent structure and other mixture models in marketing: An integrative survey and overview. In R. P. Bagozzi (Ed.), Advanced methods for marketing research (pp. 295–351). Cambridge, MA: Blackwell Publishers.
Green, P., & Krieger, A. (1991). Segmenting markets with conjoint analysis. Journal of Marketing, 55(4), 20–31.
Green, P., Carmone, F., & Wachspress, D. (1976). Consumer segmentation via latent class analysis. Journal of Consumer Research, 3(3), 170–174.
Haapanen, L., Juntunen, M., & Juntunen, J. (2016). Firms’ capability portfolios throughout international expansion: A latent class approach. Journal of Business Research, 69(12), 5578–5586.
Jedidi, K., Jagpal, H., & DeSarbo, W. (1997). Finite mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1), 39–59.
Kamakura, W. A., Wedel, M., & Agrawal, J. (1994). Concomitant variable latent class models for conjoint analysis. International Journal of Research in Marketing, 11(5), 451–464.
Kotler, P. T., & Keller, K. L. (2012). Marketing Management. Pearson.
Lenk, P., & DeSarbo, W. (2000). Bayesian inference for finite mixtures of generalized linear models with random effects. Psychometrika, 65(1), 93–119.
Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20, 36–43.
McLachlan, G., & Peel, D. (2001). Finite mixture models. New York: Wiley.
Natter, M., & Feurstein, M. (2002). Real world performance of choice-based conjoint models. European Journal of Operational Research, 137(2), 448–458.
Natter, M., Mild, A., Wagner, U., & Taudes, A. (2008). Planning new tariffs at tele.ring: The application and impact of an integrated segmentation, targeting, and positioning tool. Marketing Science, 27(4), 600–609.
Papies, D., Eggers, F., & Wlömert, N. (2011). Music for free? How free ad-funded downloads affect consumer choice. Journal of the Academy of Marketing Science, 39(5), 777–794.
Petersen, A., & Kumar, V. (2015). Perceived risk, product returns, and optimal resource allocation: Evidence from a field experiment. Journal of Marketing Research, 52(2), 268–285.
Ramaswamy, V., DeSarbo, W., Reibstein, D., & Robinson, W. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1), 103–124.
Sarstedt, M., & Ringle, C. M. (2010). Treating unobserved heterogeneity in PLS path modeling: A comparison of FIMIX-PLS with different data analysis strategies. Journal of Applied Statistics, 37(8), 1299–1318.
Sarstedt, M., Becker, J.-M., Ringle, C., & Schwaiger, M. (2011). Uncovering and treating unobserved heterogeneity with FIMIX-PLS: Which model selection criterion provides an appropriate number of segments? Schmalenbach Business Review, 63, 34–62.
Srinivasan, R. (2006). Dual distribution and intangible firm value: Franchising in restaurant chains. Journal of Marketing, 70(3), 120–135.
Steiner, M., Wiegand, N., Eggert, A., & Backhaus, K. (2016). Platform adoption in system markets: The roles of preference heterogeneity and consumer expectations. International Journal of Research in Marketing, 33(2), 276–296.
Wedel, M., & DeSarbo, W. (1994). A review of recent developments in latent class regression models. In R. P. Bagozzi (Ed.), Advanced methods of marketing research (pp. 352–388). Cambridge, MA: Blackwell Publishers.
Wedel, M., & DeSarbo, W. (1996). An exponential-family multidimensional scaling mixture methodology. Journal of Business & Economic Statistics, 14(4), 447–459.
Wedel, M., & Kamakura, W. (2000). Market segmentation. Conceptual and methodological foundations. Norwell: Kluwer.
Wilden, R., & Gudergan, S. (2015). The impact of dynamic capabilities on operational marketing and technological capabilities: Investigating the role of environmental turbulence. Journal of the Academy of Marketing Science, 43(2), 181–199.
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Gensler, S. (2022). Finite Mixture Models. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_12
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DOI: https://doi.org/10.1007/978-3-319-57413-4_12
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