Finite mixture models in market segmentation: A review and suggestions for best practices

Electronic Journal of Business Research Methods

Volumn 11, Issue 1, 2013, Pages 2-15

  Tuma, Michael ^a   Decker, Reinhold ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Finite mixture models; Latent class models; Market segmentation; Model based clustering

Indexed keywords

EID: 84880019736     PISSN: None     EISSN: 14777029     Source Type: Journal    
DOI: None     Document Type: Article

References (53)

1. Akaike, H. (1974) 'A new look at statistical model identification'. IEEE Transactions on Automatic Control, AC-19: 716-723.
2. Andrews, R. L., Ainslie, A. and Currim, I. S. (2002) 'An empirical comparison of logit choice models with discrete vs. continuous representations of heterogeneity'. Journal of Marketing Research, 39: 479-487.
3. Andrews, R. L., Brusco, M. J. and Currim, I. S. (2010) 'Amalgamation of partitions from multiple segmentation bases: A comparison of non-model-based and model-based methods'. European Journal of Operational Research, 201: 608- 618.
4. Andrews, R. L., Brusco, M. J., Currim, I. S., and Davis, B. (2010) 'An empirical comparison of methods for clustering problems: Are there benefits from having a statistical model?', Review of Marketing Science, 8: Article 3.
5. Andrews, R. L. and Currim, I. S. (2003a) 'A comparison of segment retention criteria for finite mixture logit models'. Journal of Marketing Research, 40 (2): 235-243.
6. Andrews, R. L. and Currim, I. S. (2003b) 'Retention of latent segments in regression-based marketing models'. International Journal of Research in Marketing, 20 (4): 315-321.
7. Andrews, R. L., Currim, I. S., and Leeflang, P. (2011) 'A comparison of sales response predictions from demand models applied to store-level versus panel-level data'. Journal of Business and Economic Statistics, 29 (2): 319-326.
8. Atella, V., Brindisi, F., Deb, P. and Rosati, F. C. (2004) 'Determinants of access to physician services in Italy: A latent class seemingly unrelated probit approach'. Health Economics, 13: 657-668.
9. Bekker, P., Merckens, A., and Wansbeek, T. J. (1994) Identification, equivalent models and computer algebra, New York: Academic Press.
10. Blashfield, R. K. (1976) 'Mixture model tests of cluster analysis: Accuracy of four agglomerative hierarchical methods', Psychological Bulletin, 83: 377-388.
11. Bozdogan, H. (1987) 'Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions'. Psychometrika, 52 (3): 345-370.
12. Bozdogan, H. (1994) 'Mixture model cluster analysis using selection criteria and a new informational measure of complexity, in Bozdogan, H. (Ed.) Multivariate Statistical Modelling, Dordrecht: Kluwer Academic Publishers, 2: 69- 113.
13. Brun, M., Sima, C., Hua, J., Lowey, J., Carroll, B., Suh, E. and Dougherty, E. R. (2007) 'Model-based evaluation of clustering validation measures'. Pattern Recognition, 40: 807-824.
14. Celeux G. (1998) 'Bayesian inference for mixture: The label switching problem' in Payne R. and Green P. (Eds.), COMPSTAT 98, Heidelberg: Physica, 227-232.
15. Celeux, G. and Soromenho, G. (1996) 'An entropy criterion for assessing the number of clusters in a mixture model'. Journal of Classification, 13: 195-212.
16. Celeux, G., Hurn, M., and Robert, C. P. (2000) 'Computational and inferential difficulties with mixture posterior distributions'. Journal of the American Statistical Association, 95: 957-970.
17. Decker, R. and Trusov, M. (2010) 'Estimating aggregate consumer preferences from online product reviews'. International Journal of Research in Marketing, 27 (4): 293-307.
18. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977) 'Maximum likelihood from incomplete data via the EM algorithm'. Journal of the Royal Statistical Society Series B (Methodological), 39 (1): 1-38.
19. Dias, J. (2006) 'Model selection for the binary latent class model: A Monte Carlo simulation' in Batagelj, V., Bock, H.-H., Ferligoj, A., Žiberna, A. (Eds.), Data Science and Classification, Berlin: Springer, 91-99.
20. Dias, J. and Wedel, M. (2004) 'An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods'. Statistics and Computing, 14: 323-332.
21. Dy, J. G. and Brodley, C. E. (2004) 'Feature selection for unsupervised learning'. Journal of Machine Learning Research, 5: 845-889.
22. Ebbes, P., Grewal, R. and DeSarbo, W. S. (2010) 'Modeling strategic group dynamics: A hidden Markov approach'. Quantive Marketing and Economics, 8: 241-274.
23. Formann, A. K. (1984) Die Latent-Class-Analysis: Eine Einführung in die Theorie und Anwendung, Weinheim: Beltz.
24. Frühwirth-Schnatter, S. (2006) Finite Mixture and Markov Switching Models, New York: Springer.
25. Garver, M. S., Williams, Z. and Taylor, G. S. (2008) 'Employing latent class regression analysis to examine logistics theory: An application to truck driver retention'. Journal of Business Logistics, 29 (2): 233- 257.
26. Hahn, C., Johnson, M. D., Hermann, A. and Huber, F. (2002) 'Capturing consumer heterogeneity using a finite mixture PLS approach'. Schmalenbach Business Review, 54 (3): 243-269.
27. Hair, J., Ralph. F., Anderson, E., Tatham, R. L. and William C. Black (1998) Multivariate Data Analysis, Englewood Cliffs: Prentice Hall.
28. Haughton, D., Legrand, P., and Wolford, S. (2009) 'Review of three latent class cluster analysis packages: Latent GOLD, poLCA, MCLUST'. The American Statistician, 63 (1): 81-91.
29. Harzing, A.-W. (2011) Journal Quality List, University of Melbourne, available at: www.harzing.com
30. Henson, J. M., Reise, S. P., Kim, K. H. (2007) 'Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics'. Structural Equation Modeling, 14 (2): 202-226.
31. Hofstede, F. T., Wedel, M., and Steenkamp, J.-B. E.M. (2002) 'Identifying spatial segments in international markets'. Marketing Science, 21 (2): 160-177.
32. Jedidi, K., Jagpal, H. S. and DeSarbo, W. S. (1997) 'Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity'. Marketing Science, 16 (1): 39-59.
33. Law, M. H. C., Figueiredo, M. A. T. and Jain, A. K (2004) 'Simultaneous feature selection and clustering using mixture models'. IEEE Transactions on Pattern Analysis and Machine Learning, 26: 1154-1166.
34. Lubke, G. H. and Muthén, B. (2005) 'Investigating population heterogeneity with factor mixture models'. Psychological Methods, 10 (1): 21-39.
35. Lukočiene, O., Varriale, R., and Vermunt, J. K. (2010) 'The simultaneous decision(s) about the number of lower- and higherlevel classes in multilevel latent class analysis'. Sociological Methodology, 40 (1): 247-283.
36. Lukočiene, O. and Vermunt, J. K. (2010) 'Determining the number of components in mixture models for hierarchical data' in Fink, A., Lausen, B., Seidel, W., Ultsch, A. (Eds.), Advances in Data Analysis, Data Handling and Business Intelligence, Berlin: Springer, 241-249.
37. McLachlan, G. and Peel, D. (2000) Finite Mixture Models. New York: Wiley.
38. Melnykov, V. and Maitra, R. (2010) 'Finite mixture models and model-based clustering'. Statistic Surveys, 4: 80-116.
39. Netzer, O., Lattin, J. M. and Srinivasan, V. (2008) 'A hidden Markov model of customer relationship dynamics'. Marketing Science, 27 (2): 185-204.
40. Raftery, A. E. and Dean, N. (2006) 'Variable selection for model-based clustering'. Journal of the American Statistical Association, 101: 294-304.
41. Rust, R. T. and Verhoef, P. C. (2005) 'Optimizing the marketing interventions mix in intermediate-term CRM'. Marketing Science, 24 (3): 477-489.
42. Sarstedt, M. (2008) 'Market segmentation with mixture regression models: Understanding measures that guide model selection'. Journal of Targeting, Measurement and Analysis for Marketing, 16: 228-246.
43. Sarstedt, M., Becker, J.-M, Ringle, C. M. and 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 (1): 34-62.
44. Sarstedt, M. and Schwaiger, M. (2007) 'Model selection in mixture regeression analysis - A Monte Carlo simulation study', in Preisach, C., Burkhardt, H., Schmidt-Thieme, L. and Decker, R. (Eds), Data Analysis, Machine Learning, and Applications, Berlin: Springer, 61-68.
45. Schwarz, G. (1978) 'Estimating the dimension of a model'. The Annals of Statistics, 6 (2): 461-464.
46. Sclove, S. L. (1987) 'Application of model-selection criteria to some problems in multivariate-analysis'. Psychometrika, 52: 333-43.
47. Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and van der Linde, A. (2002) 'Bayesian measures of model complexity and fit'. Journal of the Royal Statistical Society, Series B, 64, 583-640.
48. Steinley, D., and Brusco, M. J. (2008a) 'A new variable weighting and selection procedure for k-means cluster analysis'. Multivariate Behavioral Research, 43: 77-108.
49. Steinley, D., and Brusco, M. J. (2008b) 'Selection of variables in cluster analysis: An empirical comparison of eight procedures'. Psychometrika, 73: 125-144.
50. Stephens, M. (1997) Bayesian Methods for Mixtures of Normal Distributions, Dissertation, University of Oxford.
51. Tuma, M. N., Decker, R. and Scholz, S. W. (2011) 'A survey of the challenges and pitfalls of cluster analysis application in market segmentation'. International Journal of Market Research, 53 (3): 391-414.
52. Wedel, M. and DeSarbo, W. S. (2002) 'Market segment derivation and profiling va a finite mixture model framework'. Marketing Letters, 13 (1): 17-25.
53. Wedel, M. and Kamakura, W. A. (2000) Market Segmentation: Conceptual and Methodological Foundations, Dordrecht: Kluwer.