Skip to main content
SpringerLink
Log in

Retail Clients Latent Segments

  • Conference paper
Progress in Artificial Intelligence (EPIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3808))

Included in the following conference series:

Abstract

Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the best criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and \({\mathcal L}\) criteria are selected as the best performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients’ segmentation. The best information criteria yield two segments: Preferential Clients and Occasional Clients.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akaike, H.: Information Theory and an Extension of Maximum Likelihood Principle. In: Emanuel Parzen, K.T., Kitagawa, G. (eds.) Selected Papers of Hirotugu Akaike; In: Petrov, B.N., Caski, F. (eds.) Proceedings of the Second International Symposium on Information Theory. Akademiai Kiado, Budapest, pp. 267–281, p. 434. Springer, Heidelberg (1973)

    Google Scholar 

  2. Akaike, H.: Maximum likelihood identification of Gaussian autorregressive moving average models. Biometrika 60, 255–265 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  3. Banfield, J.D., Raftery, A.E.: Model-Based Gaussian and Non-Gaussian Clustering. Biometrics 49, 803–821 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  4. Biernacki, C.: Choix de modéles en Classification-Ph.D. Tesis (1997)

    Google Scholar 

  5. Biernacki, C., Celeux, G., Govaert, G.: Assessing a Mixture model for Clustering with the integrated Completed Likelihood. IEEE Transactions on Pattern analysis and Machine Intelligence 22, 719–725 (2000)

    Article  Google Scholar 

  6. Biernacki, C., Celeux, G., Govaert, G.: An improvement of the NEC criterion for assessing the number of clusters in mixture model. Pattern Recognition Letters 20, 267–272 (1999)

    Article  MATH  Google Scholar 

  7. Biernacki, C., Govaert, G.: Using the classification likelihood to choose the number of clusters. Computing Science and Statistics 29, 451–457 (1997)

    Google Scholar 

  8. Bozdogan, H.: Mixture-Model Cluster Analysis using Model Selection criteria and a new Informational Measure of Complexity. In: Bozdogan, H. (ed.) Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Approach, pp. 69–113. Kluwer Academic Publishers, Dordrecht (1994)

    Google Scholar 

  9. Bozdogan, H.: Model Selection and Akaikes’s Information Criterion (AIC): The General Theory and its Analytical Extensions. Psycometrika 52, 345–370 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  10. Cardoso, M.G.M.S., Mendes, A.B.: Segmentação de clientes de lojas de Pequena Dimensão, IX Congresso da Sociedade Portuguesa de Estatística, Ponta Delgada, pp. 157–170 (2002)

    Google Scholar 

  11. Celeux, G., Soromenho, G.: An entropy criterion for acessing the number of clusters in a mixture model. Journal of Classification 13, 195–212 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  12. Ching, J.Y., Wong, A.K.C., Chan, K.C.C.: Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 641–651 (1995)

    Article  Google Scholar 

  13. Choi, Y.-S., Moon, B.-R., Seo, S.Y.: Proceedings of the 2005 Conference on Genetic Fuzzy Discretization with Adaptive Intervals for Classification Problems. In: Conference on Genetic and Evolutionary Computation, pp. 2037–2043. ACM Press, New York (2005)

    Google Scholar 

  14. Cohen, S.H., Ramaswamy, V.: Latent segmentation models. Marketing Research Magazine, 15–22 (Summer 1998)

    Google Scholar 

  15. Crawford, S.L., Degroot, M.H., Kadane, J.B.: Modeling Lake-Chemistry Distributions: Approximate Bayesian Methods for Estimating a Finite-Mixture Model. Technometrics 34, 441–453 (1992)

    Article  Google Scholar 

  16. Dellaportas, P.: Bayesian Classification of Neolithic Tools. Appllied Statistics 47, 279–297 (1998)

    MATH  Google Scholar 

  17. Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, K., Sandhu, K., Guppy, K., Lee, S., Froelicher, V.: Rapid searches for complex patterns in biological molecules. American Journal of Cardiology 64, 304–310 (1989)

    Article  Google Scholar 

  18. Dias, J.G.: Finite Mixture Models; Review, Applications, and Computer-intensive Methods, Econimics, Groningen University, PhD Thesis, Groningen, p. 199 (2004)

    Google Scholar 

  19. Dillon, W.R., Kumar, A.: Latent structure and other mixture models in marketing: An integrative survey and overview. In: Bagozi, R.P. (ed.) Advanced methods of Marketing Research, pp. 352–388. Blackwell Publishers, Cambridge (1994)

    Google Scholar 

  20. Efron, B., Gong, G.: A Leisurely at the Bootstrap, the Jackknife, and cross-Validation. The American Statistitian 37, 36–48 (1983)

    Article  MathSciNet  Google Scholar 

  21. Fennell, G., Allenby, G.M., Yang, S., Edwards, Y.: The effectiveness of Demographic and Psychographic Variables for Explaining Brand and Product Category Use. Quantitative Marketing and Economics 1, 233–244 (2003)

    Article  Google Scholar 

  22. Figueiredo, M.A.T., Jain, A.K.: Unsupervised Learning of Finite Mixture Models. IEEE Transactions on pattern analysis and Machine Intelligence 24, 1–16 (2002)

    Article  Google Scholar 

  23. Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E.: Small Data Sets. Chapman & Hall, London (1996)

    Google Scholar 

  24. Hunt, L., Jorgensen, M.: Mixture model clustering for mixed data with missing information. Computational Statistics & Data Analysis 41, 429–440 (2003)

    Article  MathSciNet  Google Scholar 

  25. Hunt, L.A., Basford, K.E.: Fitting a Mixture Model to Three-Mode Trhee-Way Data with Categorical and Continuous Variables. Journal of Classification 16, 283–296 (1999)

    Article  MATH  Google Scholar 

  26. Hurvich, C.M., Tsai, C.-L.: Regression and Time Series Model Selection in Small Samples. Biometrika 76, 297–307 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  27. Kibler, D., Aha, D.W., Albert, M.: Instance-based prediction of real-valued attributes. Computational Intelligence 5 (1989)

    Google Scholar 

  28. Kim, B.-D., Srinivasan, K., Wilcox, R.T.: Identifying Price Sensitive Consumers: The Relative Merits of Demographic vs. Purchase Pattern Information. Journal of Retailing 75, 173–193 (1999)

    Article  Google Scholar 

  29. Leroux, B.G., Puterman, M.L.: Maximum-Penalized-Likelihood Estimation for Independent and Markov-Dependent Mixture Models. Biometrics 48, 545–558 (1992)

    Article  Google Scholar 

  30. McLachlan, G.F., Peel, D.: Finite Mixture Models. John Wiley & Sons, Inc., Chichester (2000)

    Book  MATH  Google Scholar 

  31. McQuarrie, A., Shumway, R., Tsai, C.-L.: The model selection criterion AICu. Statistics & Probability Letters 34, 285–292 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  32. Moustaki, I., Papageorgiou, I.: Latent class models for mixed variables with applications in Archaeometry. Computational Statistics & Data Analysis (2004) (in press)

    Google Scholar 

  33. Price, C.J., Kimmel, C.A., Tyl, R.W., Marr, M.C.: The development toxicity of ethylene glycol in rats and mice. Toxicological Applications in Pharmacology 81, 113–127 (1985)

    Article  Google Scholar 

  34. Redner, R.A., Walker, H.F.: Mixture Densities, Maximum Likelihood and the EM Algorithm. SIAM review 26, 195–239 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  35. Rissanen, J.: Modeling by shortest data description. Automatica 14, 465–471 (1978)

    Article  MATH  Google Scholar 

  36. Schwarz, G.: Estimating the Dimenson of a Model. The Annals of Statistics 6, 461–464 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  37. Shi, J.-Q., Lee, S.-Y.: Latent variable models with mixed continuous and polytomous data. Journal of the Royal Statistical Society, Series A 62 (2000)

    Google Scholar 

  38. Smith, W.R.: Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing 21, 3–8 (1956)

    Article  Google Scholar 

  39. Tsai, C.-Y., Chiu, C.-C.: A purchase-based market segmentation methodology. Expert Systems with Applications 27, 265–276 (2004)

    Article  Google Scholar 

  40. Vermunt, J.K., Magidson, J.: Latent class cluster analysis. In: Hagenaars, J.A., McCutcheon, A.L. (eds.) Applied Latent Class Analysis, pp. 89–106. Cambridge University Press, Cambridge (2002)

    Chapter  Google Scholar 

  41. Vriens, M.: Market Segmentation. Analytical Developments and Applications Guidlines, Technical Overview Series, Millward Brown IntelliQuest, pp. 1–42 (2001)

    Google Scholar 

  42. Wedel, M., Kamakura, W.A.: Market Segmentation: Concepts and methodological foundations. Kluwer Academic Publishers, Boston (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ISCSP-Instituto Superior de Ciências Sociais e Políticas, R. Almerindo Lessa, Pólo Universitário do Alto da Ajuda, 1349-055, Lisboa, Portugal

    Jaime R. S. Fonseca

  2. Department of Quantitative Methods, ISCTE – Business School, Av. das Forças Armadas, 1649-026, Lisboa, Portugal

    Margarida G. M. S. Cardoso

Authors
  1. Jaime R. S. Fonseca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Margarida G. M. S. Cardoso
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Portugal Telecom Inovação (PTI), Centro de Informatica e Sistemas da Universidade de Coimbra (CISUC),  

    Carlos Bento

  2. Department of Informatics Engineering, Coimbra University, Portugal

    Amílcar Cardoso

  3. Centre of Human Language Technology and Bioinformatics, University of Beira Interior, 6201-001, Covilhã, Portugal

    Gaël Dias

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fonseca, J.R.S., Cardoso, M.G.M.S. (2005). Retail Clients Latent Segments. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_35

Download citation

  • DOI: https://doi.org/10.1007/11595014_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30737-2

  • Online ISBN: 978-3-540-31646-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation