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Aspectos Metodológicos da Segmentação de Mercado: Base de Segmentação e Métodos de Classificação

Author

Listed:
  • Ana Oliveira-Brochado

    (EDGE, CESUR, DECIVIL-IST, Universidade Técnica de Lisboa)

  • Francisco Vitorino Martins

    (EDGE, Faculdade de Economia da Universidade do Porto)

Abstract

This work provides a broad review of the past literature on market segmentation, focusing on a discussion of proposed bases and classification methods. Multiple segmentation bases are detached, organized according to two axes - observable/ unobservable, general/ specific of the product and evaluated according to some criteria that must be satisfied for an effective segmentation: identifiability, substantiality, accessibility, stability, actionability and responsiveness Classification methods grouped in three classes - nonoverlapping, overlapping and fuzzy, according to the format of the partition matrix they provide.

Suggested Citation

  • Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Aspectos Metodológicos da Segmentação de Mercado: Base de Segmentação e Métodos de Classificação," FEP Working Papers 261, Universidade do Porto, Faculdade de Economia do Porto.
  • Handle: RePEc:por:fepwps:261
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    File URL: http://www.fep.up.pt/investigacao/workingpapers/08.01.17_wp261.pdf
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    References listed on IDEAS

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    1. Kamakura, Wagner A & Novak, Thomas P, 1992. "Value-System Segmentation: Exploring the Meaning of LOV," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(1), pages 119-132, June.
    2. John R. Hauser & Glen L. Urban, 1977. "A Normative Methodology for Modeling Consumer Response to Innovation," Operations Research, INFORMS, vol. 25(4), pages 579-619, August.
    3. Hauser, John R. & Urban, Glen L., 1975. "A normative methodology for modeling consumer response to innovation," Working papers 785-75., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    4. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    5. P.-L. Dubois & A. Jolibert, 2005. "Le marketing," Post-Print halshs-00095259, HAL.
    6. Wayne DeSarbo & Richard Oliver & Arvind Rangaswamy, 1989. "A simulated annealing methodology for clusterwise linear regression," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 707-736, September.
    7. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    8. Wayne Desarbo, 1982. "Gennclus: New models for general nonhierarchical clustering analysis," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 449-475, December.
    9. Wayne DeSarbo & Vijay Mahajan, 1984. "Constrained classification: The use of a priori information in cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 49(2), pages 187-215, June.
    10. Peter J. Lenk & Wayne S. DeSarbo & Paul E. Green & Martin R. Young, 1996. "Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs," Marketing Science, INFORMS, vol. 15(2), pages 173-191.
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    Cited by:

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    More about this item

    Keywords

    market segmentation; effective segmentation; segmentation bases; classification analysis;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • D0 - Microeconomics - - General

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