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Determining the Number of Market Segments Using an Experimental Design

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  • Ana Oliveira-Brochado

    ()
    (EDGE, CESUR, DECIVIL-IST, Universidade T├ęcnica de Lisboa)

  • Francisco Vitorino Martins

    ()
    (EDGE, Faculdade de Economia da Universidade do Porto)

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    Abstract

    The aim of this work is to determine how well criteria designed to help the selection of the adequate number of mixture components perform in mixture regressions of normal data. We address this research question based on results of an extensive experimental design. The simulation experiment compares several criteria (26), including information criteria and classification-based criteria. In this full factorial design we manipulate 9 factors and 22 levels, namely: true number of segments (2 or 3), mean separation between segments (low, medium or high), number of consumers (100 or 300), number of observations per consumer (5 or 10), number of predictors (2, 6 or 10), measurement level of predictors (binary, metric or mixed), error variance (20% or 60%), minimum segment size (5-10%, 10-20% or 20-30%) and error distribution (normal versus uniform). The performance of the segment retention criteria is evaluated by their success rates; we also investigate the influence of experimental factors and their levels on success rates. The best results were obtained for the criteria AIC3, AIC4, HQ, ICLBIC and ICOMPLBIC. BIC and CAIC also perform well with large samples and a large number of market segments.

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    File URL: http://www.fep.up.pt/investigacao/workingpapers/08.01.17_wp263.pdf
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    Bibliographic Info

    Paper provided by Universidade do Porto, Faculdade de Economia do Porto in its series FEP Working Papers with number 263.

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    Length: 16 pages
    Date of creation: Jan 2008
    Date of revision:
    Handle: RePEc:por:fepwps:263

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    Related research

    Keywords: Market segmentation; information criteria; classification criteria; experimental design; simulation;

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    1. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer, vol. 13(2), pages 195-212, September.
    2. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer, vol. 12(1), pages 21-55, March.
    3. Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
    4. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer, vol. 5(2), pages 249-282, September.
    5. Hawkins, Dollena S. & Allen, David M. & Stromberg, Arnold J., 2001. "Determining the number of components in mixtures of linear models," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 15-48, November.
    6. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer, vol. 52(3), pages 345-370, September.
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