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On heterogeneous latent class models with applications to the analysis of rating scores

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  • Bertrand, Aurelie
  • Hafner, Christian

Abstract

Discovering the preferences and the behaviour of consumers is a key challenge in marketing. Information about such topics can be gathered through surveys in which the respondents must assign a score to a number of items. In this article we suggest a strategy to analyze such data and achieve this objective: it consists in identifying groups of consumers whose response patterns are similar and characterizing them in terms of preferences and covariates. We use latent class models allowing for heterogeneity of both latent class and within-class probabilities across individuals. We illustrate the proposed methodology using data about the preferences of Belgian households for supermarkets.
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  • Bertrand, Aurelie & Hafner, Christian, 2011. "On heterogeneous latent class models with applications to the analysis of rating scores," LIDAM Discussion Papers ISBA 2011028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2011028
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    Cited by:

    1. Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    2. Bart Neuts & João Romão & Peter Nijkamp & Asami Shikida, 2016. "Market segmentation and their potential economic impacts in an ecotourism destination," Tourism Economics, , vol. 22(4), pages 793-808, August.
    3. Kumar, Sunil & Husain, Zakir & Mukherjee, Diganta, 2017. "Assessing consistency of consumer confidence data using latent class analysis with time factor," Economic Analysis and Policy, Elsevier, vol. 55(C), pages 35-46.

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    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
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