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Investigating brand preferences across social groups and consumption contexts

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  • Minki Kim
  • Pradeep Chintagunta

Abstract

Using a unique dataset on U.S. beer consumption, we investigate brand preferences of consumers across various social group and context related consumption scenarios (“scenarios”). As sufficient data are not available for each scenario, understanding these preferences requires us to share information across scenarios. Our proposed modeling framework has two main building blocks. The first is a standard continuous random coefficients logit model that the framework reduces to in the absence of information on social groups and consumption contexts. The second component captures variations in mean preferences across scenarios in a parsimonious fashion by decomposing the deviations in preferences from a base scenario into a low dimensional brand map in which the brand locations are fixed across scenarios but the importance weights vary by scenario. In addition to heterogeneity in brand preferences that is reflected in the random coefficients, heterogeneity in preferences across scenarios is accounted for by allowing the brand map itself to have a discrete heterogeneity distribution across consumers. Finally, heterogeneity in preferences within a scenario is accounted for by allowing the importance weights to vary across consumers. Together, these factors allow us to parsimoniously account for preference heterogeneity across brands, consumers and scenarios. We conduct a simulation study to reassure ourselves that using the kind of data that is available to us, our proposed estimator can recover the true model parameters from those data. We find that brand preferences vary considerably across the different social groups and consumption contexts as well as across different consumer segments. Despite the sparse data on specific brand-scenario combinations, our approach facilitates such an analysis and assessment of the relative strengths of brands in each of these scenarios. This could provide useful guidance to the brand managers of the smaller brands whose overall preference level might be low but which enjoy a customer franchise in a particular segment or in a particular context or a social group setting. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Minki Kim & Pradeep Chintagunta, 2012. "Investigating brand preferences across social groups and consumption contexts," Quantitative Marketing and Economics (QME), Springer, vol. 10(3), pages 305-333, September.
  • Handle: RePEc:kap:qmktec:v:10:y:2012:i:3:p:305-333
    DOI: 10.1007/s11129-011-9117-0
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Junghyun Park & Minki Kim & Pradeep K Chintagunta, 2022. "Mapping Consumers’ Context-Dependent Consumption Preferences: A Multidimensional Unfolding Approach [An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations o," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 49(2), pages 202-228.
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    8. Sinha, Priyank & Sainy, Romi, 2021. "How can Indian small-scale fashion retailers survive COVID-19 disruption?-A Brand Portfolio Optimization Perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Heterogeneous brand preferences; Social group and consumption context; Random coefficients logit model; C51; D12; L66; M3;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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