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.
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Notes
In other words, the preference distribution from the random coefficients logit model is also the distribution of preferences for the base scenario.
Allowing for the full set of random coefficients across all S − 1 remaining scenarios would require estimating (J − 1)×(S − 1) additional mean preference parameters and (S − 1)×J×(J − 1)/2 additional covariance parameters for the heterogeneity distribution. Note that with J brands there are only J − 1 parameters since one of the mean preference parameters is normalized to 0.
We divide the age by 100 and take the demeaned value of age in estimation.
Sweeps months are November, February, May and July in which Nielsen collects viewing information based on seven-day diaries filled out by sample households in many television markets in the United States.
There are several identification issues associated with these types of models. We return to these issues later.
We run the first stage regression using the instrumental variables suggested in the data section. After obtaining the predicted marketing variables and accordingly the residuals, we use them to create control functions for the individual level data.
We thank an anonymous reviewer for pointing out this alternative specification.
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Acknowledgements
We thank Karen Garvin and David Miller of Research International and Rafael Alcaraz (now at Hersheys) and Gary Fehlhaber at MillerCoors for the data. The authors also thank Sha Yang for her feedback, and the Editor and 2 anonymous QME reviewers for their extensive comments.
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Kim, M., Chintagunta, P.K. Investigating brand preferences across social groups and consumption contexts. Quant Mark Econ 10, 305–333 (2012). https://doi.org/10.1007/s11129-011-9117-0
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DOI: https://doi.org/10.1007/s11129-011-9117-0