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Published in: Quantitative Marketing and Economics 3/2012

01-09-2012

Investigating brand preferences across social groups and consumption contexts

Authors: Minki Kim, Pradeep K. Chintagunta

Published in: Quantitative Marketing and Economics | Issue 3/2012

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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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Footnotes
1
In other words, the preference distribution from the random coefficients logit model is also the distribution of preferences for the base scenario.
 
2
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.
 
3
We divide the age by 100 and take the demeaned value of age in estimation.
 
4
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.
 
5
There are several identification issues associated with these types of models. We return to these issues later.
 
6
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.
 
7
We thank an anonymous reviewer for pointing out this alternative specification.
 
Literature
go back to reference Allenby, G. M. (1990). Hypothesis testing with scanner data: The advantage of bayesian methods. Journal of Marketing Research, 27, 379–389.CrossRef Allenby, G. M. (1990). Hypothesis testing with scanner data: The advantage of bayesian methods. Journal of Marketing Research, 27, 379–389.CrossRef
go back to reference Allenby, G. M., & Rossi, P. E. (1999). Marketing models of consumer heterogeneity. Journal of Marketing Research, 30(2), 171–182. Allenby, G. M., & Rossi, P. E. (1999). Marketing models of consumer heterogeneity. Journal of Marketing Research, 30(2), 171–182.
go back to reference Allenby, G. M., & Rossi, P. E. (2009). Bayesian Applications in Marketing. Working Paper, University of Chicago, Booth School of Business. Allenby, G. M., & Rossi, P. E. (2009). Bayesian Applications in Marketing. Working Paper, University of Chicago, Booth School of Business.
go back to reference Belk, R. W. (1974). An exploratory assessment of situational effects in buyer behavior. Journal of Marketing Research, 11, 156–163.CrossRef Belk, R. W. (1974). An exploratory assessment of situational effects in buyer behavior. Journal of Marketing Research, 11, 156–163.CrossRef
go back to reference Belk, R. W. (1975). Situational variables and consumer behavior. Journal of Consumer Research, 2(3), 157–164.CrossRef Belk, R. W. (1975). Situational variables and consumer behavior. Journal of Consumer Research, 2(3), 157–164.CrossRef
go back to reference Bettman, J. R., Luce, M. F., & Payne, J. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187–217.CrossRef Bettman, J. R., Luce, M. F., & Payne, J. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187–217.CrossRef
go back to reference Carlson, K. C., & Bond, S. D. (2006). Improving preference assessment: Limiting the effects of context through pre-exposure to attribute levels. Management Science, 52(3), 410–421.CrossRef Carlson, K. C., & Bond, S. D. (2006). Improving preference assessment: Limiting the effects of context through pre-exposure to attribute levels. Management Science, 52(3), 410–421.CrossRef
go back to reference Chintagunta, P. K. (1994). Heterogeneous logit model implications for brand positioning. Journal of Marketing Research, 31, 304–312.CrossRef Chintagunta, P. K. (1994). Heterogeneous logit model implications for brand positioning. Journal of Marketing Research, 31, 304–312.CrossRef
go back to reference DeSarbo, W. S., Atalay, A. S., Lebaron, D., & Blanchard S. J. (2008). Estimating multiple consumer segment ideal points from context-dependent survey data. Journal of Consumer Research, 35, 142–153.CrossRef DeSarbo, W. S., Atalay, A. S., Lebaron, D., & Blanchard S. J. (2008). Estimating multiple consumer segment ideal points from context-dependent survey data. Journal of Consumer Research, 35, 142–153.CrossRef
go back to reference Dube, J. P., Hitsch, G., & Rossi, P. E. (2009). State Dependence and Alternative Explanations for Consumer Inertia. Working Paper, University of Chicago, Booth School of Business. Dube, J. P., Hitsch, G., & Rossi, P. E. (2009). State Dependence and Alternative Explanations for Consumer Inertia. Working Paper, University of Chicago, Booth School of Business.
go back to reference Dickson, P. R. (1982). Person-situation: Segmentation’s missing link. Journal of Marketing, 46, 56–64.CrossRef Dickson, P. R. (1982). Person-situation: Segmentation’s missing link. Journal of Marketing, 46, 56–64.CrossRef
go back to reference Elrod, T. (1988). Choice map: Inferring a product-market map from panel data. Marketing Science, 7, 21–40.CrossRef Elrod, T. (1988). Choice map: Inferring a product-market map from panel data. Marketing Science, 7, 21–40.CrossRef
go back to reference Elrod, T., & Keane, M. (1995). A factor-analytic probit model for representing market structure in panel data. Journal of Marketing Research, 32, 1–16.CrossRef Elrod, T., & Keane, M. (1995). A factor-analytic probit model for representing market structure in panel data. Journal of Marketing Research, 32, 1–16.CrossRef
go back to reference Fennell, G. (1978). Consumer’s perception of the product use situation. The Journal of Marketing, 42, 38–47.CrossRef Fennell, G. (1978). Consumer’s perception of the product use situation. The Journal of Marketing, 42, 38–47.CrossRef
go back to reference Guadagni, P. M., & Little, J. D. C. (1983). A logit model of brand choice calibrated on scanner data. Marketing Science, 2, 203–238.CrossRef Guadagni, P. M., & Little, J. D. C. (1983). A logit model of brand choice calibrated on scanner data. Marketing Science, 2, 203–238.CrossRef
go back to reference Hausman, J. (1996). Valuation of new goods under perfect and imperfect competition. In T. Bresnahan, R. Gordon (Ed.), The Economics of New Goods, Studies in Income and Wealth (Vol. 58). Chicago: National Bureau of Economic Research. Hausman, J. (1996). Valuation of new goods under perfect and imperfect competition. In T. Bresnahan, R. Gordon (Ed.), The Economics of New Goods, Studies in Income and Wealth (Vol. 58). Chicago: National Bureau of Economic Research.
go back to reference Lee, J. K. H., Sudhir, K., & Steckel, J. H. (2002). A Multiple ideal point model: Capturing multiple preference effects from within an ideal point framework. Journal of Marketing Research, 39, 73–86.CrossRef Lee, J. K. H., Sudhir, K., & Steckel, J. H. (2002). A Multiple ideal point model: Capturing multiple preference effects from within an ideal point framework. Journal of Marketing Research, 39, 73–86.CrossRef
go back to reference McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior. In P. Zarenmbka (Ed.), Frontiers of Econometrics. New York: Academic Press. McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior. In P. Zarenmbka (Ed.), Frontiers of Econometrics. New York: Academic Press.
go back to reference Nevo, A. (2001). Measuring market power in the ready-to-eat cereal industry. Econometrica, 69(2), 307–342.CrossRef Nevo, A. (2001). Measuring market power in the ready-to-eat cereal industry. Econometrica, 69(2), 307–342.CrossRef
go back to reference Petrin, A., & Train, K. (2006). Control Function Corrections for Omitted Attributes in Differentiated Product Models. Working Paper, University of Minnesota, Twin Cities. Petrin, A., & Train, K. (2006). Control Function Corrections for Omitted Attributes in Differentiated Product Models. Working Paper, University of Minnesota, Twin Cities.
go back to reference Petrin, A., & Train, K. (2010). A control function approach to endogeneity in consumer choice models. Journal of Marketing Research, 47(1), 3–13.CrossRef Petrin, A., & Train, K. (2010). A control function approach to endogeneity in consumer choice models. Journal of Marketing Research, 47(1), 3–13.CrossRef
go back to reference Ratneshwar, S., & Shocker, A. (1991). Substitution in use and the role of usage context in product. Journal of Marketing Research, 28, 281–295.CrossRef Ratneshwar, S., & Shocker, A. (1991). Substitution in use and the role of usage context in product. Journal of Marketing Research, 28, 281–295.CrossRef
go back to reference Yang, S., Allenby, G. M. & Fennell, G. (2002). Modeling variation in brand preference: The roles of objective environment and motivating conditions. Marketing Science, 21(1), Winter, 14–31.CrossRef Yang, S., Allenby, G. M. & Fennell, G. (2002). Modeling variation in brand preference: The roles of objective environment and motivating conditions. Marketing Science, 21(1), Winter, 14–31.CrossRef
Metadata
Title
Investigating brand preferences across social groups and consumption contexts
Authors
Minki Kim
Pradeep K. Chintagunta
Publication date
01-09-2012
Publisher
Springer US
Published in
Quantitative Marketing and Economics / Issue 3/2012
Print ISSN: 1570-7156
Electronic ISSN: 1573-711X
DOI
https://doi.org/10.1007/s11129-011-9117-0