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Incorporating Demographic Variables in Brand Choice Models: An Indivisible Alternatives Framework

Author

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  • Kirthi Kalyanam

    (Department of Marketing, Leavey School of Business, 121 St. Joseph's Hall, Santa Clara University, Santa Clara, California 95053)

  • Daniel S. Putler

    (Faculty of Commerce and Business Administration, University of British Columbia, 2053 Main Mall, Vancouver, British Columbia, Canada V6T 1Z2)

Abstract

Incorporating demographic variables in brand choice models is conceptually appealing and has numerous managerial benefits. Retailers and brand managers can assess geodemographic variations in demand and marketing mix response in order to implement micromarketing strategies. For example, a retailer planning to locate a new outlet can get some sense of the differences in demand patterns and price and promotion sensitivities in the new trading area in order to make initial stocking, inventory, pricing, and promotion decisions. For existing outlets, retailers can fine tune the assortment and merchandising activities in a category to match local market conditions. Similarly, a packaged goods manufacturers would benefit from getting a sense of whether they should stress promotional activity for one part of a brand's product line in some retail trading areas, and other parts of the brand's line in other retail trading areas. Unfortunately, a general finding across existing studies is that the impact of demographic variables on brand choice is neither strong nor consistent. These findings are puzzling given that one would expect certain demographic variables, such as income, to have some influence on brand choice behavior. Moreover, Hoch et al. (Hoch, S. J., B. D. Kim, A. J. Montgomery, P. E. Rossi. 1995. Determinants of store-level price elasticity. (February) 17–29.), using store level data, find that a relatively small set of demographic variables is much more influential than competitive variables in explaining differences in price sensitivity across retail trading areas. In light of such contradictory findings, there is a need for a better conceptualization of the role of income and other demographic variables in household purchasing, and this is the primary objective of this study. This paper presents a microeconomic-based framework called the “indivisible alternatives” (IDA) framework to model household brand choice. A key aspect of the IDA framework is that it explicitly models alternatives in the market as being indivisible, while past work has either explicitly or implicitly assumed perfect divisibility. Indivisibilities force a household's selection of a brand size and its level of category expenditure into a single joint decision. Hence, demographic variables that influence category expenditure (e.g., income) also influence the choice of a brand size. In addition, since alternatives in the category come in several discrete sizes, indivisibilities introduce differences in holding costs in the choice of a brand-size combination. Consequently, demographic factors that influence holding costs through consumption rate differences (e.g., household size) impact the choice of a brand size in a product category. In this manner, indivisibilities in market offerings naturally lead to differences in choice behavior across households that can be linked to demographic factors in a logical fashion. In empirical applications to two different scanner panel data sets (ketchup and ground coffee), the proposed framework compares favorably with two benchmark specifications in terms of both goodness of fit and predictive validity. The results from these product categories indicate that a household's price sensitivity is inversely related to its income level, and that factors such as household size and seasonality, which are likely to influence consumption rates, make households more or less willing to buy larger package sizes. Income elasticity estimates from the model confirm that, ceteris paribus, households with lower incomes will have a higher propensity to purchase private labels and generic brands, and a lower propensity to purchase national brands, compared with households with higher incomes. The micromarketing potential of the IDA framework is explored in market simulations conducted for two different zip codes within a metropolitan area that vary with respect to income levels and other demographic factors. The results indicate that for the product categories studied, there are substantial differences across market areas in their response to the retail promotion of very large and very small package sizes within the same brand's product line. The differences in response suggest that it may be beneficial to customize promotional programs to market areas based on their underlying demographic composition. Finally, our findings suggest a link between household income and deal proneness that is conditional on other demographic variables and the expenditure required for the brand size in question. Specifically, we find that smaller households with lower income levels are more likely to respond to promotions for smaller, less expensive items within a category, while larger households with higher incomes are more likely to respond to promotions for larger, more expensive items within a category.

Suggested Citation

  • Kirthi Kalyanam & Daniel S. Putler, 1997. "Incorporating Demographic Variables in Brand Choice Models: An Indivisible Alternatives Framework," Marketing Science, INFORMS, vol. 16(2), pages 166-181.
  • Handle: RePEc:inm:ormksc:v:16:y:1997:i:2:p:166-181
    DOI: 10.1287/mksc.16.2.166
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    Cited by:

    1. Lansley, Guy & Longley, Paul, 2016. "Deriving age and gender from forenames for consumer analytics," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 271-278.
    2. Harald J. van Heerde & Peter S. H. Leeflang & Dick R. Wittink, 2004. "Decomposing the Sales Promotion Bump with Store Data," Marketing Science, INFORMS, vol. 23(3), pages 317-334, December.
    3. Zhang Qin & Seetharaman P.B. & Narasimhan Chakravarthi, 2005. "Modeling Selectivity in Households' Purchase Quantity Outcomes: A Count Data Approach," Review of Marketing Science, De Gruyter, vol. 3(1), pages 1-21, July.
    4. Tsarenko, Yelena & Strizhakova, Yuliya, 2015. "“What does a woman want?†The moderating effect of age in female consumption," Journal of Retailing and Consumer Services, Elsevier, vol. 26(C), pages 41-46.
    5. Yanhong H. Jin & David Zilberman & Amir Heiman, 2008. "Choosing Brands: Fresh Produce versus Other Products," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(2), pages 463-475.
    6. Karsten Hansen & Vishal Singh & Pradeep Chintagunta, 2006. "Understanding Store-Brand Purchase Behavior Across Categories," Marketing Science, INFORMS, vol. 25(1), pages 75-90, 01-02.
    7. Zhang, Qin & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "The Indirect Impact of Price Deals on Households’ Purchase Decisions Through the Formation of Expected Future Prices," Journal of Retailing, Elsevier, vol. 88(1), pages 88-101.
    8. Banerjee, Arindam & Awasthy Dheeraj & Gupta V, 2003. "A Choice Modeling Approach to Evaluate Effectiveness of Brand Development Initiatives," IIMA Working Papers WP2003-01-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    9. Kirthi Kalyanam & Sharad Borle & Peter Boatwright, 2007. "Deconstructing Each Item's Category Contribution," Marketing Science, INFORMS, vol. 26(3), pages 327-341, 05-06.
    10. Geraldine Fennell & Greg Allenby & Sha Yang & Yancy Edwards, 2003. "The Effectiveness of Demographic and Psychographic Variables for Explaining Brand and Product Category Use," Quantitative Marketing and Economics (QME), Springer, vol. 1(2), pages 223-244, June.
    11. Lamey, L. & Deleersnyder, B. & Dekimpe, M.G. & Steenkamp, J-B.E.M., 2005. "The Impact of Business-Cycle Fluctuations on Private-Label Share," ERIM Report Series Research in Management ERS-2005-061-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    12. Greg M. Allenby & Thomas S. Shively & Sha Yang & Mark J. Garratt, 2004. "A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts," Marketing Science, INFORMS, vol. 23(1), pages 95-108, June.
    13. K. C. Mittal & Anupama Prashar, 2010. "A Study of Diversity in Retail Purchase Behaviour in Food and Grocery in Punjab: An Aid to Formulate Retail Strategy," Vision, , vol. 14(4), pages 255-265, October.
    14. Grewal, Dhruv & Janakiraman, Ramkumar & Kalyanam, Kirthi & Kannan, P.K. & Ratchford, Brian & Song, Reo & Tolerico, Stephen, 2010. "Strategic Online and Offline Retail Pricing: A Review and Research Agenda," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 138-154.
    15. Rafael SuArez VEGA & José Luis Gutiérrez ACUNA & Manuel Rodriguez DiAZ, 2015. "Spatial Analysis Of Consumer Behavior In A Food Products Market," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 10(1), pages 25-42, February.
    16. Bernhard Swoboda & Cathrin Puchert & Dirk Morschett, 2016. "Explaining the differing effects of corporate reputation across nations: a multilevel analysis," Journal of the Academy of Marketing Science, Springer, vol. 44(4), pages 454-473, July.
    17. Ryan W. Buell & Dennis Campbell & Frances X. Frei, 2021. "The Customer May Not Always Be Right: Customer Compatibility and Service Performance," Management Science, INFORMS, vol. 67(3), pages 1468-1488, March.
    18. Jean-Pierre Dubé, 2004. "Multiple Discreteness and Product Differentiation: Demand for Carbonated Soft Drinks," Marketing Science, INFORMS, vol. 23(1), pages 66-81, September.
    19. Peter J. Danaher, 2002. "Optimal Pricing of New Subscription Services: Analysis of a Market Experiment," Marketing Science, INFORMS, vol. 21(2), pages 119-138, February.
    20. Rhodes, Charles, 2010. "Demographic Variability In U.S. Consumer Responsiveness To Carbonated Soft-Drink Marketing Practices," 115th Joint EAAE/AAEA Seminar, September 15-17, 2010, Freising-Weihenstephan, Germany 116419, European Association of Agricultural Economists.
    21. Sanghak Lee & Greg M. Allenby, 2014. "Modeling Indivisible Demand," Marketing Science, INFORMS, vol. 33(3), pages 364-381, May.
    22. CAMPO, Katia & GIJSBRECHTS, Els & NISOL, Patricia, 1999. "Towards a conceptual framework of out-of-stock behaviour: The impact of product, consumer, and situation characteristics on out-of-stock reactions," Working Papers 1999023, University of Antwerp, Faculty of Business and Economics.
    23. Dawes, John G., 2012. "Brand-Pack Size Cannibalization Arising from Temporary Price Promotions," Journal of Retailing, Elsevier, vol. 88(3), pages 343-355.
    24. Groznik, Ana & Heese, H. Sebastian, 2010. "Supply chain interactions due to store-brand introductions: The impact of retail competition," European Journal of Operational Research, Elsevier, vol. 203(3), pages 575-582, June.

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