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Modeling Consumer Demand for Variety

Author

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  • Jaehwan Kim

    (Leeds School of Business, University of Colorado at Boulder, Boulder, Colorado 80309)

  • Greg M. Allenby

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

  • Peter E. Rossi

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

Consumers are often observed to purchase more than one variety of a product on a given shopping trip. The simultaneous demand for varieties is observed not only for packaged goods such as yogurt or soft drinks, but in many other product categories such as movies, music compact disks, and apparel. Multinomial (MN) choice models cannot be applied to data exhibiting the simultaneous choice of more than one variety. The random utility interpretation of either the MN logit or probit model uses a linear utility specification that cannot accommodate interior solutions with more than one variety (alternative) chosen. To analyze data with multiple varieties chosen requires a nonstandard utility specification. Standard demand models in the economics literature exhibit only interior solutions. We propose a demand model based on a translated additive utility structure. The model nests the linear utility structure, while allowing for the possibility of a mixture of corner and interior solutions where more than one but not all varieties are selected. We use a random utility specification in which the unobservable portion of marginal utility follows a log-normal distribution. The distribution of quantity demanded (the basis of the likelihood function) is derived from these log-normal random utility errors. The likelihood function for this class of models with mixtures of corner and interior solutions is a mixed distribution with both a continuous density portion and probability mass points for the corners. The probability mass points must be calculated by integrals of the log-normal errors over rectangular regions. We evaluate these high-dimensional integrals using the GHK approximation. We employ a Bayesian hierarchical model, allowing household-specific utility parameters. Our utility specification related to the approach of Wales and Woodland (1983) who employ a translated quadratic utility function. Wales and Woodland were only able to study, at the most, three varieties because there was no practical way to evaluate the utility function at that time. In addition, the quadratic utility specification is not a globally valid utility function, making welfare computations and policy experiments questionable. Hendel (1999) and Dube (1999) present an alternative approach in the utility function which is constructed by summing up over unobservable consumption occasions. While only one variety is consumed on each occasion, the marginal utilities of varieties change over the consumption occasions, giving rise to a simultaneous purchase of multiple varieties. Our Bayesian inference approach allows us to obtain individual household estimates of utility parameters. Household utility estimates are used to compute the value of each variety. We compute a compensating value for the removal of each flavor; that is, we compute the monetary equivalent of the household's loss in utility from removal of a flavor. These calculations show that households highly value popular flavors and would incur substantial utility losses from removal of these flavors from the yogurt assortment. Next we consider the implications of our model for retailer assortment and pricing policies. Given limited shelf space, only a subset of the possible varieties can be displayed for purchase at any one time. If consumers value variety, then a retailer with lower variety must compensate the consumers in some way, such as a lower price level. We see this trade-off between price and variety across different retailing formats. Discount or warehouse format retailers often have both lower variety and lower prices. To measure this trade-off, we explore the utility loss from reduction in variety and find the reductions in price that will compensate for this utility loss. These price reduction calculations must be based on a valid utility structure. Heterogeneity in tastes is critical in these utility computations and policy experiments. We find that a relatively small fraction of households with extreme preferences dominate the compensating value computations. That is, some households are observed to purchase mostly or exclusively one variety. These households must be heavily compensated for the removal of this variety from the assortment. In some retailing contexts, customization of the assortment is possible at the customer level. We show that such customization virtually eliminates any utility loss from reduction in variety.

Suggested Citation

  • Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
  • Handle: RePEc:inm:ormksc:v:21:y:2002:i:3:p:229-250
    DOI: 10.1287/mksc.21.3.229.143
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    References listed on IDEAS

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    1. Matthew Gentzkow, 2007. "Valuing New Goods in a Model with Complementarity: Online Newspapers," American Economic Review, American Economic Association, vol. 97(3), pages 713-744, June.
    2. Batarce, Marco & Ivaldi, Marc, 2014. "Urban travel demand model with endogenous congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 331-345.
    3. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
    4. Pradeep Chintagunta & Jean-Pierre Dubé & Vishal Singh, 2003. "Balancing Profitability and Customer Welfare in a Supermarket Chain," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 111-147, March.
    5. Greg Allenby & Geraldine Fennell & Joel Huber & Thomas Eagle & Tim Gilbride & Dan Horsky & Jaehwan Kim & Peter Lenk & Rich Johnson & Elie Ofek & Bryan Orme & Thomas Otter & Joan Walker, 2005. "Adjusting Choice Models to Better Predict Market Behavior," Marketing Letters, Springer, vol. 16(3), pages 197-208, December.
    6. Sikder, Sujan & Pinjari, Abdul Rawoof, 2013. "The benefits of allowing heteroscedastic stochastic distributions in multiple discrete-continuous choice models," Journal of choice modelling, Elsevier, vol. 9(C), pages 39-56.
    7. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    8. Castro, Marisol & Bhat, Chandra R. & Pendyala, Ram M. & Jara-Díaz, Sergio R., 2012. "Accommodating multiple constraints in the multiple discrete–continuous extreme value (MDCEV) choice model," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 729-743.
    9. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan & Sen, Sudeshna, 2006. "A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 827-850, December.
    10. Pinjari, Abdul Rawoof & Bhat, Chandra, 2010. "A multiple discrete-continuous nested extreme value (MDCNEV) model: Formulation and application to non-worker activity time-use and timing behavior on weekdays," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 562-583, May.
    11. Pinjari, Abdul Rawoof, 2011. "Generalized extreme value (GEV)-based error structures for multiple discrete-continuous choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 474-489, March.
    12. Sergi Jiménez-Martín & Antonio Ladrón de Guevara-Martínez, 2009. "A state-dependent model of hybrid behavior with rational consumers in the attribute space," Investigaciones Economicas, Fundación SEPI, vol. 33(3), pages 347-383, September.
    13. Jungwoo Shin & Chang Seob Kimi & Jongsu Lee, 2009. "Model for Studying Commodity Bundling with a Focus on Consumer Preference," TEMEP Discussion Papers 200934, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Nov 2009.
    14. Zhang, Xiao & Boscardin, W. John & Belin, Thomas R., 2008. "Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3697-3708, March.
    15. Matthew Gentzkow, 2006. "Valuing New Goods in a Model with Complementarities: Online Newspapers," NBER Working Papers 12562, National Bureau of Economic Research, Inc.
    16. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
    17. Jeong, Jaehoon & Seob Kim, Chang & Lee, Jongsu, 2011. "Household electricity and gas consumption for heating homes," Energy Policy, Elsevier, vol. 39(5), pages 2679-2687, May.
    18. Jäggi, Boris & Weis, Claude & Axhausen, Kay W., 2013. "Stated response and multiple discrete-continuous choice models: Analyses of residuals," Journal of choice modelling, Elsevier, vol. 6(C), pages 44-59.
    19. Ahn, Jiwoon & Jeong, Gicheol & Kim, Yeonbae, 2008. "A forecast of household ownership and use of alternative fuel vehicles: A multiple discrete-continuous choice approach," Energy Economics, Elsevier, vol. 30(5), pages 2091-2104, September.
    20. Teichert, Thorsten & Shehu, Edlira & von Wartburg, Iwan, 2008. "Customer segmentation revisited: The case of the airline industry," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(1), pages 227-242, January.
    21. McCabe, Mark J. & Nevo, Aviv & Rubinfeld, Daniel L., 2006. "The Pricing of Academic Journals," Berkeley Olin Program in Law & Economics, Working Paper Series qt13d1h835, Berkeley Olin Program in Law & Economics.
    22. Empen, Janine, 2011. "Preissetzung Auf Dem Deutschen Joghurtmarkt: Eine Hedonische Analyse," 51st Annual Conference, Halle, Germany, September 28-30, 2011 115362, German Association of Agricultural Economists (GEWISOLA).
    23. von Haefen, Roger H., 2007. "Empirical strategies for incorporating weak complementarity into consumer demand models," Journal of Environmental Economics and Management, Elsevier, vol. 54(1), pages 15-31, July.
    24. Desai, Kalpesh Kaushik & Trivedi, Minakshi, 2014. "Do consumer perceptions matter in measuring choice variety and variety seeking?," Journal of Business Research, Elsevier, vol. 67(1), pages 2786-2792.
    25. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.

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