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Avoiding aggregation bias in demand estimation: A multivariate promotional disaggregation approach

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  • Steven Tenn

Abstract

Demand models produce biased results when applied to data aggregated across stores with heterogeneous promotional activity. We show how to modify extant aggregate demand frameworks to avoid this problem. First a consumer-level model is developed, which is then integrated over the heterogeneous stores to arrive at aggregate demand. Our approach is highly practical since it requires only standard scanner data of the type produced by the major vendors. Using data for super-premium ice cream, we apply the proposed methodology to the random coefficients logit demand framework. Copyright Springer Science + Business Media, LLC 2006

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  • Steven Tenn, 2006. "Avoiding aggregation bias in demand estimation: A multivariate promotional disaggregation approach," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 383-405, December.
    • Handle: RePEc:kap:qmktec:v:4:y:2006:i:4:p:383-405
    DOI: 10.1007/s11129-006-9011-3
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    References listed on IDEAS

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    Cited by:

    1. Hernán A. Bruno & Naufel J. Vilcassim, 2008. "—Structural Demand Estimation with Varying Product Availability," Marketing Science, INFORMS, vol. 27(6), pages 1126-1131, 11-12.
    2. Draganska, Michaela & Klapper, Daniel, 2010. "Choice Set Heterogeneity and the Role of Advertising: An Analysis with Micro and Macro Data," Research Papers 2063, Stanford University, Graduate School of Business.
    3. Tenn, Steven & Yun, John M., 2008. "Biases in demand analysis due to variation in retail distribution," International Journal of Industrial Organization, Elsevier, vol. 26(4), pages 984-997, July.
    4. Zizhuo Wang & Chaolin Yang & Hongsong Yuan & Yaowu Zhang, 2021. "Aggregation Bias in Estimating Log‐Log Demand Function," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 3906-3922, November.
    5. Jean-Pierre H. Dubé & Ali Hortaçsu & Joonhwi Joo, 2020. "Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares," Working Papers 2020-13, Becker Friedman Institute for Research In Economics.
    6. Tenn, Steven & Froeb, Luke & Tschantz, Steven, 2010. "Mergers when firms compete by choosing both price and promotion," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 695-707, November.
    7. Piyush Anand & Clarence Lee, 2023. "Using Deep Learning to Overcome Privacy and Scalability Issues in Customer Data Transfer," Marketing Science, INFORMS, vol. 42(1), pages 189-207, January.
    8. Matthew J. Schneider & Sharan Jagpal & Sachin Gupta & Shaobo Li & Yan Yu, 2018. "A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data," Marketing Science, INFORMS, vol. 37(1), pages 153-171, January.
    9. Jean-Pierre H. Dubé & Ali Hortaçsu & Joonhwi Joo, 2020. "Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares," NBER Working Papers 26795, National Bureau of Economic Research, Inc.
    10. Jean-Pierre Dubé & Ali Hortaçsu & Joonhwi Joo, 2021. "Random-Coefficients Logit Demand Estimation with Zero-Valued Market Shares," Marketing Science, INFORMS, vol. 40(4), pages 637-660, July.

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