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A Comparison of Sales Response Predictions From Demand Models Applied to Store-Level versus Panel Data

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  • Rick L. Andrews
  • Imran S. Currim
  • Peter S. H. Leeflang

Abstract

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Suggested Citation

  • Rick L. Andrews & Imran S. Currim & Peter S. H. Leeflang, 2011. "A Comparison of Sales Response Predictions From Demand Models Applied to Store-Level versus Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 319-326, April.
    • Handle: RePEc:taf:jnlbes:v:29:y:2011:i:2:p:319-326
    DOI: 10.1198/jbes.2010.07225
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    Cited by:

    1. Alantari, Huwail J. & Currim, Imran S. & Deng, Yiting & Singh, Sameer, 2022. "An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 1-19.
    2. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    3. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.
    4. Choi, Sunhee & Duhan, Dale F. & Dass, Mayukh, 2023. "The influence of corporate social responsibility appeals (CSRAs) on product sales: Which appeal types perform better?," Journal of Retailing, Elsevier, vol. 99(1), pages 115-135.
    5. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    6. Leeflang, Peter, 2011. "Paving the way for “distinguished marketing”," International Journal of Research in Marketing, Elsevier, vol. 28(2), pages 76-88.
    7. Cuellar, Steven S. & Brunamonti, Marco, 2014. "Retail channel price discrimination," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 339-346.

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