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

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 differe...

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Bibliographic Details
Published in:Journal of business & economic statistics 2011-04, Vol.29 (2), p.319-326
Main Authors: Andrews, Rick L., Currim, Imran S., Leeflang, Peter S. H.
Format: Article
Language:English
Subjects:
Brands
Coefficients
Comparative studies
Datasets
Demand curves
Economic models
Finite mixture model
Heterogeneity
Market prices
Marketing
Modeling
Multilevel models
Nested logit
Parametric models
Pulp & paper industry
Random coefficients model
Sales forecasting
Sales promotions
Simulation
Simulations
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Summary: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.
ISSN:0735-0015
1537-2707
DOI:10.1198/jbes.2010.07225