A Comparison of Sales Response Predictions From Demand Models Applied to Store-Level versus Panel DataAndrews, R. L., Currim, I. S. & Leeflang, P. S. H., Apr-2011, In : Journal of Business & Economic Statistics. 29, 2, p. 319-326 8 p.
Research output: Contribution to journal › Article › Academic › peer-review
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.
|Number of pages||8|
|Journal||Journal of Business & Economic Statistics|
|Publication status||Published - Apr-2011|
- Finite mixture model, Heterogeneity, Nested logit, Random coefficients model, AGGREGATE DATA, SCANNER DATA, CHOICE MODELS, ENDOGENEITY, HETEROGENEITY, SEGMENTATION, STRATEGIES, MARKET