Correlated Dynamics in Marketing Sensitivities

41 Pages Posted: 23 Apr 2021 Last revised: 19 Mar 2024

See all articles by Ryan Dew

Ryan Dew

University of Pennsylvania - Marketing Department

Yuhao Fan

affiliation not provided to SSRN

Date Written: March 13, 2024

Abstract

Understanding individual customers' sensitivities to prices, promotions, brands, and other marketing mix elements is fundamental to a wide swath of marketing problems. An important but understudied aspect of this problem is the dynamic nature of these sensitivities, which change over time and vary across individuals. Prior work has developed methods for capturing such dynamic heterogeneity within product categories, but neglected the possibility of correlated dynamics across categories. In this work, we introduce a framework to capture such correlated dynamics using a hierarchical dynamic factor model, where individual preference parameters are influenced by common cross-category dynamic latent factors, estimated through Bayesian nonparametric Gaussian processes. We apply our model to grocery purchase data, and find that a surprising degree of dynamic heterogeneity can be accounted for by only a few global trends. We also characterize the patterns in how consumers' sensitivities evolve across categories. Managerially, the proposed framework not only enhances predictive accuracy by leveraging cross-category data, but enables more precise estimation of quantities of interest, like price elasticity.

Keywords: Bayesian nonparametrics, Gaussian process, choice models, machine learning

JEL Classification: C01, C11, C25

Suggested Citation

Dew, Ryan and Fan, Yuhao, Correlated Dynamics in Marketing Sensitivities (March 13, 2024). Available at SSRN: https://ssrn.com/abstract=3832290 or http://dx.doi.org/10.2139/ssrn.3832290

Ryan Dew (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

Yuhao Fan

affiliation not provided to SSRN

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