Correlated Dynamics in Marketing Sensitivities

TL;DR

This paper introduces a hierarchical dynamic factor model using Bayesian nonparametric Gaussian processes to capture correlated, time-varying marketing sensitivities across categories, improving prediction and estimation accuracy.

Contribution

It presents a novel framework for modeling correlated dynamic heterogeneity in marketing sensitivities across categories using Bayesian nonparametric methods.

Findings

A significant portion of dynamic heterogeneity is explained by few global trends.

The model improves predictive accuracy over existing methods.

Patterns in sensitivity evolution across categories are characterized.

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.