Correlated Dynamics in Marketing Sensitivities
This work addresses the problem of understanding dynamic customer sensitivities across categories for marketing managers, representing an incremental improvement over prior methods that focused only on within-category dynamics.
The paper tackled the problem of capturing correlated dynamics in customers' marketing sensitivities across product categories, which prior work had neglected, by introducing a hierarchical dynamic factor model using Bayesian nonparametric Gaussian processes; the result showed that a surprising degree of dynamic heterogeneity could be accounted for by only a few global trends, enhancing predictive accuracy and enabling more precise estimation of quantities like price elasticity.
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.