A Gaussian Process Model of Cross-Category Dynamics in Brand Choice

35 Pages Posted: 23 Apr 2021 Last revised: 25 Apr 2021

See all articles by Ryan Dew

Ryan Dew

University of Pennsylvania - Marketing Department

Yuhao Fan

affiliation not provided to SSRN

Date Written: April 22, 2021


Understanding individual customers’ sensitivities to prices, promotions, brand, and other aspects of the marketing mix is fundamental to a wide swath of marketing problems, including targeting and pricing. Companies that operate across many product categories have a unique opportunity, insofar as they can use purchasing data from one category to augment their insights in another. Such cross-category insights are especially crucial in situations where purchasing data may be rich in one category, and scarce in another. An important aspect of how consumers behave across categories is dynamics: preferences are not stable over time, and changes in individual-level preference parameters in one category may be indicative of changes in other categories, especially if those changes are driven by external factors. Yet, despite the rich history of modeling cross-category preferences, the marketing literature lacks a framework that flexibly accounts for correlated dynamics, or the cross-category interlinkages of individual-level sensitivity dynamics. In this work, we propose such a framework, leveraging individual-level, latent, multi-output Gaussian processes to build a non-parametric Bayesian choice model that allows information sharing of preference parameters across customers, time, and categories. We apply our model to grocery purchase data, and show that our model detects interesting dynamics of customers’ price sensitivities across multiple categories. Managerially, we show that capturing correlated dynamics yields substantial predictive gains, relative to benchmarks. Moreover, we find that capturing correlated dynamics can have implications for understanding changes in consumers preferences over time, and developing targeted marketing strategies based on those dynamics.

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

JEL Classification: C01, C11, C25

Suggested Citation

Dew, Ryan and Fan, Yuhao, A Gaussian Process Model of Cross-Category Dynamics in Brand Choice (April 22, 2021). 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

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics