arXiv 23 Apr 2021 · Statistics — Applications
arXiv:2104.11702 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by appendix_titled_section at “Appendix A: Visualization of Full Correlation Matrix” · 98% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dew, Ryan, Asim Ansari, and Yang Li (2020) Modeling Dynamic Heterogeneity Using Gaussian Processes self | 1.000 | 14 | 4 | 100% |
| 2 | Álvarez, Mauricio A., Lorenzo Rosasco, and Neil D. Lawrence (2012) Kernels for vector-valued functions: A review | 0.811 | 4 | 2 | 100% |
| 3 | Gordon, Brett R., Avi Goldfarb, and Yang Li (2013) Does Price Elasticity Vary with Economic Growth? A Cross-Category Analysis | 0.811 | 4 | 2 | 100% |
| 4 | Ainslie, Andrew and Peter E. Rossi (1998) Similarities in Choice Behavior Across Product Categories | 0.644 | 2 | 2 | 100% |
| 5 | Du, Rex Y. and Wagner A. Kamakura (2012) Quantitative Trendspotting | 0.644 | 2 | 2 | 100% |
| 6 | Dubé, Jean-Pierre, Günter J Hitsch, and Peter E Rossi (2018) Income and wealth effects on private-label demand: Evidence from the great recession | 0.644 | 2 | 2 | 100% |
| 7 | Teh, Yee Whye, Matthias Seeger, and Michael I. Jordan (2005) Semiparametric Latent Factor Models, (2005) | 0.644 | 2 | 2 | 100% |
| 8 | Singh, Vishal P., Karsten T. Hansen, and Sachin Gupta (2005) Modeling Preferences for Common Attributes in Multicategory Brand Choice | 0.511 | 2 | 1 | 100% |
| 9 | Betancourt, Michael (2018) A Conceptual Introduction to Hamiltonian Monte Carlo | 0.405 | 1 | 1 | 100% |
| 10 | Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman (2023) Detecting Routines: Applications to Ridesharing Customer Relationship Management self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | dark-blue Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models | 0.737 | 3 | 2 |