Optimal price targeting
File(s)
Accepted version
Author(s)
Smith, Adam N
Seiler, Stephan
Aggarwal, Ishant
Type
Journal Article
Abstract
We study the profitability of personalized pricing policies in a setting with consumer-level panel data. To compare pricing policies, we propose an inverse probability-weighted estimator of profits, discuss how to handle nonrandom price variation, and show how to apply it in a typical consumer-packaged good market with supermarket scanner data. We generate pricing policies from Bayesian hierarchical choice models, regularized regressions, neural networks, and nonparametric classifiers using different sets of data inputs. We find that the performance of machine learning methods is highly varied, ranging from a 30.7% loss to a 14.9% gain relative to a blanket couponing strategy, whereas hierarchical models generate profit gains in the range of 13−16.7%. Across all models, information on consumers’ purchase histories leads to large improvements in profits, whereas demographic information has only a small impact. We find that out-of-sample fit statistics are uncorrelated with profit estimates and provide poor guidance toward model selection.
Date Issued
2022-08-02
Date Acceptance
2022-07-13
Citation
Marketing Science, 2022
ISSN
0732-2399
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Journal / Book Title
Marketing Science
Copyright Statement
Copyright © 2022, INFORMS
Identifier
https://pubsonline.informs.org/doi/10.1287/mksc.2022.1387
Subjects
Targeting
Personalization
Heterogeneity
Choice Models
Machine Learning
1505 Marketing
Marketing
Publication Status
Published online
Date Publish Online
2022-08-30