Arbeitspapier
Optimal Price Targeting
We examine the profitability of personalized pricing policies that are derived using different specifications of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized regressions, and classification trees using different sets of data inputs. To compare pricing policies, we employ an inverse probability weighted estimator of profits that explicitly takes into account non-random price variation and the panel nature of the data. We find that the performance of machine learning models is highly varied, ranging from a 21% loss to a 17% gain relative to a blanket couponing strategy, and a standard Bayesian hierarchical logit model achieves a 17.5% gain. Across all models purchase histories lead to large improvements in profits, but demographic information only has a small impact. We show that out-of-sample hit probabilities, a standard measure of model performance, are uncorrelated with our profit estimator and provide poor guidance towards model selection.
- Language
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Englisch
- Bibliographic citation
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Series: CESifo Working Paper ; No. 9439
- Classification
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Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Neural Networks and Related Topics
Model Evaluation, Validation, and Selection
Consumer Economics: Empirical Analysis
Production, Pricing, and Market Structure; Size Distribution of Firms
Retail and Wholesale Trade; e-Commerce
- Subject
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targeting
personalization
heterogeneity
choice models
machine learning
- Event
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Geistige Schöpfung
- (who)
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Smith, Adam N.
Seiler, Stephan
Aggarwal, Ishant
- Event
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Veröffentlichung
- (who)
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Center for Economic Studies and ifo Institute (CESifo)
- (where)
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Munich
- (when)
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2021
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Smith, Adam N.
- Seiler, Stephan
- Aggarwal, Ishant
- Center for Economic Studies and ifo Institute (CESifo)
Time of origin
- 2021
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