| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP16096 |
| DP16096 Optimal Price Targeting | |
| Adam Smith; Stephan Seiler; Ishant Aggarwal | |
| 发表日期 | 2022-07-14 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | 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 non-random 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, while demographic information only has a small impact. We find that out-of-sample fit statistics are uncorrelated with profit estimates and provide poor guidance towards model selection. |
| 主题 | Industrial Organization |
| 关键词 | Targeting Personalization Heterogeneity Choice models Machine learning |
| URL | https://cepr.org/publications/dp16096-2 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/546563 |
| 推荐引用方式 GB/T 7714 | Adam Smith,Stephan Seiler,Ishant Aggarwal. DP16096 Optimal Price Targeting. 2022. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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