Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

Max Biggs, Wei Sun, Markus Ettl
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:946-956, 2021.

Abstract

Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies that are not interpretable, resulting in slow adoption in practice. We present a novel, customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-biggs21a, title = {Model Distillation for Revenue Optimization: Interpretable Personalized Pricing}, author = {Biggs, Max and Sun, Wei and Ettl, Markus}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {946--956}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/biggs21a/biggs21a.pdf}, url = {https://proceedings.mlr.press/v139/biggs21a.html}, abstract = {Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies that are not interpretable, resulting in slow adoption in practice. We present a novel, customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.} }
Endnote
%0 Conference Paper %T Model Distillation for Revenue Optimization: Interpretable Personalized Pricing %A Max Biggs %A Wei Sun %A Markus Ettl %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-biggs21a %I PMLR %P 946--956 %U https://proceedings.mlr.press/v139/biggs21a.html %V 139 %X Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies that are not interpretable, resulting in slow adoption in practice. We present a novel, customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.
APA
Biggs, M., Sun, W. & Ettl, M.. (2021). Model Distillation for Revenue Optimization: Interpretable Personalized Pricing. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:946-956 Available from https://proceedings.mlr.press/v139/biggs21a.html.

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