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arXiv · 2007.01903

Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

Max Biggs·Wei Sun·Markus Ettl
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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 which are not interpretable, resulting in slow adoption in practice. We present a 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.

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Max Biggs, Wei Sun, Markus Ettl. 2021-06-09. Model Distillation for Revenue Optimization: Interpretable Personalized Pricing. https://arxiv.org/abs/2007.01903

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