Model Distillation for Revenue Optimization: Interpretable Personalized Pricing

• URL: http://arxiv.org/abs/2007.01903v2
• Date: Wed, 9 Jun 2021 18:22:11 GMT
• Title: Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
• Authors: Max Biggs, Wei Sun, Markus Ettl
• Abstract summary: We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm. It segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability.
• Score: 8.07517029746865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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