Model Distillation for Revenue Optimization: Interpretable Personalized Pricing
This addresses the need for simple, verifiable pricing strategies in business applications, though it is incremental as it builds on existing model distillation and tree-based methods.
The paper tackles the problem of creating interpretable personalized pricing policies by distilling a complex black-box model into a tree-based algorithm, achieving revenue maximization with quantified regret in synthetic and real-world datasets.
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.