  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.
