A common desire amongst practitioners is to have a data-driven personalized pricing strategy. It is also desirable to have this pricing policy be simple and interpretable, so it can be verified, monitored and easily implemented. Many efforts to incorporate machine learning into pricing do not satisfy this criteria. We present a prescriptive tree-based algorithm which segments customers with similar valuations and prescribes prices in such way which maximizes revenue, while maintaining interpretability. Furthermore, we present bounds on the revenue of a simple interpretable pricing strategy relative to the fully personalized data driven pricing policy.