Ticket Pricing via Prescriptive Model Distillation

Abstract

Powerful blackbox machine learning models often lead to complex policies which are difficult to verify and manage. Biggs et. al 2021 proposed a decision tree approach to extract revenue-maximizing pricing policies which are also interpretable by separating the counterfactual estimation and policy learning steps. We implement this method on premium ticket pricing with a large international airline. Backtest results show that this method is capable of achieving significant improvement over the current pricing with just a few rules and it is being currently integrated into the analytics pipeline for a live pilot.