Abstract
The goal of price optimization is to maximize total revenue by adjusting the prices of products, on the basis of predicted sales numbers that are functions of pricing strategies. Recent advances in demand modeling using machine learning raise a new challenge in price
optimization, i.e., how to manage statistical errors in estimation. In this paper, we show that uncertainty in recently-proposed prescriptive price optimization frameworks can be represented by a matrix normal distribution. For this particular uncertainty, we propose novel robust quadratic programming algorithms for conservative lower-bound maximization. We offer an asymptotic probabilistic guarantee of conservativeness of our formulation. Our experiments on both artificial and actual price data show that our robust price optimization allows users to determine best risk-return trade-offs and to explore safe, profitable price strategies.
Keywords
- Uncertainty in AI: Decision/Utility Theory
- Constraints and Satisfiability: Constraint Optimisation
