Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization
This addresses the challenge for pricing operators in applying price-optimization methods to real-world operations by providing reasonable and interpretable bounds, though it is incremental as it builds on existing methods.
The study tackled the problem of determining interpretable price bounds for price optimization by proposing a framework that estimates bounds from historical data and adjusts them with shape constraints, achieving effectiveness demonstrated through numerical experiments on real service data.
This study addresses the interpretable estimation of price bounds in the context of price optimization. In recent years, price-optimization methods have become indispensable for maximizing revenue and profits. However, effective application of these methods to real-world pricing operations remains a significant challenge. It is crucial for operators responsible for setting prices to utilize reasonable price bounds that are not only interpretable but also acceptable. Despite this necessity, most studies assume that price bounds are given constant values, and few have explored reasonable determinations of these bounds. Therefore, we propose a comprehensive framework for determining price bounds that includes both the estimation and adjustment of these bounds. Specifically, we first estimate price bounds using three distinct approaches based on historical pricing data. Then, we adjust the estimated price bounds by solving an optimization problem that incorporates shape constraints. This method allows the implementation of price optimization under practical and reasonable price bounds suitable for real-world applications. We report the effectiveness of our proposed method through numerical experiments using historical pricing data from actual services.