Optimal Feature-Based Market Segmentation and Pricing
研究了一种半个性化定价方法，通过客户特征进行市场细分并设定细分价格，证明在统计假设下可高效计算最优策略，且仅需少量细分就能接近完全个性化定价的收益。
Operationalizing Semipersonalized Pricing How can modern firms leverage feature information to set prices in way that is both profitable and practical? A new study in Operations Research addresses this question by analyzing feature-based market segmentation and pricing (FBMSP), a semipersonalized approach to pricing where firms use customer characteristics to group buyers and set segment-specific prices. Although businesses often rely on heuristic “segment-then-price” methods, in their article the authors show that under realistic statistical assumptions, the jointly optimal segmentation and pricing policy can be computed efficiently. Further, using structural results about the optimal FBMSP, the authors prove that semipersonalized pricing quickly converges to the performance of fully personalized pricing, motivating its use in practice. Finally, in a case study on U.S. home mortgage data, they apply their method and show it significantly outperforms traditional heuristics, achieving near-maximal revenue with only a few segments. This research offers both practical tools and theoretical insights for firms navigating the balance between personalization and implementability in pricing.