Assortment Optimization Under History-Dependent Effects
研究了顾客对重复出现的品类产生疲劳时，如何根据历史搭配动态优化品类，实际数据表明此方法能提升高达10.4%的收入并保持产品多样性。适合零售、餐饮及平台运营者参考。
Curing Consumer Fatigue: A Data-Driven Approach to Dynamic Assortment Have you ever grown tired of seeing the same lunch options at your corporate cafeteria every day? This phenomenon, known as customer satiation, poses a major challenge for businesses serving repeat customers, such as dining services and online flash-sale platforms. When customer utility depends on historical offerings, traditional assortment planning methods often fail, sacrificing either overall revenue or product variety. In the paper “Assortment Optimization Under History-Dependent Effects,” the authors develop a new optimization framework to capture how past assortments influence future customer preferences. By reformulating this challenging nonlinear problem into tractable mixed-integer programs, they derive optimal assortment policies over time. Using real-world corporate cafeteria data, the study demonstrates revenue improvements of up to 10.4% over static assortment policies while maintaining product variety and reducing customer fatigue.