Significant research effort has been devoted in recent years to developing personalized pricing, promotions, and product recommendation algorithms that can leverage rich customer data to learn and earn. Systematic benchmarking and evaluation of these causal learning systems remains a critical challenge, due to the lack of suitable datasets and simulation environments. In this work, we propose a multi-stage model for simulating customer shopping behavior that captures important sources of heterogeneity, including price sensitivity and past experiences. We embedded this model into a working simulation environment -- RetailSynth. RetailSynth was carefully calibrated on publicly available grocery data to create realistic synthetic shopping transactions. Multiple pricing policies were implemented within the simulator and analyzed for impact on revenue, category penetration, and customer retention. Applied researchers can use RetailSynth to validate causal demand models for multi-category retail and to incorporate realistic price sensitivity into emerging benchmarking suites for personalized pricing, promotions, and product recommendations.

翻译：近年来，大量研究工作致力于开发能够利用丰富客户数据进行学习与收益优化的个性化定价、促销及产品推荐算法。由于缺乏合适的数据集和仿真环境，对这些因果学习系统的系统性基准测试与评估仍是一项关键挑战。本文提出了一种多阶段模型来模拟客户购物行为，该模型捕捉了价格敏感度和历史经验等重要异质性来源。我们将该模型嵌入到可运行的仿真环境——RetailSynth中。RetailSynth基于公开可用的杂货数据经过精心校准，可生成逼真的合成购物交易记录。仿真器中实现了多种定价策略，并分析了其对收入、品类渗透率和客户留存率的影响。应用研究人员可利用RetailSynth验证多品类零售的因果需求模型，并将真实的消费者价格敏感度纳入个性化定价、促销及产品推荐的新兴基准测试体系。