什么促成了一次销售？通过LLM代理重新思考端到端的卖家-买家零售动态
What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents
AI总结：
本文提出RetailSim框架，通过统一环境建模零售流程，展现行为忠实度和经济规律，用于分析买家-卖家互动及销售策略评估。
摘要：
在部署前评估零售策略具有挑战性，因为结果由多个阶段决定，从卖家说服通过买家-卖家互动到购买决策。然而，现有零售模拟器仅捕捉该过程的部分方面，并未建模跨阶段依赖性，难以评估早期决策如何影响下游结果。我们提出了RetailSim，一个端到端的零售模拟框架，通过统一环境建模该流程，专门设计用于通过多样化产品空间、基于人物的代理和多轮互动实现模拟保真度。我们通过双协议评估RetailSim，包括人类对行为保真的评估和针对现实世界经济规律的元评估，证明其能够再现关键模式，如人口统计学购买行为、价格-需求关系和异质性价格弹性。我们进一步通过面向决策的用例展示其实用性，包括人物推断、买家-卖家互动分析和销售策略评估，证明RetailSim作为探索零售策略受控测试床的潜力。
英文摘要：
Evaluating retail strategies before deployment is difficult, as outcomes are determined across multiple stages, from seller-side persuasion through buyer-seller interaction to purchase decisions. However, existing retail simulators capture only partial aspects of this process and do not model cross-stage dependencies, making it difficult to assess how early decisions affect downstream outcomes. We present RetailSim, an end-to-end retail simulation framework that models this pipeline in a unified environment, explicitly designed for simulation fidelity through diverse product spaces, persona-driven agents, and multi-turn interactions. We evaluate RetailSim with a dual protocol comprising human evaluation of behavioral fidelity and meta-evaluation against real-world economic regularities, showing that it successfully reproduces key patterns such as demographic purchasing behavior, the price-demand relationship, and heterogeneous price elasticity. We further demonstrate its practical utility via decision-oriented use cases, including persona inference, seller-buyer interaction analysis, and sales strategy evaluation, showing RetailSim's potential as a controlled testbed for exploring retail strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
