用大模型代理模拟买卖全过程，预测销售效果。
What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents
- 构建统一框架，用角色化代理模拟从推销到成交的全流程
- 复现真实消费规律，如价格与需求关系、人群差异弹性
- 适合测试营销策略、分析互动行为，无需真实投放
在部署前评估零售策略困难，因结果受多阶段影响，包括卖家说服、买卖交互和购买决策。现有仿真工具仅覆盖部分环节，未能建模跨阶段依赖，难以评估早期决策对下游结果的影响。我们提出 RetailSim，一个端到端零售仿真框架，通过多样化产品空间、角色驱动的智能体和多轮交互，在统一环境中建模完整流程，注重仿真保真度。采用双协议评估：人类评估行为保真度，元评估对照真实经济规律。结果表明，该框架成功复现了关键模式，如人口统计购买行为、价格-需求关系及异质价格弹性。进一步通过面向决策的应用案例展示其价值：角色推断、买卖交互分析与销售策略评估，证明 RetailSim 可作为可控测试平台探索零售策略。
原文摘要 · Abstract (English)
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.
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