What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents

Title:What Makes a Sale? Simulating End-to-End Seller--Buyer Retail Dynamics with LLM Agents

Abstract: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.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2604.04468 [cs.AI]

(or arXiv:2604.04468v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2604.04468 Focus to learn more arXiv-issued DOI via DataCite

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