Problem
Research questions and friction points this paper is trying to address.
retail simulation
end-to-end dynamics
buyer-seller interaction
cross-stage dependencies
sales strategy evaluation
What Makes a Sale? Rethinking End-to-End Seller--Buyer Retail Dynamics with LLM Agents
🤖 AI Summary
Existing retail simulators struggle to model the cross-stage influence of sellers’ early decisions on final purchases through multi-stage interactions. This work proposes RetailSim—the first end-to-end retail simulation framework—that integrates a diverse product space, persona-driven large language model agents, and multi-round buyer-seller interactions to holistically model the entire purchase journey from persuasion to transaction. RetailSim enables high-fidelity simulation of cross-stage dependencies for the first time, supporting both sales strategy evaluation and buyer persona inference. Its validity is established through dual verification: human behavioral fidelity assessment and meta-evaluation against established economic principles. Experiments successfully reproduce key empirical regularities—including demographic purchasing disparities, price-demand relationships, and heterogeneous price elasticities—demonstrating the framework’s practical utility for strategy testing.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
end-to-end retail simulation
LLM agents
persona-driven modeling
multi-turn buyer-seller interaction
economic regularities validation
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