What Makes a Sale? Rethinking 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.

Figures (9)

• Figure 1: Overview of RetailSim: The framework models retail interactions as a unified, multi-stage pipeline, capturing seller strategies, multi-turn interactions, and downstream buyer outcomes, enabling end-to-end analysis of how decisions propagate across stages.
• Figure 2: Estimated personas of Five LLMs as seller (top) and buyer (bottom) roles. Each bar represents the probability assigned to the dominant label in a binary trait classification.
• Figure 3: Normalized revenue heatmap across five LLMs.
• Figure 4: Example of annotation template for sales script quality evaluation (1–5 Likert scale). Each HIT contains 10 scripts with 5 sections each.
• Figure 5: Example of annotation template for pre- and post-purchase inquiry naturalness evaluation (1–5 Likert scale). Each HIT contains 5 inquiries.