Student Publications [Scholarly]

LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior

Document Type

Conference Proceeding

Abstract

Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly realworld deployment. However, post-event analyses and rule-based agent-based models (ABMs) struggle to capture the complexity of human behavior and social interaction. We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics. Building on recent advances in large language model simulation in a sandbox environment, our framework enables generative agents to interact, express internal reasoning, form habits, and make purchasing decisions without predefined rules. In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes and reveals emergent social patterns beyond the reach of conventional methods. This approach offers marketers a scalable, low-risk tool for pre-implementation testing, reducing reliance on time-intensive post-event evaluations and lowering the risk of underperforming campaigns. © 2025 IEEE.

Publication Title

Proceedings - 2025 IEEE International Conference on e-Business Engineering, ICEBE 2025

Publication Date

11-2025

ISBN

9798331590383

DOI

10.1109/ICEBE68123.2025.00018

Keywords

artificial intelligence, business simulation, genai, llm, marketing strategy, multi-agent

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