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

• URL: http://arxiv.org/abs/2510.18155v1
• Date: Mon, 20 Oct 2025 23:15:44 GMT
• Title: LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior
• Authors: Man-Lin Chu, Lucian Terhorst, Kadin Reed, Tom Ni, Weiwei Chen, Rongyu Lin,
• Abstract summary: We introduce an LLM-powered multi-agent simulation framework that models consumer decisions and social dynamics.<n>In a price-discount marketing scenario, the system delivers actionable strategy-testing outcomes.
• Score: 1.6352616029995921
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating consumer decision-making is vital for designing and evaluating marketing strategies before costly real- world 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 envi- ronment, 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 con- ventional 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.

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