{"ID":2853022,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18155","arxiv_id":"2510.18155","title":"LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior","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 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.","short_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 s...","url_abs":"https://arxiv.org/abs/2510.18155","url_pdf":"https://arxiv.org/pdf/2510.18155v1","authors":"[\"Man-Lin Chu\",\"Lucian Terhorst\",\"Kadin Reed\",\"Tom Ni\",\"Weiwei Chen\",\"Rongyu Lin\"]","published":"2025-10-20T23:15:44Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.SI\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}