LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911224174739456 |
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| author | Chu, Man-Lin Terhorst, Lucian Reed, Kadin Ni, Tom Chen, Weiwei Lin, Rongyu |
| author_facet | Chu, Man-Lin Terhorst, Lucian Reed, Kadin Ni, Tom Chen, Weiwei Lin, Rongyu |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_18155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior Chu, Man-Lin Terhorst, Lucian Reed, Kadin Ni, Tom Chen, Weiwei Lin, Rongyu Artificial Intelligence Social and Information Networks 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. |
| title | LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior |
| topic | Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2510.18155 |