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

2025-10-20

AI Summary

Traditional consumer behavior modeling relies on post-hoc analysis and rule-based agent models, limiting its capacity to capture cognitive complexity and emergent social dynamics. To address this, we propose a large language model (LLM)-driven generative multi-agent system that abandons predefined rules and instead enables dynamic simulation of consumer decision-making, habit formation, and social diffusion through natural-language interaction, internal cognitive modeling, and co-evolutionary learning. Deployed in a price-promotion sandbox environment, the system autonomously generates interpretable strategic feedback and— for the first time—uncovers latent population-level consumption patterns and social cascade effects. Compared to conventional approaches, our framework achieves higher ecological validity, lower experimental cost, and superior scalability, establishing a novel computational experimentation paradigm for pre-testing marketing strategies.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Simulating consumer decision-making for marketing strategy evaluation
• Capturing complex human behavior beyond rule-based models
• Providing scalable pre-implementation testing with emergent social patterns

Innovation

• LLM-powered multi-agent framework simulates consumer decisions
• Generative agents interact without predefined rules
• System reveals emergent social patterns for marketing