LLM-Based Multi-Agent System for Simulating and Analyzing Marketing and Consumer Behavior
This provides marketers with a scalable, low-risk tool for pre-implementation testing, reducing reliance on post-event evaluations, though it is incremental as it builds on existing LLM simulation methods.
The authors tackled the problem of simulating consumer decision-making for marketing strategy testing by introducing an LLM-powered multi-agent framework, which models consumer behavior and social dynamics without predefined rules, delivering actionable outcomes and revealing emergent patterns in a price-discount scenario.
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.