2025-12-11 –, Machine Learning & AI
Understanding customer behavior is essential in marketing. Traditionally, marketers rely on methods such as surveys, customer interviews, and focus groups to gather insights. However, these approaches can be expensive, time-consuming, and limited in scale and diversity.
Recently, multi-agent simulation powered by Large Language Models (LLMs) is emerging as an innovative technique. TinyTroupe, for example, enables the creation of different personas (e.g., budget‑minded Gen‑Z shoppers, premium‑seeking parents), allowing marketers to predict and optimize advertising effectiveness or replace time-consuming interviews rapidly.
In this talk, I will introduce the key concepts of LLM-powered multi-agent simulations, demonstrate their practical application in marketing through TinyTroupe, and share actionable insights and recommendations.
Agenda:
- Introduction - 1 min
- Why Synthetic Persona Simulation? (concept + traditional vs. LLM based) - 4 min
- Core concepts and techniques - 4 min
- Overview and comparison of key open-source libraries - 2 min
- Use-case demo: Pre-testing advertising campaigns - 7 min
- Use-case demo: Replacing customer interviews with simulations - 7 min
- Lessons learned ‒ practical tips and challenges - 3 min
- Q&A - 4 min
Key Takeaways:
- Understand the core concepts and advantages of LLM-powered multi-agent persona simulation.
- Learn how to leverage TinyTroupe for efficient and insightful marketing analytics.
Target Audience:
- Data analysts and data scientists interested in customer analytics and marketing.
- Marketers, business analysts, and executives seeking innovative approaches to understanding customer behavior and optimizing marketing strategies.
- IT specialists and developers interested in applying LLM and multi-agent simulation technologies to real-world business scenarios.
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Hajime is a data professional with 8+ years of expertise in marketing, retail, and eCommerce, working in New York.