09AI-Driven Marketing Research (Persona Generation & Multi-Agent Simulations)
Role: Solution Engineer / Prototyper
Executive summary
Prototyped an AI persona framework using multi-agent LLM simulations to generate synthetic customer personas and stress-test new product concepts before costly market research.
- Azure OpenAI
- Microsoft Agent Framework
- TinyTroupe (Microsoft Research)
- NLP sentiment analysis
A consumer-banking client explored using AI for market research and product development—creating customer personas and simulating audience reactions to new financial products before expensive real-world studies. We proposed generative AI and multi-agent systems to create synthetic personas, run simulations and extract insights.
- Use LLM prompts to create detailed, realistic personas (e.g., tech-savvy millennial investor, risk-averse retiree) with attributes and behavioral scripts.
- Place persona agents in scenarios mimicking early product research (e.g., a simulated focus group on a new card feature).
- Capture conversations and decisions to derive insights (likes, objections, questions).
- Evaluate whether simulations yield plausible, valuable insights to supplement traditional research.
I built a sandbox on Azure OpenAI using Microsoft Agent Framework for multi-agent orchestration and TinyTroupe (Microsoft's open-source persona-simulation library) to define realistic TinyPerson personas and place them in a simulated TinyWorld, plus a moderator agent representing the product to drive discussion. Transcripts were aggregated and analyzed for sentiment and themes (via NLP and LLM summarization). I refined persona realism with the client's marketing team using real customer profiles as guidance, keeping everything in a secure environment with no sensitive real data.
The simulations produced fresh qualitative insights—one persona consistently raised mobile-app security concerns, flagging a marketing emphasis. While clearly speculative, the client valued it as a creative complement that helped form hypotheses to later test with surveys, showcasing generative AI in a consultative role and impressing their innovation lead.
The project highlighted boundaries and ethics in AI simulations—careful prompt and agent design avoided stereotypes and inappropriate content. Multi-agent simulations can spark intriguing results but must be validated with real data; they augment human creativity rather than replace user feedback.