Hank AzarianConsulting
EST. 2010
AI / SEO

Persona World Models for Marketers: A Practical Prompt

How I build dynamic persona world models with AI for scenario testing and message alignment, plus a copy-paste prompt you can use.

Hank Azarian
Abstract visualization of AI personas and scenario branches.
Level up from persona profile → world model → dialog simulation → scenario testing.

Building dynamic world models is one of my favorite uses of AI as a marketer. It lets me combine scenario testing with message alignment — and tune both to my specific goals. If you try similar approaches, I'd love to hear your results.

High-Level Approach

  1. Seed it with the Persona Profile. Start with demographics, values, media habits, and communication style.
  2. Tell it to Build a World Model. The AI constructs an internal JSON model with beliefs, triggers, criteria, tone, and objections.
  3. Instruct it to Simulate Dialog Turns. Play both roles: marketer pitches, persona responds per the world model. After each turn, the JSON state updates.
  4. Add Scenario Testing. Run variations (different prices, offers, channels) to pressure-test assumptions before you ship.

Copy-Paste Prompt (Starter)

Drop this into your favorite LLM as a starting point and adapt as needed.

[System]
You are an AI facilitator running a persona world-model simulation.

[User]
Persona Profile:
 • Age: 32, urban professional
 • Values: sustainability, convenience, data
 • Media habits: 45 min IG, 25 min LinkedIn
 • Communication style: informal + data-driven

Task:
1. Build an internal JSON "world model" with beliefs, triggers, criteria, tone, objections.
2. Play two roles:
   – Marketer: pitches a sustainable watch brand funnel.
   – Persona (Alex): responds per your world model.
3. After each pitch, update the JSON state.
4. Conduct 5 rounds of dialog.
5. At the end, output the final JSON and a 3-sentence summary of Alex's mindset.

Then:
• Run two scenario tests (price at $299 vs $199) and compare outcomes.

Begin now.

Why this works

  • Grounded state: Forcing an internal JSON state reduces drift and makes changes explicit.
  • Dual-role dialog: Alternating marketer ↔ persona exposes message resonance fast.
  • Scenario forks: Price/offer variations pressure-test assumptions before you ship.

Tips for Better Results

  • Personas: Add motivations, constraints, and an explicit "jobs-to-be-done."
  • Evidence: Ask the model to cite which belief or trigger drove each response.
  • Noise: Insert "day-in-the-life" distractions to keep responses realistic.
  • Metrics: Have it score each turn on clarity, trust, and intent to act.
  • Boundaries: Cap token length per turn to keep outputs crisp and comparable.

If you run your own test (e.g., $299 vs $199), share what surprised you most — I'm collecting interesting patterns across categories.