Persona-Simulated Message Test
Test marketing against an AI modeled on the exact buyer persona
- Difficulty
- Advanced
- Time to result
- ~weeks to results
- Steps
- 4
- Confidence
- 90%
Create or configure an AI assistant to represent a narrowly defined buyer, such as a vice president of sales at a mid-sized SaaS company. Ground the simulation in research describing the person's responsibilities, concerns, vocabulary, buying criteria, and desired outcomes. Give it the real marketing asset and explain how the product is intended to help. Ask it to identify what resonates, what does not, and why, then use those reactions to revise the message. The method improves on generic AI critique by introducing an explicit audience model and consistent evaluation lens. However, the assistant is a synthetic reviewer, not a real customer. Its output should generate hypotheses and accelerate iteration before marketers validate consequential conclusions through interviews, experiments, sales conversations, or observed behavior.
Origin
Extracted from Marketing Against The Grain as Kieran Flanagan's proposed B2B version of character.ai for persona-specific marketing feedback.
Core principles
- 01Evaluate messaging through the buyer's priorities, not the marketer's preferences
- 02Train the simulation on specific persona evidence
- 03Ask what resonates and what fails separately
- 04Tie criticism to the product's promised customer outcome
- 05Treat simulated reactions as hypotheses, not customer proof
How to run it
- 1
Define the simulated buyer
Specify role, company type, scale, goals, pains, buying authority, and relevant product category.
Pro tip Build separate simulations for users, champions, and economic buyers.
Watch out A broad persona produces generic feedback.
- 2
Ground the persona
Supply interviews, research reports, sales notes, and behavioral evidence that describe how this buyer thinks and decides.
Pro tip Prioritize first-party customer language.
Watch out Ungrounded simulations reproduce stereotypes and model assumptions.
- 3
Run a structured critique
Provide the asset and ask what resonates, what fails, what is unclear, and what would increase confidence or action.
Pro tip Require the persona to connect every reaction to a stated priority.
Watch out Do not ask only whether the persona likes the page.
- 4
Revise and validate
Update the asset based on plausible findings, then test the most important hypotheses with real buyers or behavioral data.
Pro tip Track whether simulated objections also appear in customer conversations.
Watch out Synthetic feedback is not market validation.
In the wild
Kieran proposed a VP of sales bot trained to understand how the product helps that role. A marketer could submit a page and ask which sections resonate with a VP of sales and which do not, producing persona-specific revision ideas.
→ A generic AI website review becomes a repeatable simulation focused on one buyer's priorities.
Common mistakes
Using an untrained persona
Merely telling a model to act like a buyer encourages confident stereotypes instead of evidence-grounded reactions.
Confusing simulation with validation
The assistant cannot establish what real buyers will actually do.
Combining incompatible buyers
Blending several roles into one synthetic persona obscures conflicting needs and buying criteria.
Is it for you?
Best for
Teams with strong persona research that need rapid directional feedback on webpages, campaigns, and product positioning.
Not ideal for
Teams lacking reliable customer evidence or attempting to replace real interviews and behavioral testing entirely.
From the transcript
“what would make it 10x is if you had an assistant trained to act like the persona that you sell to”
“what parts of this page resonate to you because our software helps VP of sales in this way, and what parts of the page does…”
“those B2B assistants can help you actually make your marketing completely on point for the persona that you are trying to attract, engage, and acquire.”
From the episode
6 AI Growth Hacks Top Marketers Don't Want You To Know (#167)