Customer Language Style-Guide Simulator
Turn customer conversations into an AI persona for message development
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 6
- Confidence
- 88%
This workflow transforms unstructured voice-of-customer evidence into a reusable AI customer simulator. The team supplies customer calls, emails, support tickets, and related language to an AI system, which organizes recurring vocabulary, pains, objections, and reasoning into a style guide representing the customer. That guide is then loaded into an interactive AI workspace so marketers can ask how the represented customer might perceive a proposed product, describe a pain, or react to positioning. Useful statements from the dialogue become candidate ads and copy variations. The simulator accelerates synthesis and ideation, but it does not replace reality: generated language should be checked against original source material and validated through customer interviews or behavioral advertising tests.
Origin
The Marketing Against The Grain hosts extended Ryan Deiss’s AI-assisted problem research into a repeatable workflow using calls, emails, support tickets, style guides, simulated dialogue, and ad testing.
Core principles
- 01Unstructured customer evidence can become a reusable language model
- 02A customer style guide should reflect observed language rather than brand preference
- 03Simulated dialogue accelerates pain-point and message exploration
- 04Generated messages still require live-market validation
How to run it
- 1
Collect raw customer language
Assemble transcripts, emails, support tickets, surveys, and other unstructured evidence of how customers speak.
Pro tip Include both successful customers and frustrated prospects when possible.
Watch out Remove sensitive or unnecessary personal information before processing.
- 2
Extract the language structure
Use AI to identify recurring vocabulary, pain points, objections, desired outcomes, and communication patterns.
Pro tip Require the model to distinguish direct evidence from inference.
Watch out Do not allow a polished summary to erase unusual but meaningful customer phrases.
- 3
Create the style guide
Organize the findings into a reusable guide that represents the customer’s language and perspective.
Pro tip Include example phrases and topics the persona would not naturally use.
Watch out A brand voice guide and a customer language guide serve different purposes.
- 4
Simulate customer dialogue
Load the guide into an AI session and ask the represented customer about pains, proposed products, and possible solutions.
Pro tip Ask follow-up questions that challenge vague or convenient answers.
Watch out The simulation is a hypothesis generator, not an actual customer.
- 5
Generate message candidates
Convert the most promising language from the dialogue into concise ad and copy variations.
Pro tip Retain the customer’s wording where it remains accurate and natural.
- 6
Validate in the market
Test candidates with real customers or behavioral ad experiments and retain only supported language.
Pro tip Feed validated and rejected messages back into the guide.
Watch out Never present simulated preference as proven demand.
In the wild
A SaaS team feeds customer-call transcripts, cancellation emails, and support tickets into an AI system. It creates a language guide, uses the guide to question a simulated customer about a planned feature, and converts the strongest responses into plain-language ad candidates.
→ The team reaches testable customer-centered messages faster while preserving a validation step with real prospects.
Common mistakes
Treating simulation as validation
An AI persona can synthesize patterns and generate hypotheses, but it cannot prove that real customers will respond.
Training on brand copy alone
A simulator built from internal marketing language will reproduce the company’s assumptions rather than the customer’s perspective.
Is it for you?
Best for
Teams with substantial customer conversations but no efficient way to reuse their language during campaign development.
Not ideal for
Startups with no reliable customer evidence that treat an invented AI persona as validated market truth.
From the transcript
“AI can take all of your unstructured data, whether that's your customer calls, your customer emails, your support tickets, all of these different things and…”
“you can basically upload that style guide to get uh a back and forth dialogue as if that's your customer”
“I can just go then test that language for my target audience based upon impressions.”
From the episode
Revealing the 2024 Marketing Strategy of a $200M Founder