Customer Language Grammar
Turn customer conversations into an AI-powered copy quality system
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 7
- Confidence
- 96%
The Customer Language Grammar converts raw conversations into a reusable standard for evaluating marketing copy. Begin by transcribing sales calls and other customer interactions, then use AI to identify repeated phrases, benefits, objections, and thematic categories. Store those findings as a customer communications library and configure a custom GPT to compare new landing pages, emails, and sales materials against it. The system can maintain separate corpora for closed-won and closed-lost deals, helping marketers emphasize language correlated with purchases and avoid patterns associated with rejection. As support tickets, website feedback, and social mentions are added, the grammar becomes a continuously improving quality-control layer that makes brand communication sound more like the customer and less like internal marketing jargon.
Origin
Extracted from Marketing Against The Grain, where Kieran Flanagan proposed using AI to build a Grammarly-like system based on customers' actual language.
Core principles
- 01Use the language customers naturally use
- 02Treat conversations as structured marketing data
- 03Distill large volumes of feedback into recurring themes
- 04Check every asset against evidence from customers
- 05Separate language associated with won and lost deals
How to run it
- 1
Collect customer conversations
Gather sales calls, interviews, support exchanges, reviews, and social mentions that contain customers' unfiltered descriptions of their problems and outcomes.
Pro tip Start with closed-won sales calls because their language is connected to an observed purchase decision.
Watch out Do not rely only on copy already written by the marketing team.
- 2
Transcribe the source material
Convert calls and recordings into searchable text with a transcription tool such as Otter.ai or an existing sales platform.
Pro tip Preserve enough context to distinguish a customer's words from a salesperson's framing.
Watch out Poor transcription quality can corrupt important product terms and customer phrases.
- 3
Extract themes and categories
Ask AI to group repeated language into themes such as problems, benefits, objections, desired outcomes, and decision triggers.
Pro tip Retain representative verbatim phrases under every generated theme.
Watch out Do not accept categories that cannot be traced back to source material.
- 4
Separate outcome-based corpora
Create distinct summaries for closed-won and closed-lost deals, then compare their linguistic commonalities and differences.
Pro tip Look for phrases that recur disproportionately in successful deals.
Watch out Correlation in language does not prove that a phrase caused the sale.
- 5
Configure the copy checker
Load the language themes and source examples into a custom GPT instructed to flag deviations and recommend more resonant alternatives.
Pro tip Require the GPT to explain which stored theme supports each suggestion.
Watch out Do not let the checker flatten every asset into one uniform tone.
- 6
Review live marketing assets
Submit landing pages, emails, and sales copy to the checker and revise language that conflicts with customer evidence.
Pro tip Prioritize headlines, benefit statements, and calls to action first.
Watch out Keep human review in the loop for brand, legal, and strategic judgments.
- 7
Refresh the grammar
Periodically add new calls, tickets, and mentions so the system reflects current customer language and market conditions.
Pro tip Track when themes were last updated and which sources produced them.
Watch out A stale library can reinforce language customers no longer use.
In the wild
A B2B software company transcribes its recent sales calls and finds that buyers repeatedly describe the product as reducing handoff delays. Its homepage instead emphasizes an abstract orchestration platform. The team loads the call themes into a custom GPT, checks the page, and rewrites the headline around eliminating handoff delays while preserving the brand's voice.
→ The landing page expresses a concrete benefit in language already used by successful buyers.
A marketing team creates separate language libraries for closed-won and closed-lost opportunities. Its checker discovers that successful buyers discuss implementation speed, while lost prospects focus on integration uncertainty. The next nurture sequence foregrounds onboarding speed and directly answers integration concerns.
→ The campaign incorporates purchase-associated language while addressing a recurring objection.
Common mistakes
Using internal jargon as customer evidence
Marketing and product terminology can overwhelm the language customers actually use. Build the corpus primarily from direct customer speech and writing.
Keeping won and lost deals together
A single blended corpus can hide meaningful differences between language associated with purchases and language associated with rejection.
Treating AI themes as unquestionable truth
Generated categories should remain linked to verbatim evidence and be reviewed by someone who understands the customers and market.
Is it for you?
Best for
Marketing and sales teams with access to calls, support tickets, reviews, or other voice-of-customer data.
Not ideal for
Teams without sufficient customer evidence or those seeking a substitute for direct customer research.
From the transcript
“what about a grammar for your customer language”
“you'll be able to compile those transcriptions and then you'll be able to create a chat chbd prompt to theme the C to create categories…”
“I would pre-program it to basically look for errors in the content that I upload that don't fit the themes and categories in the way…”
From the episode
I Created An Ai Email Course Using ChatGPT