Customer-Language Grammarly
Turn customer conversations into guardrails that rewrite content in buyers' words.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 7
- Confidence
- 99%
Customer-Language Grammarly converts raw customer evidence into a reusable writing and review system. The team transcribes sales calls, compiles them into a corpus, and separates calls that produced customers from those that ended without a purchase. AI categorizes recurring themes, problems, phrases, and tonal patterns, producing a customer-language guardrail document. That document becomes the reference material for a custom GPT or similar tool instructed to correct uploaded content so it matches how customers actually speak. The repository can then expand beyond sales calls to support tickets, social mentions, product reviews, and trusted review sites. The output is a scalable language layer for prospecting emails, marketing copy, sales decks, and other customer-facing content.
Origin
Kieran Flanagan proposed the method on Marketing Against The Grain after connecting Claude Hopkins's customer-language principle with AI categorization and custom GPTs.
Core principles
- 01Effective marketing speaks in the language customers naturally use.
- 02Sales conversations provide high-signal language about problems, objections, and outcomes.
- 03Won and lost deals should be compared rather than blended indiscriminately.
- 04A reusable language repository can govern many types of content.
- 05The repository improves as additional customer evidence is incorporated.
How to run it
- 1
Collect customer speech
Gather a representative set of sales-call recordings or transcripts that can lawfully be analyzed.
Pro tip Include different customer types, sellers, deal sizes, and outcomes.
Watch out Obtain appropriate consent and protect sensitive information.
- 2
Segment outcomes
Separate closed-won conversations from closed-lost conversations so similarities and differences remain visible.
Pro tip Add metadata for buyer role, market segment, and deal stage.
Watch out Blending all calls can conceal language associated with success or rejection.
- 3
Compile the corpus
Transcribe recordings and combine the resulting text into a structured body of customer conversations.
Pro tip Retain speaker labels and source references for validation.
Watch out ASR errors can distort repeated terminology.
- 4
Extract language patterns
Use AI to categorize key themes, categories, exact phrases, objections, and common tone-of-voice characteristics.
Pro tip Require exact excerpts alongside every inferred pattern.
Watch out Do not let summaries replace the customers' original wording.
- 5
Create guardrails
Turn recurring evidence into guidance covering preferred vocabulary, customer-stated problems, desired outcomes, tone, and prohibited internal jargon.
Pro tip Separate strong universal patterns from segment-specific language.
Watch out Overly rigid guardrails can erase necessary variation between audiences.
- 6
Build the language reviewer
Upload the guardrails to a custom GPT or equivalent tool and instruct it to revise content against the evidence.
Pro tip Ask it to explain each material rewrite by citing the relevant guardrail.
Watch out Human review remains necessary for claims, nuance, and brand judgment.
- 7
Apply and expand
Review prospecting emails, marketing content, and sales enablement materials, then add support tickets, social mentions, and product reviews to the repository.
Pro tip Version the repository and evaluate content performance after updates.
Watch out New data should be quality-checked before it changes the canonical language guidance.
In the wild
A team compiles won and lost sales calls, extracts the phrases customers use for their main problem, and uploads the resulting guardrails to a custom GPT. A product-focused prospecting email is then rewritten to use the buyer's vocabulary and emphasize the themes recurring in successful calls.
→ The email sounds more customer-focused and less like internal product marketing.
Common mistakes
Using only successful calls
Won deals reveal resonant language, but lost deals also expose objections, confusion, and phrases that fail to convert.
Summarizing away exact language
The system becomes generic when AI abstractions replace the words customers actually used.
Treating the corpus as timeless
Customer language changes with products, markets, and competitors, so the repository requires ongoing evidence.
Is it for you?
Best for
Companies with enough recorded customer conversations or other voice-of-customer material to reveal recurring patterns.
Not ideal for
Businesses with a tiny, unrepresentative corpus or no lawful permission to process recorded conversations.
From the transcript
“Compile all your transcriptions together. So you get like one document, you transcribe all your sales calls, you put all the sales calls into a…”
“And so you can actually use Chat GBT then to create like a customer language guardrails based upon those sales calls, right?”
“You can upload that doc and say hey this is like my customer language please correct all content that I upload here to match that…”
From the episode
How To Master Sales Prospecting With Ai In 2024