Customer Language Bank
What this does
Reads your voice-of-customer material in bulk and returns the language buyers actually use - the words for their problem, the phrases they use for the outcome they want, the terms they use for you and for alternatives. It groups this into themes ranked by how often each shows up, then holds it up against your current messaging to show where your words and their words have parted ways.
Copy written in the team's internal language quietly loses buyers who describe their world differently. This gives you their vocabulary, weighted by frequency, so the messaging can meet them where they are.
Why this is a Fable-recommended job
The signal is in the volume - a phrase matters because many customers use it, and you only see that by reading a lot of them together. Claude Fable 5 can take hundreds of reviews, tickets, and snippets in a single run and rank the recurring language, which a sample of a dozen would miss.
If Fable is not available or the budget is spent: run in batches of a few hundred items, produce a themed phrase list per batch, then merge and re-rank the lists in a final pass.
Evidence to gather first
- Product reviews (G2, Capterra, app stores, Trustpilot)
- Support tickets and chat logs
- Sales and onboarding call snippets where customers describe their problem or results
- Community posts, survey free-text, churn and cancellation reasons
- Social mentions, if available
Read .agents/product-marketing-context.md first if it exists, so the language
can be checked against your positioning and personas.
Process
- Confirm the sources and roughly how many items are included. The report will state the volume.
- Read all of it. Pull the recurring language for: the problem, the trigger to look for a solution, the desired outcome, how they describe your product, and how they describe alternatives.
- Cluster into themes and count frequency. Keep the raw phrasings as examples.
- Compare the top themes against your current messaging. Note where you use a different word than the market does for the same thing.
- Produce the language bank.
Output format
- Summary - items analyzed, sources, the dominant themes.
- Language table - theme | what buyers say (real phrases with source) | how often it appears | the word your messaging currently uses | gap.
- Swap list - specific places your copy could adopt the buyer's word.
- Persona signals - differences in language by segment or role, if the material supports splitting them out.
The evidence standard
Every phrase in the bank must be a real quote with its source noted (review, ticket, call), not a paraphrase or an invented "customers say" line. Frequency counts must come from the material; if you can only estimate, label it an estimate. Do not clean up buyer language into marketing language - the unpolished wording is the whole point.
