Use case · Discover & Design · Flagship
Turn permissioned customer feedback into a tested problem list
For businesses deciding what to offer or improve next: AI groups your existing reviews, emails and call notes into named problems, each linked to the original quote — then you test one with a real priced offer.
Baseline
Decisions about what to sell next run on instinct and the loudest recent customer. Count the feedback you already hold: reviews, emails, call notes from the last 6 months.
Sample output
Problem list: “Scheduling uncertainty” — 9 distinct customers, 14 quotes linked; “Price transparency” — 4 customers, 5 quotes linked; one flagged single-source complaint.
Illustrative example. Not a live customer result.
Inputs
- Reviews, feedback emails and call notes you have permission to analyse
- Transcripts only where consent covers this use
- Your current service list and prices
What AI prepares
- Group feedback into named problems with a count of distinct customers per problem
- Link every problem to the original quotes that support it
- Flag problems supported by only one loud customer
A person approves
- A person checks for sample bias before acting
- A person decides pricing and whether to invest
- No customer quote is paraphrased into words they did not say