Quantify customer demand behind feature requests
For when your roadmap debate has 6 opinions and zero data — and you need to show which requests carry the most revenue.
How teams use Enterpret today
Situation
A PM needed to justify moving "bulk export" up the Q3 roadmap. Sales insisted it was critical; support said it was rare. Nobody had the data to settle it.
Action — asked Enterpret AI
How many accounts have mentioned bulk export or data export in the last 6 months? Break down by segment and total ARR.
Impact
Wisdom surfaced 47 accounts ($2.1M ARR) requesting bulk export, 80% enterprise. The feature moved from P3 to P1 and was funded for Q3.
Situation
A project manager was writing a PRD for a frequently requested feature but needed customer evidence AND context from the engineering backlog.
Action — prompted Claude with Enterpret MCP connector
Built a PRD generator that pulls customer demand data from Enterpret MCP and related tickets from Jira or Linear to create evidence-backed first drafts.
Impact
The PRD generator pulled demand data from Enterpret and cross-referenced with 14 open Jira or Linear issues. Found 3 customer-reported needs with no matching ticket. What used to take a day of research was done in one prompt.
Situation
After quantifying demand, the PM needed the evidence attached to the actual roadmap items — not in a separate report that gets forgotten.
Action — linked feature request themes to Jira or Linear work items
Linked the top feature request themes to their corresponding Jira or Linear issues. Each ticket received the requesting account count, total ARR, and verbatim customer quotes automatically.
Impact
During sprint planning, engineers could see "47 accounts, $2.1M ARR" directly on the Jira or Linear ticket. Prioritization shifted from opinion to evidence.
