Measure how customers react to a launch
For when you shipped last Tuesday and the PM is asking 'so, did customers like it?' — and all you have is a hunch.
The Challenge
No feedback loop after launch
Usage metrics show adoption numbers but not why people like or dislike the feature. You're flying blind on reception.
Scattered signals
Reactions come through support tickets, app reviews, NPS verbatims, and social posts. Nobody aggregates them in time to act.
Slow iteration cycles
By the time a post-launch survey comes back, 3 weeks have passed. Quick-turn feedback would let you iterate in days, not months.
How teams use Enterpret today
Situation
After launching a redesigned onboarding flow, the product team couldn't tell if 65% completion was good or bad from usage metrics alone.
Action - asked Wisdom, Enterpret's AI assistant
What are the top feedback themes mentioning onboarding or setup since our launch date? Show sentiment split and volume trend.
Impact
Within 72 hours, surfaced a 4x spike in 'confused by new layout' feedback — a regression the team patched before it hit NPS.
Situation
A product marketing team needed to understand how a pricing change was received across segments before the board update.
Action - prompted Claude with Enterpret MCP connector
Use the Enterpret Wisdom connector to compare sentiment about our pricing change between enterprise and SMB customers since the announcement date. Show the top themes for each segment.
Impact
Enterprise was mostly neutral; SMB was frustrated about losing a free-tier feature. The team added a migration path for affected users.
Situation
A PM launched an AI-powered feature and wanted to track reception over the first month — not just day-1 reactions.
Action -
Action — Configured an Enterpret Quality Monitor agent in Slack to track weekly launch sentiment
Alert me every week with the trend of feedback themes mentioning our AI feature. Show whether sentiment is improving or declining week over week.
Impact
Week 1 was mostly complaints. By week 3, after two iterations, positive feedback overtook negative — giving evidence that iteration was working.
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