ClawDeployTurn Scattered Feedback Into Clear Priorities
A step-by-step AI workflow that aggregates product feedback from every channel, categorizes it, and delivers prioritized feature requests.
The Problem
Product feedback arrives via support tickets, app reviews, NPS surveys, social media, and sales calls. Without systematic synthesis, roadmap decisions are driven by whoever's loudest — not by what customers actually need most.
Step-by-Step Workflow
The AI agent collects feedback from all sources into a unified view, normalizing different formats.
Process feedback from these 4 sources: 150 support tickets (CSV), 30 App Store reviews, 45 NPS survey responses, and 20 Twitter mentions. Extract the core request or complaint from each.
Aggregated 245 feedback entries: - Support tickets: 150 (61%) - App Store reviews: 30 (12%) - NPS responses: 45 (18%) - Social media: 20 (8%) Successfully extracted core issue from 238/245 entries (97% extraction rate). 7 entries were too vague to categorize.
Each piece of feedback is categorized by feature area, type (bug/request/praise/complaint), and sentiment.
Categorize all 238 extracted feedback items by feature area and type. Group similar requests together and count unique requestors per theme.
Top themes by unique requestors: 1. **Mobile app stability** (67 requestors) — Bug reports, crashes, slow performance 2. **Slack integration** (43 requestors) — Feature request, persistent demand 3. **Dark mode** (38 requestors) — Feature request, cosmetic 4. **Export to CSV** (29 requestors) — Feature request, workflow blocker 5. **Onboarding clarity** (24 requestors) — Complaint, confusion at step 3 6. **API documentation** (18 requestors) — Complaint, incomplete docs 7. **Team collaboration** (15 requestors) — Feature request, shared workspaces
For the top requests, the agent estimates demand strength by counting unique sources, analyzing user segments, and estimating revenue impact.
For the top 3 feature requests (Slack integration, dark mode, CSV export), quantify demand. How many unique users? What segments? Any churn or revenue signals?
Demand quantification: **Slack integration** (43 unique requestors) - Segments: 78% Team plan, 22% Growth plan - 5 churned users cited this as reason (est. $4,900/yr lost) - 12 trial users mentioned it (est. $8,600/yr potential) - Revenue impact: ~$13,500/yr **Dark mode** (38 unique requestors) - Segments: evenly distributed across all plans - 0 churn attributions - Revenue impact: Minimal direct, but strong satisfaction signal **CSV export** (29 unique requestors) - Segments: 85% Growth plan (power users) - 3 churned users cited this (est. $3,600/yr lost) - Revenue impact: ~$5,400/yr Priority order by revenue impact: Slack > CSV export > Dark mode.
Findings are compiled into a roadmap recommendation with clear priority rationale based on data, not opinions.
Based on all feedback analysis, create a roadmap recommendation for next quarter. Include priority rationale and estimated impact.
**Q2 Roadmap Recommendation (Data-Driven)** 🔴 **P0: Mobile app stability** — 67 reports, active churn risk, satisfaction scores dropping. Fix before new features. 🟡 **P1: Slack integration** — 43 requests, $13.5K/yr revenue impact, 6 weeks of consistent demand. Highest ROI feature. 🟡 **P2: CSV export** — 29 requests from power users, $5.4K/yr impact. Small build, high goodwill. 🟢 **P3: Onboarding redesign** — 24 complaints, correlated with early churn. Already improving per last week's data. 🟢 **P4: Dark mode** — 38 requests but no revenue signal. Good candidate for a hackathon or side project. **Rationale:** Stability first (stop bleeding), then highest-ROI feature (Slack), then quick wins (CSV).
Time Saved
Save 8-12 hours per week of manual feedback reading, tagging, and synthesis. Get data-backed roadmap recommendations instead of opinion-driven prioritization.
Frequently Asked Questions
What feedback formats does the agent process?
CSV exports, plain text, app store reviews (copy-paste), survey responses, and social media mentions. If it's text, the agent can process it.
How does it handle duplicate feedback?
The agent groups similar requests by semantic meaning, not exact wording. "Add Slack" and "Would love Slack integration" count as the same request.
Can it track feedback trends over time?
Yes. Run the workflow weekly and the agent compares against previous periods, showing which themes are growing, declining, or newly emerging.
Does it account for user segment when prioritizing?
Yes. The agent cross-references feedback with user plan type and usage level, weighting requests from higher-value segments appropriately.