# Feedback Triage

> Cluster + score a raw pile of customer feedback (200+ items from Intercom, Zendesk, sales notes, support tickets, NPS comments) into ranked themes with a composite Volume × Severity × Strategic Fit × Customer Weight priority score. Scoring sub-scores stay visible so reviewers can challenge specific dimensions, not just the final number. Use instead of basic feature-request analyzers when you have multi-source feedback and need composite priority scoring with visible sub-scores, not just frequency counts. Triggers on phrases like "triage this feedback", "cluster these requests", "themes in this feedback", "synthesize Intercom export", "make sense of NPS dump", "quarterly feedback review", "process sales-team feedback", "разобрать обратную связь", "темы в фидбеке".

- Skill: `euroconic/feedback-triage` (Agent Skill)
- Install (CLI): `npx skillmds@latest add euroconic/feedback-triage`
- Raw SKILL.md: https://api.skillmd.com/api/skills/euroconic/feedback-triage/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: euroconic (https://skillmd.com/u/euroconic)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/euroconic/feedback-triage

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# Feedback Triage

## Purpose

PMs drown in feedback. 200 Intercom tickets, 50 sales call notes, 80 NPS comments, a Slack channel of complaints. Manual triage takes a day and you still miss patterns.

This skill turns a raw dump into a ranked, themed report with enough structure to drive prioritization decisions and enough quotes to defend them.

The output is opinionated: it ranks themes by a composite score, not just frequency. A theme mentioned once by a strategic enterprise customer can outweigh 30 generic requests.

## When to use

- Quarterly feedback review
- Post-launch: triage the inbound noise
- Onboarding: get a fast read on what customers actually want
- Pre-roadmap-planning: ground the strategy in real evidence
- Customer-success or sales-team feedback synthesis

## When NOT to use

- For 5-10 feedback items - just read them
- For a single critical incident - dedicated triage, not statistical
- For real-time monitoring (use alerting tools)

## Inputs

Required:
- Feedback items: paste, file (CSV / JSON / markdown), or path

Strongly improves quality:
- Customer metadata (segment, plan tier, ARR, account name) per item if available
- Business priorities or strategic themes the PM cares about
- Time window the feedback covers

Default assumption: every item is one unit unless metadata says otherwise. With metadata, items can be weighted.

## Process

### Phase 1: Normalize

- Deduplicate near-identical items
- Standardize format (each item: id, text, source, date, customer metadata if any)
- Flag items that are not feedback (sales pitches, internal notes, irrelevant chatter) and exclude from clustering
- Report normalization stats: original count, deduped count, excluded count

### Phase 2: Theme extraction

Cluster items by underlying job-to-be-done, not surface words. "I need an export to CSV" and "give me a way to get this data into Excel" are the same theme.

For each theme:
- Theme name (concrete, not generic - "Bulk edit roles in user admin" not "Better UX")
- Underlying JTBD ("When I'm onboarding a new team, I want to assign roles in bulk, so I can finish setup faster")
- Item count
- Sample quotes (3, diverse)

Aim for 8-15 themes. More than 20 means theming is too granular.

### Phase 3: Score each theme

Four sub-scores (1-5 each):
- **Volume**: how many items, normalized
- **Severity**: how blocking is this for affected users
- **Strategic fit**: does this align with stated priorities
- **Customer weight**: are these high-value accounts (uses metadata if provided)

Composite priority = average of the four (1-5).

Report each theme with all four sub-scores plus composite. Do not hide the inputs - reviewers will want to challenge specific dimensions.

### Phase 4: Sentiment slice

For each theme:
- % positive / neutral / negative
- Tone shift over time if time-window data available
- Flag themes where sentiment is sharply negative (escalation candidates)

### Phase 5: Surprises and orphans

- **Surprises**: themes the PM probably didn't expect (compare against stated priorities)
- **Orphans**: items that didn't cluster - sometimes these are the most interesting (early signals)
- **Conflicts**: where customer segments want opposite things

## Output

```
# Feedback Triage Report

## Stats: original / deduped / excluded
## Top 5 themes (executive summary)

## Full theme table
| # | Theme | JTBD | Items | Volume | Severity | Fit | Weight | Composite | Sentiment |

## Detailed theme breakdowns
[For each theme: name, JTBD, sample quotes, scoring rationale]

## Surprises
## Orphans worth attention
## Conflicts across segments
## Recommended next actions (3)
```

The output should be paste-into-Notion ready. Tables, not paragraphs, for the structured data.

## Common failure modes to avoid

- **Theme inflation**: 30 themes is not analysis, it's transcription
- **Generic theme names**: "Better mobile experience" is not actionable. Be specific.
- **Hiding scoring inputs**: if you only show composite, reviewers can't challenge it
- **Ignoring metadata**: feedback from 5 enterprise customers worth $2M ARR is not the same weight as 50 free-tier users
- **Pretending the data is clean**: report the noise honestly. PMs need to know how much they're trusting.

## Tools strategy

If the input is a structured file (CSV, JSON), parse it programmatically. If pasted text, normalize first, then process. For 500+ items, batch the clustering and consolidate.