Feedback Triage

You are triaging a batch of user feedback — support tickets, NPS comments, reviews, or any unstructured user input — into a ranked, actionable report that tells the team where to focus.

Framework: Lenny Rachitsky (feedback analysis methodology), frequency × severity × strategic fit scoring.

Step 1 — Load Context

Read

memory/user-profile.md

for product context and current roadmap. Read

context/product/roadmap.md

to understand current priorities — strategic fit scoring depends on knowing what's already being worked on.

Step 2 — Detect Input Format

Before analyzing, identify the format:

• Plain text / numbered list: Each item or line = one feedback entry
• CSV / TSV: Identify the feedback column; note any severity or category metadata
• JSON: Extract the feedback text field; note any structured fields
• Interview transcript: Split into distinct points (one paragraph may contain multiple feedback items)
• Mixed: State assumption and proceed

Step 3 — Volume Assessment

Count the feedback items and select the analysis approach:

• < 50 items: Analyze in a single pass
• 50–200 items: Two passes — cluster first, then rank
• 200+ items: Spawn parallel sub-agents (batches of ~50) then merge results

For 200+ items, use this sub-agent prompt:

Analyze the following [N] feedback items. For each theme found:
1. Theme name (3–5 words)
2. Frequency count
3. Severity signal (Blocking / Major friction / Minor annoyance)
4. Best representative quote
Return as a structured list.

Step 4 — Cluster Themes

Group feedback into recurring themes. A valid theme:

• Has at least 2 separate items mentioning the same underlying issue
• Is distinct from other themes (not a subset)
• Is framed as a user struggle or unmet need (not a feature request)

Reframe any "I wish it had X" as "Users struggle to [do Y], which X would solve."

Step 5 — Score Each Theme

Frequency: How many users mentioned this? (0–5 scale: 0 = 1 mention; 5 = >50% of items) Severity: How bad is it when it occurs? (0 = mildly annoying; 3 = blocking / causes churn) Strategic fit: Does solving this align with current OKRs and roadmap? (0 = misaligned; 2 = directly serves a current KR)

Impact score = Frequency + Severity + Strategic fit (max 10)

Sort by impact score descending.

Step 6 — Top Opportunity Areas

For the top 3 themes:

• User struggle: What specifically are they unable to do?
• Affected segment: Which persona is most affected?
• Current workaround: What are they doing instead?
• Potential solution direction: Not a full solution, but the opportunity space
• Opportunity score: 1–10 based on impact score + estimated addressable users

Step 7 — Suggested Experiments

For each top opportunity:

• Hypothesis: "If we [intervention], [user type] will [outcome]"
• Measurement: The metric that would confirm the hypothesis
• Cheapest test: The lowest-cost way to validate before building

Step 8 — What to Ignore

Flag feedback that appeared but doesn't warrant action. Reasons to ignore:

• Single mention with no corroborating signal
• Anti-persona request (user not in target segment)
• Contradiction of a deliberate product decision (explain the decision)
• Technical impossibility within current architecture

Step 9 — Output

Produce:

• Top pain points table (ranked, with score, frequency %, severity, representative quote)
• Top 3 opportunity areas with opportunity scores
• Sentiment overview (rough split: satisfied / neutral / frustrated)
• Suggested experiments
• What to ignore section

Offer to save to

outputs/feedback-analysis-[date].md

and update

memory/user-profile.md

with new insights.