pm-feedback-synthesis

name	pm-feedback-synthesis
description	Use when you have raw user feedback from multiple sources (interviews, surveys, tickets, reviews) and need to extract themes, patterns, and actionable insights

PM Feedback Synthesis

Transform raw, unstructured user feedback from multiple sources into structured, actionable product insights. Cluster themes, extract evidence, and link findings to opportunities on your Opportunity Solution Tree.

Core principle: PMs have data goldmines but no time to mine them. This skill turns scattered feedback into a single source of truth.

Announce at start: "I'm using the pm-feedback-synthesis skill to synthesize user feedback."

When to Use

• You have interview transcripts, survey responses, support tickets, or reviews to process
• You need to identify patterns across multiple feedback sources
• You're preparing for roadmap or prioritization decisions
• You want to update your OST with fresh evidence
• Stakeholders are asking "what are users saying?"

The Process

Step 1: Gather Sources

List all feedback sources available. Common sources:

Source	Typical Format	What to Extract
User interviews	Transcripts or notes	Pain points, desires, JTBD, direct quotes
Support tickets	Ticket export	Recurring issues, friction points, feature requests
App store reviews	Review text + rating	Satisfaction drivers, churn signals, competitive mentions
NPS surveys	Scores + open comments	Detractor reasons, promoter praise
Sales call notes	CRM notes	Objections, competitive comparisons, buying triggers
Community/social	Reddit, Slack, Discord	Unfiltered opinions, workarounds, wishlists
Analytics drop-offs	Funnel data	Where users struggle (quantitative, needs qualitative pairing)

Step 2: Ingest and Cluster

For each source, extract and cluster:

1. Extract meaningful statements — Ignore generic praise/complaints. Focus on specific observations: "I couldn't find the export button" not "the UI is bad"

2. Tag each statement with:

   • Theme: Onboarding, Performance, Collaboration, Pricing, etc.
   • Sentiment: Pain point, Desire, Praise, Confusion, Workaround
   • Frequency: How many users mentioned this?
   • Persona/segment: Which user type?

3. Cluster related statements — Group statements within each theme that point to the same underlying need

Step 3: Produce Structured Output

# Feedback Synthesis — [Date]

## Top Themes (by frequency and impact)

### Theme 1: [Name] (N mentions, X% of users)
**What users are saying:**
- "[Direct quote]" — User persona, source
- "[Direct quote]" — User persona, source

**Root cause:** [What's actually broken or missing?]
**Opportunity:** [What job is the user trying to do?]
**Link to OST:** [Which opportunity on the tree does this connect to?]

### Theme 2: [Name]
...

## Emerging Signals (low volume but interesting)
- [Signal] — only 2-3 mentions but suggests a new pattern

## What Changed Since Last Synthesis
- [Theme X] mentions increased 3x since last month
- [Theme Y] was top-3 last time, now barely mentioned (likely fixed)

## Recommendations
1. [Actionable recommendation tied to a theme]
2. [Actionable recommendation]
3. [Question for further investigation]

Step 4: Connect to Product Process

Route insights to the right skill:

• New opportunities → Add to continuous-discovery OST
• Validation needed → Feed into product-discovery interviews
• Clear feature request → Evaluate in prioritization
• Bug/support issue → Route to engineering queue
• Competitive signal → Feed into competitive-analysis

When to Run This

• Weekly: Light pass — what's new in support tickets and NPS?
• Monthly: Deep synthesis — all sources, full theme clustering
• Post-launch: 7-day and 30-day feedback pulse
• Pre-roadmap: Comprehensive synthesis to inform prioritization

Common Mistakes

Mixing sources without weighting: A power user's complaint ≠ every user's experience. Always note frequency and segment.

Confirmation bias: Only seeing themes that support your existing beliefs. Read the full dataset before forming conclusions.

Over-aggregating: "Users want better UX" is useless. "5 of 12 interviewees couldn't find the export function because it's hidden in a dropdown" is actionable.

Synthesis without action: Insights that don't connect to product decisions (OST update, roadmap change, interview topic) are wasted effort.

Treating signals as certainties: "3 users mentioned X" is a signal to investigate, not a mandate to build. Distinguish between evidence strength levels.

Red Flags

Never:

• Synthesize without linking to sources ("users want X" — which users? where? when?)
• Present frequency without denominator ("15 users complained" — out of how many?)
• Ignore feedback that contradicts your roadmap
• Skip the "what changed" comparison from last synthesis
• Let insights sit in a doc — route them to the right skill

Integration

Feeds into:

• continuous-discovery — New opportunities for the OST
• product-discovery — Hypotheses to validate
• prioritization — Evidence for scoring decisions
• competitive-analysis — Competitive signals from user mentions

Fed by:

• product-discovery — Interview transcripts to synthesize
• launch-planning — Post-launch feedback collection

Key References

• Teresa Torres, "Continuous Discovery Habits" (connecting feedback to OSTs)
• "The Mom Test" by Rob Fitzpatrick (distinguishing signal from noise)
• Dovetail, Productboard, and similar research repositories