pisithrps/user-research-synthesis

/user-research-synthesis - Turn Interview Data Into Insights

When the PM types /user-research-synthesis, transform raw user interview notes, transcripts, and observations into actionable product insights.

Context Routing Logic (Internal - for Claude)

Automatic Context Checks: When this skill is invoked, immediately check:

| Source | Files/Folders | Search Terms | What to Extract | |--------|---------------|--------------|-----------------| | Existing Research | context-library/research/*.md | topic from chat, user segments | Previous findings to avoid duplication | | Related PRDs | context-library/prds/*.md | problem related to interviews | Problem framing and hypothesis | | Strategy Context | context-library/strategy/*.md | user segment, strategic fit | How findings ladder to strategy | | Previous Synthesis | outputs/research-synthesis/ | topic name | Past research to build on | | Interview Guides | context-library/research/interview-guides/ | topic | What questions were asked |

Context Priority:

1. Raw interview data FIRST (always use verbatim quotes)
2. Related PRDs and problem statements SECOND
3. Previous research on related topics THIRD
4. Strategic context FOURTH

Cross-Skill Links:

• After synthesis → Link to /prd-draft to turn insights into feature spec
• If about competitor mentions → Link to /competitor-analysis
• If about retention → Link to /retention-analysis for churn patterns
• If informing strategy → Link to /write-prod-strategy

---

Step 0: Understanding Your Research Context

Before we synthesize, let me understand what you've learned...

Checking:

• context-library/research/ for previous findings on this topic
• context-library/prds/ for the problem statement
• Interview guides used: what were you trying to validate?
• Previous synthesis on related topics

Based on what I find, I'll show you:

What You Were Researching

Topic:

• [What problem or feature were you exploring?]
• [Your original hypothesis: what did you expect to find?]

Sample:

• [Who did you interview? # of participants, roles, segments]
• [Notable characteristics: power users? churn risk? non-users?]

Context from Previous Research:

• [What do we already know about this problem?]
• [What did you expect research to confirm/disprove?]

PM-Specific Diagnosis Questions

1. Research Quality: Are these high-quality interviews with right users, or preliminary conversations?
2. Saturation: How many interviews did you do? (5-8 = good, 3-4 = preliminary, 10+ = very deep)
3. Bias Risk: Are you interviewing customers who love you, or a balanced sample?
4. Surprise Factor: What surprised you? (Best insights often contradict expectations)
5. Action Threshold: Do you have enough evidence to make decisions, or need more research?

---

When to Use

• After completing 5-8 user interviews
• Processing customer feedback from multiple sources
• Synthesizing usability test results
• Analyzing support tickets or sales call notes
• Converting qualitative data into product decisions

---

How It Works

This is a 4-step process:

Step 1: Upload Your Raw Data

Step 2: Extract Key Observations

Step 3: Cluster Into Themes (Affinity Mapping)

Step 4: Generate Actionable Recommendations

---

Step 1: Upload Your Raw Data

When the PM types /user-research-synthesis, start with:

Let's turn your user research into actionable insights.

**What data do you have?**

Upload or paste any combination of:
- Interview transcripts (from Grain, Otter.ai, or manual notes)
- Usability test recordings or notes
- Customer support tickets
- Sales call summaries
- Survey responses (open-ended)
- Slack messages from customer channels

You can upload multiple files or just paste everything into this chat.

**How many interviews/data points?**
[Let me know so I can gauge the scope]

**What were you trying to learn?**
(e.g., "Why users churn after the first week" or "Pain points in the onboarding flow")

What to Look For in the Data

As you upload, I'll automatically start flagging:

• Direct quotes - Verbatim user language (the most powerful stuff)
• Behavioral patterns - What users actually did (not what they said they'd do)
• Pain points - Explicit frustrations or workarounds
• Jobs to be done - What users are trying to accomplish
• Unexpected use cases - How they're using the product in ways you didn't anticipate
• Emotion signals - Moments of frustration, delight, confusion

---

Step 2: Extract Key Observations

Once I have your data, I'll say:

Great, I've reviewed [X] interviews/data points.

I'm going to extract individual observations - each one gets its own "sticky note."

This will take a few minutes. I'll create:
- User quotes (in their exact words)
- Observed behaviors (what they actually did)
- Pain points (explicit problems they mentioned)
- Workarounds (clever hacks they've built)
- Context (their role, goals, environment)

Processing now...

Output Format

I'll create a structured list like this:

## Observation #1
**Type:** Pain Point  
**User:** Marcus (PM, Spotify)  
**Quote:** "I have 47 voice memos on my phone that are just 'remember to follow up with design about X.' I never convert them."  
**Context:** Uses multiple task managers, frustrated with manual entry  
**Emotion:** Frustration (high)

## Observation #2
**Type:** Behavior  
**User:** Priya (PM, Notion)  
**Quote:** "Half my tasks come from casual hallway conversations."  
**Behavior:** Captures ideas verbally but loses them before writing down  
**Context:** Fast-paced startup environment

[... continues for all observations]

Quality Checks

As I extract, I'll flag:

• ⚠️ Vague statements - "Users want a better experience" (not actionable)
• ⚠️ Future predictions - "I would definitely use this" (unreliable)
• ⚠️ Leading question responses - If I detect the interviewer asked a leading question
• ✅ High-quality signals - Specific stories, concrete examples, strong emotion

---

Step 3: Cluster Into Themes (Affinity Mapping)

After extraction, I'll say:

I've extracted [X] observations from your data.

Now I'm going to group these into themes using affinity mapping.

I'll look for:
- Patterns that appear across multiple users
- Contradictions (where users disagree)
- Underlying needs beneath surface-level requests
- Jobs-to-be-done that aren't being solved

Clustering now...

The Clustering Process

I'll create theme clusters like this:

## Theme 1: "Task Capture Friction"
**Frequency:** 6 out of 8 users mentioned this  
**Severity:** High (blocks daily workflow)

**Key Observations:**
- [Observation #1: Marcus voice memos]
- [Observation #2: Priya hallway conversations]
- [Observation #5: Jake loses tasks from Slack]
- [Observation #12: Sarah Excel spreadsheet workaround]

**Pattern:**
Users capture tasks in the moment (voice, text, conversation) but face friction converting them into their task manager. The "structuring" step is the bottleneck.

**Direct Quotes:**
- "I never convert them." - Marcus
- "Then 3 days later I'm like... wait, what API thing?" - Priya

**Jobs-to-be-done:**
When I have a spontaneous task idea, I want to capture it instantly without thinking about structure, so that I don't lose track of commitments.

---

## Theme 2: "Context Loss Between Tools"
**Frequency:** 5 out of 8 users  
**Severity:** Medium

[... continues for each theme]

Handling Contradictions

When users disagree, I'll explicitly call it out:

## 🔴 Contradiction Detected: "Manual Control vs Automation"

**User Group A (3 users):** Want AI to automatically assign tasks
- "Just figure out who it should go to" - Jake
- "I don't want to think about it" - Maria

**User Group B (5 users):** Want full manual control
- "AI might get this wrong" - Priya
- "I need to decide priority myself" - Marcus

**Recommendation:**
Default to manual with an "AI suggestion" that users can accept/reject. This preserves control while reducing friction.

---

Step 4: Generate Actionable Recommendations

After clustering, I'll create your final output:

I've identified [X] major themes from your research.

Now I'll translate these into actionable recommendations with:
- What to build (prioritized)
- What NOT to build (non-goals)
- Open questions (what you still need to learn)
- Success metrics (how to measure if you solved the problem)

Generating recommendations...

Recommendation Format

# User Research Synthesis: [Topic]
**Date:** [Today's date]  
**Interviews:** [X participants]  
**Synthesized by:** [PM name]

---

## Executive Summary

**Top 3 Insights:**
1. [Insight with impact]
2. [Insight with impact]
3. [Insight with impact]

**Recommended Actions:**
1. [Build this first]
2. [Explore this next]
3. [Deprioritize this]

---

## Theme 1: [Theme Name]
**User Impact:** [% of users affected]  
**Severity:** [High/Medium/Low]  
**Current Workaround:** [How users solve this today]

### The Problem
[Describe the core issue in user language]

### Supporting Evidence
- **Direct Quote:** "[Exact user words]" - [User name]
- **Observed Behavior:** [What you saw them do]
- **Frequency:** [X out of Y users mentioned this]

### Recommended Solution
**Build:** [Specific feature/change]

**Why this solution:**
- Addresses the root cause: [explain]
- Fits into existing workflow: [explain]
- Validated by user behavior: [explain]

**What NOT to build:**
- [Alternative you considered but rejected]
- [Why it won't work]

**Success Metrics:**
- Primary: [How you'll measure success]
- Guardrail: [Metric that can't get worse]

**Open Questions:**
- [ ] [What you still need to validate] - @[who to ask]
- [ ] [Edge case to test]

---

[Repeat for each theme]

---

## Themes We're NOT Addressing (And Why)

### Theme X: [Lower priority theme]
**Why we're not prioritizing:**
- Only 2 out of 8 users mentioned it
- Workarounds exist and aren't too painful
- Doesn't align with our Q2 strategy

---

## Contradictions & Open Questions

[List any conflicts in the data]
[List assumptions that need more validation]

---

## Appendix: Raw Observations

[All extracted observations for reference]

---

Advanced Techniques

The Mom Test Check

As I analyze, I'll flag any data that might be unreliable:

❌ Future predictions: "I would definitely use this" ❌ Hypotheticals: "If you built X, I would Y" ❌ Compliments: "This is great!" (without specifics) ❌ Feature requests: "You should add Z" (without context)

✅ Past behaviors: "Last time I tried to do X, I had to Y" ✅ Specific stories: "Here's exactly what happened..." ✅ Observed patterns: "Every single time I do this..." ✅ Workarounds: "Here's my hack for this problem"

Advanced Interview Question Patterns

From Aakash Gupta's Customer Interview Framework:

1. The Time Machine Question

• Ask: "Walk me through the last time you [did this task]. What happened first, then what?"
• Why: Gets specific, grounded stories instead of generalized opinions
• Example: "Walk me through the last time you tried to book a meeting with your team"

2. The Money Question

• Ask: "Have you paid for a solution to this problem? What did you buy?"
• Why: Actual purchasing behavior reveals true pain severity
• Red flag: If they haven't spent money/time, the pain might not be real

3. The Workaround Deep-Dive

• Ask: "Show me how you're solving this today. Can you share your screen?"
• Why: Existing workarounds reveal the real job-to-be-done
• Look for: Complex Excel sheets, manual processes, duct-taped integrations

4. The Switching Cost Question

• Ask: "What would it take for you to stop using [current solution] and switch to something new?"
• Why: Reveals switching barriers and minimum viable features
• Red flag: "Oh I'd switch immediately!" = probably won't

5. The Budget Authority Question

• Ask: "If you wanted to buy a solution tomorrow, what's your process? Who needs to approve?"
• Why: Identifies if you're talking to the decision-maker
• Critical for B2B: Understanding org structure and buying process

6. The Failure Story

• Ask: "Tell me about a time this went wrong. What broke? What did you do?"
• Why: Failure moments reveal pain intensity and consequences
• Example: "When was the last time you missed a deadline because of this issue?"

7. The Champion Question

• Ask: "Who else at your company has this problem? Can you introduce me?"
• Why: Tests if problem is widespread, gets referrals
• Red flag: "Just me, I think" = niche problem

8. The Recent Purchase Question

• Ask: "What other tools/products have you bought in the last 6 months? What made you buy?"
• Why: Reveals buying patterns and decision criteria
• Look for: Procurement process, evaluation criteria, deal-breakers

Five Whys Technique

When users give surface-level answers, dig deeper:

User: "I want better search"
You: "Why do you need better search?"
User: "I can't find my old tasks"
You: "Why do you need to find old tasks?"
User: "Because I reference them for new projects"
You: "Why do you reference old tasks?"
User: "Because I repeat similar patterns"
You: "Why don't you have templates?"
User: "I DO have templates, but they're scattered across 3 tools"

→ Real problem: Template organization, not search

Apply this during synthesis - I'll automatically run "5 Whys" on pain points to find root causes.

The Silent Segments Framework

Who you DIDN'T talk to matters as much as who you did.

After interviews, I'll create a "Missing Voices" section:

## 🚨 Missing Segments (Research Gaps)

**Who we talked to:**
- 8 enterprise PMs (large companies, >1000 employees)
- All technical backgrounds
- All English-speaking, US-based

**Who we DIDN'T talk to:**
- Small company PMs (<100 employees)
- Non-technical PMs (marketing, ops)
- International users (Asia, Europe, LatAm)
- Free tier users (only talked to paid)

**Risks:**
- Our insights may not apply to SMB market
- May miss non-technical user pain points
- International workflows might differ significantly
- Free users might have different needs

**Recommendation:**
- Next round: 5 interviews with SMB PMs
- Test prototypes with non-technical users
- Consider international user research

This prevents building for a narrow segment while claiming broad applicability.

Sentiment Analysis

For each theme, I'll assess emotional intensity:

• 😤 High frustration - Active blocker, considering alternatives
• 😕 Medium pain - Annoying but manageable
• 🤷 Low priority - Nice-to-have, not urgent

This helps prioritize which problems to solve first.

Job-to-be-Done Framing

For each theme, I'll translate into JTBD format:

When [situation],
I want to [motivation],
So I can [outcome].

Example:

• When I'm in a meeting and someone mentions an action item,
• I want to capture it instantly without disrupting the conversation,
• So I can ensure I don't forget commitments and follow through.

---

Common Mistakes to Avoid

Mistake 1: Treating All Feedback Equally

Not all user feedback is equally valuable. I'll help you:

• Weight feedback by frequency (how many users mentioned it)
• Consider severity (how painful the problem is)
• Factor in strategic fit (does this align with your roadmap)

Mistake 2: Building Exactly What Users Ask For

Users are great at describing problems, terrible at designing solutions.

When I see a feature request, I'll dig into:

• What problem are they really trying to solve?
• What's the job-to-be-done?
• Are there simpler solutions?

Mistake 3: Confirmation Bias

If you went into interviews with a hypothesis, I'll flag:

• Evidence that supports your hypothesis
• Evidence that contradicts it
• Contradictory evidence gets extra weight (it's harder to see)

Mistake 4: The Silent Majority

Remember: the users who agreed to be interviewed are not representative of all users.

I'll remind you:

• Who did you NOT talk to?
• What segments are missing from this research?
• What assumptions are you making about non-participants?

---

Output Integration

Where Files Go

Research synthesis:

• Active work: outputs/research-synthesis/[topic]-[date].md
• When finalized: Archive to context-library/research/[topic]-synthesis.md for future reference
• Executive summary: Can be shared directly with stakeholders

Link to Other Work

After synthesis:

• Create PRD - Use /prd-draft to turn top themes into feature spec
• Inform strategy - If findings affect multiple features, feed to /write-prod-strategy
• Update roadmap - Which themes map to Q# priorities?
• Competitive context - If competitor mentions appear, link to /competitor-analysis

Cross-Skill Integration

Feeds into:

• /prd-draft - Auto-populate Hypothesis with user quotes and insights
• /write-prod-strategy - Themes inform strategic pillars
• /status-update - Key research findings go in stakeholder updates
• /competitor-analysis - If competitors mentioned, extract those mentions

Pulls from:

• context-library/research/ - What questions were asked?
• context-library/prds/ - What was the original problem hypothesis?
• /interview-guide - Questions asked in the interview
• context-library/meetings/ - Past conversations about this problem

---

Integration With Your PRD

After synthesis, I'll offer:

Your research synthesis is complete!

**Next steps:**

1. **Create a PRD** - Use `/prd-draft` to turn these insights into a spec
2. **Update roadmap** - Which themes should we prioritize this quarter?
3. **Share with stakeholders** - I can help draft a summary for leadership
4. **Plan follow-up research** - What open questions need validation?

What would you like to do first?

Auto-Populate PRD Sections

When you use /prd-draft after synthesis, I'll automatically:

• Pull user quotes into the "Hypothesis" section
• Add frequency data to "Strategic Fit"
• Suggest success metrics based on pain points
• Flag non-goals from lower-priority themes

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Tips for Better Synthesis

1. Synthesize Quickly

Do this within 24 hours of your last interview while insights are fresh.

2. Involve Your Team

Share the raw observations with designers and engineers. Let them help cluster themes. They'll spot patterns you might miss.

3. Look for the "Why"

Don't stop at surface-level problems. Keep asking "Why?" until you get to the root cause.

Bad: "Users want a faster app" Good: "Users abandon tasks mid-flow because load times break their mental model of the workflow"

4. Preserve User Language

Use exact quotes. Don't paraphrase into "product-speak." User language is powerful for presentations.

5. Note What Surprised You

The most valuable insights are often the ones that contradicted your assumptions.

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Output Deliverables

When synthesis is complete, I'll create:

1. Executive Summary (1 page) - For leadership and stakeholders
2. Full Synthesis Report (Markdown file) - Complete analysis with all themes
3. Insight Highlights (Video clips or quotes) - For sharing with the team
4. Research Archive (Appendix) - Raw observations for future reference

All files will be saved to:

• outputs/research-synthesis/[topic]-[date].md
• context-library/personal-context-pm-background.md (updated)

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When to Do More Research

After synthesis, I might recommend additional research if:

• Themes lack depth (not enough examples)
• Major contradictions remain unresolved
• You talked to <5 users (not enough for saturation)
• Critical segments are missing from your sample
• Open questions outnumber clear insights

Rule of thumb: After 5-8 interviews with the right users, you should have 3-5 clear themes with strong supporting evidence. If not, talk to more people.

---

Remember: User research isn't about validation. It's about discovery. The goal isn't to hear what you want; it's to learn what you need to know.

---

Output Quality Self-Check

Before presenting output to the PM, verify:

• [ ] File saved to correct location: Output saved to outputs/research-synthesis/[topic]-[date].md
• [ ] Context routing table was checked: Reviewed context-library/research/ for past findings, context-library/prds/ for related problem statements, and context-library/strategy/ for strategic fit
• [ ] Themes backed by 2+ sources: Every theme in the synthesis is supported by observations from at least 2 different interview participants (not a single anecdote)
• [ ] Direct user quotes included: Each key finding includes at least one verbatim user quote with attribution (participant name or identifier)
• [ ] Insight severity/frequency rated: Every theme has a frequency count (e.g., "6 out of 8 users") and a severity rating (High/Medium/Low)
• [ ] Recommendations linked to specific product areas: Each recommendation names a concrete feature, flow, or product area it applies to (not "improve the experience")
• [ ] Contradictory findings explicitly flagged: Any cases where users disagreed or data conflicted are called out in a dedicated section with both perspectives presented
• [ ] Handoff to /prd-draft suggested: If actionable findings exist that could become features, the output explicitly suggests running /prd-draft as a next step