product-user-researchUse this skill when conducting user research: interviews, usability testing, persona creation, and insight synthesis. This skill enforces: research question definition, participant recruitment, interview protocols, usability testing methodology. Do NOT use for: quantitative surveys, A/B testing, market sizing, competitive analysis.
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Designs and executes user research studies including interviews, usability testing, insight synthesis, and persona development. The goal is to reduce uncertainty about user needs, behaviors, and pain points through systematic qualitative investigation. Research output directly informs product strategy, feature definition, and design decisions.
Exact user phrases: user research, user interview, customer interview, usability testing, persona, user journey.
Research study plan with interview protocol, usability test script, synthesis report, and persona artifacts.
## User Research Study
### Research Question
{question} | Decision it will inform: {decision}
### Method: {interviews / usability testing / diary study}
Participants: {N} users | Segment: {target segment}
### Interview Protocol
{Sections: warm-up, exploration, deep-dive, wrap-up}
### Key Insights
1. {insight} (evidence: {source})
2. {insight} (evidence: {source})
### Personas Created
{persona name}: {demographics} | {goals} | {pain points}
### Recommendations
{actionable next steps based on insights}
No preamble. No postamble. No explanations.
7000 tokens
Research methods fall along two primary axes:
| Axis | Spectrum | Description |
|---|---|---|
| Nature | Generative vs Evaluative | Explore unknown problems vs test known solutions |
| Data | Qualitative vs Quantitative | Rich depth vs statistical breadth |
| Setting | Natural vs Controlled | Real context vs lab environment |
| Timing | Longitudinal vs Point-in-time | Behavior over time vs snapshot |
User research maps to the Double Diamond design process:
| Phase | Diamond Stage | Research Activity | Output |
|---|---|---|---|
| Discover | Divergent | Exploratory interviews, diary studies, field observation | Problem space map, user needs |
| Define | Convergent | Affinity mapping, thematic analysis, persona creation | Problem statement, personas |
| Develop | Divergent | Concept testing, co-creation workshops, prototype feedback | Solution ideas, design criteria |
| Deliver | Convergent | Usability testing, beta feedback, satisfaction surveys | Validated design, launch readiness |
| Level | Name | Characteristics |
|---|---|---|
| 1 | Reactive | Research done ad-hoc when crisis hits, no process |
| 2 | Foundational | Basic methods established, some regular cadence |
| 3 | Integrated | Research embedded in product cycles, dedicated budget |
| 4 | Strategic | Research drives strategy, cross-functional partnerships |
| 5 | Generative | Continuous research culture, proactive discovery |
Assess current maturity level and define target level before planning research.
Articulate the specific question the research will answer. Identify the product decision that depends on the answer. Distinguish generative (exploratory, what's possible) vs evaluative (testing, does it work) research. Frame questions that can be answered with qualitative data.
Write research questions using this structure:
Research Question: How do [segment] currently [task/goal] and what prevents them from [desired outcome]?
Decision: We will decide [specific decision] based on the findings.
Hypothesis: We believe [assumption] is true because [reasoning].
Criteria for good research questions:
Bad example: "Do users like our new feature?" (leading, hypothetical, yes/no) Good example: "How do users currently accomplish task X and where do they experience friction?"
Define participant criteria using screening questionnaire. Recruit 5-8 participants per segment for qualitative studies. Ensure diversity across relevant dimensions (usage frequency, plan tier, role). Offer appropriate incentives. Schedule 60-min sessions with 15-min buffer between.
Participant screener template:
Screener Criteria for [Study Name]:
- Must-have: [non-negotiable attributes]
- Nice-to-have: [preferred but not required]
- Exclude: [who should not participate]
- Diversity targets: [e.g., 40-60% gender split, 3+ plan tiers]
Recruitment channels (prioritized):
1. [Best channel for this segment]
2. [Secondary channel]
3. [Fallback channel]
Incentive: ${amount} per {duration} session
Payment method: {gift card / transfer / charity donation}
Sample size guidelines:
Write semi-structured interview guide. Start with broad questions, narrow to specifics. Use active listening and follow-up probes. Cover: current behavior, pain points, goals, reaction to concepts. Include task scenarios for usability testing. Avoid leading questions.
Protocol structure with timing:
| Section | Duration | Purpose | Key Techniques |
|---|---|---|---|
| Introduction | 5 min | Consent, rapport building | Explain "no wrong answers," confirm recording |
| Warm-up | 5-10 min | Context, current behavior | "Tell me about your role", day-in-the-life |
| Main exploration | 20-25 min | Deep dive into topic | Behavioral questions, process mapping |
| Concept response | 10-15 min | Reaction to ideas/solutions | Show prototype, observe reaction |
| Wrap-up | 5 min | Reflection, closure | "What did we miss?", next steps |
Effective probing techniques:
Questions to avoid:
Define tasks that cover critical user journeys. Measure task completion rate, time on task, error rate. Use think-aloud protocol. Capture both performance metrics and qualitative feedback. Test with prototype (low or high fidelity depending on stage).
Task design principles:
Metrics per task:
| Metric | Collection Method | Target Benchmark |
|---|---|---|
| Task completion (binary) | Observer records pass/fail | >80% first attempt |
| Time on task | Screen recording timing | Varies by task complexity |
| Error rate | Count of wrong paths or clicks | <2 per task |
| SEQ (1-7) | Post-task survey | >5.5 |
| CES (1-5) | Post-task survey | <2 |
| SUS (0-100) | Post-test survey | >68 |
Use affinity mapping to cluster observations into themes. Identify patterns across participants (not just standout quotes). Triangulate findings with quantitative data. Develop user journey maps showing pain points and opportunities. Prioritize insights by severity and frequency.
Affinity mapping process:
Synthesis output formats:
| Format | Best For | Structure |
|---|---|---|
| Findings report | Stakeholder communication | Exec summary, method, findings, recommendations |
| Journey map | Visualizing end-to-end experience | Stages, actions, thoughts, emotions, opportunities |
| Experience principles | Guiding design decisions | 3-5 principles derived from research |
| Opportunity map | Prioritizing next steps | Opportunities ranked by impact and effort |
Create 3-5 distinct personas based on research patterns. Include: demographics, goals, behaviors, pain points, and context. Give each persona a name and representative photo. Base persona on real user data, not stereotypes. Include relevant quotes from interviews.
Persona template:
Persona: {Name}
Role: {Job title or user type}
Demographics: {Age range, location, industry}
Quote: {Representative verbatim quote}
Bio: {2-3 sentence narrative of their context and routine}
Goals:
- Primary: {main goal when using the product}
- Secondary: {supporting goals}
Pain Points:
1. {Specific frustration} — "{supporting quote}"
2. {Specific frustration} — "{supporting quote}"
3. {Specific frustration} — "{supporting quote}"
Behaviors:
- {Behavioral pattern} (evidence: {N} of {total} participants)
- {Behavioral pattern} (evidence: {N} of {total} participants)
Current Tools: {what they use instead of or alongside the product}
Structure the findings report for maximum actionability:
Every finding must include:
| Pitfall | Description | Prevention |
|---|---|---|
| Confirmation bias | Seeking evidence that confirms assumptions | Ask disconfirming questions; include devil's advocate |
| Leading questions | Questions that suggest a desired answer | Use behavioral recall, not opinions about future |
| Hypothetical bias | Users overstate future behavior intentions | Ask about past behavior, test with real prototypes |
| Social desirability | Participants tell you what they think you want | Normalize negative feedback; "we need your honesty" |
| Recency bias | Weighting recent or memorable events too heavily | Use diary studies for longitudinal accuracy |
| Small sample generalization | Drawing quantitative conclusions from N=5 | Report patterns, not percentages from qual studies |
| Cherry-picking quotes | Selecting quotes that support a predetermined narrative | Document all themes, including contradictory ones |
| Solutioneering | Participants suggesting features instead of describing problems | Redirect: "Help me understand what you're trying to accomplish" |
| Ignoring edge cases | Only studying mainstream users to the exclusion of extreme users | Deliberately recruit power users and non-users |
| Analysis paralysis | Collecting data without synthesizing | Set aside dedicated synthesis time: 2x session length |
| Practice | Rationale |
|---|---|
| Always pilot test your protocol | Catches ambiguous questions, timing issues, technical glitches |
| Record and transcribe every session | Enables accurate analysis and verbatim quotes |
| Take breaks between sessions | Prevents carryover bias; debrief and reset each time |
| Involve observers from cross-functional teams | Builds research empathy and stakeholder buy-in |
| Maintain a research repository | Enables longitudinal analysis and prevents duplicate studies |
| Use a research consent form | Legal requirement; builds trust with participants |
| Share raw data alongside synthesized findings | Allows others to verify conclusions |
| Timebox synthesis: 2x total research time | Prevents analysis paralysis |
| Include at least one "how might we" per insight | Bridges from research to action |
| Validate qualitative findings with quantitative data when possible | Triangulation increases confidence |
# Research Plan: {Study Name}
## Background
- Product area: {what is being studied}
- Stakeholders: {who cares about the outcome}
- Existing knowledge: {what we already know}
## Research Questions
1. {Primary question} → Decision: {what we will decide}
2. {Secondary question} → Decision: {what we will decide}
## Method
- Approach: {interviews / usability testing / diary study}
- Session length: {minutes}
- Total participants: {N} across {M} segments
- Timeline: {start} to {end}
## Participants
- Recruited from: {source}
- Screening criteria: {key criteria}
- Incentive: ${amount} per session
## Outputs
- {Artifact 1}: {description}
- {Artifact 2}: {description}
| Tool | Use Case | Cost |
|---|---|---|
| Dovetail | Transcription, tagging, analysis | Paid |
| Condens | Repository, tagging, highlights | Paid |
| Aurelius | Research repository, tagging | Paid |
| Airtable | Custom research tracking | Freemium |
| Miro/Mural | Affinity mapping (visual) | Freemium |
| Excel/Sheets | Simple data coding and tracking | Free |
| Delve | Thematic analysis tool | Paid |
| EnjoyHQ | Research repository, integrations | Paid |
| Artifact | Tool | Format |
|---|---|---|
| Affinity map | Miro, Mural, FigJam | Visual board |
| Journey map | Smaply, Miro, LucidChart | Timeline visualization |
| Persona | Notion, Canva, PPT | Document or poster |
| Findings report | Notion, Google Docs, Confluence | Structured document |
| Insight database | Airtable, Dovetail | Tagged and searchable |
A B2B SaaS company with 8% monthly churn conducted exit interviews with 15 churned customers. The research revealed that the primary churn driver was not product quality but poor onboarding — 12 of 15 participants never reached the activation milestone. The company redesigned the onboarding flow based on this insight, reducing 90-day churn from 24% to 12% within one quarter.
Method: 30-min structured exit interviews with churned customers Key insight: "I signed up, looked around for 5 minutes, and couldn't figure out what to do next" Action: Redesigned onboarding with guided activation checklist Result: 50% reduction in churn over 90 days
A project management tool was considering 12 new features. User research with 18 participants across 3 segments revealed that 4 of the planned features addressed problems users didn't have, while 2 unplanned features (offline mode and better search) were critical. Reallocating development effort saved an estimated $120K in development costs.
Method: Task-based interviews with card sorting exercise Key insight: Users prioritized reliability (offline access) over new functionality Action: Reprioritized roadmap based on user pain point frequency Result: Avoided building 4 unwanted features, 20% increase in user satisfaction
An e-commerce platform created 4 distinct shopper personas through 25 interviews and a survey of 500 users. The "Bargain Hunter" persona drove a new price-drop alert feature; the "Researcher" persona drove a comparison tool. Conversion rates increased 15% and average session time increased 40%.
Method: 25 interviews + 500-response survey Key insight: Different segments had fundamentally different shopping behaviors that the one-size-fits-all design failed Action: Created personalized experiences for each persona segment Result: 15% conversion increase, 40% session time increase
For quantitative validation of insights, hand off to product-analytics. For running experiments based on findings, hand off to product-ab-testing.
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config: defaults: timeout: 30s retryCount: 3 overrides: production: timeout: 60s retryCount: 5 development: timeout: 300s retryCount: 1
| Metric | Threshold | Severity | Action |
|---|---|---|---|
| Error rate | > 1% over 5min | Critical | Page on-call |
| p99 latency | > 2s over 5min | Warning | Investigate |
| Throughput drop | > 50% over 1min | Critical | Check upstream |
| Queue depth | > 1000 over 1min | Warning | Scale consumers |
| Disk usage | > 85% | Warning | Clean or expand |
| Memory usage | > 90% heap | Critical | Restart or scale |
| Anti-Pattern | Symptom | Root Cause | Solution |
|---|---|---|---|
| Premature optimization | Complex code for no measured benefit | Guessing instead of profiling | Measure first, optimize based on data |
| Copy-paste reuse | Duplicate code across codebase | Lack of abstraction | Extract shared logic into libraries |
| Gold-plating | Features with no current requirement | Over-engineering | YAGNI — build what's needed now |
| Magical thinking | Assumptions without validation | Skipping error handling | Handle all failure modes explicitly |
Cache hierarchy: L1 (in-memory local) → L2 (distributed Redis/Memcached) → L3 (CDN/Edge). Cache invalidation: TTL-based (simple, stale), event-based (complex, fresh), write-through (consistent, higher write latency), write-behind (fast writes, eventual consistency).