Ux Researcher Designer

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name: "ux-researcher-designer" description: UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use for user research, persona creation, journey mapping, and design validation.

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UX Researcher & Designer

Generate user personas from research data, create journey maps, plan usability tests, and synthesize research findings into actionable design recommendations.

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Table of Contents

Trigger Terms

Workflows

- Workflow 1: Generate User Persona - Workflow 2: Create Journey Map - Workflow 3: Plan Usability Test - Workflow 4: Synthesize Research

Tool Reference

Quick Reference Tables

Knowledge Base

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Trigger Terms

Use this skill when you need to:

"create user persona"

"generate persona from data"

"build customer journey map"

"map user journey"

"plan usability test"

"design usability study"

"analyze user research"

"synthesize interview findings"

"identify user pain points"

"define user archetypes"

"calculate research sample size"

"create empathy map"

"identify user needs"

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Workflows

Workflow 1: Generate User Persona

Situation: You have user data (analytics, surveys, interviews) and need to create a research-backed persona.

Steps:

1. Prepare user data

Required format (JSON):

   [
     {
       "user_id": "user_1",
       "age": 32,
       "usage_frequency": "daily",
       "features_used": ["dashboard", "reports", "export"],
       "primary_device": "desktop",
       "usage_context": "work",
       "tech_proficiency": 7,
       "pain_points": ["slow loading", "confusing UI"]
     }
   ]
   

2. Run persona generator

   # Human-readable output
   python scripts/persona_generator.py
   # JSON output for integration
   python scripts/persona_generator.py json
   

3. Review generated components

| Component | What to Check | |-----------|---------------| | Archetype | Does it match the data patterns? | | Demographics | Are they derived from actual data? | | Goals | Are they specific and actionable? | | Frustrations | Do they include frequency counts? | | Design implications | Can designers act on these? |

4. Validate persona

- Show to 3-5 real users: "Does this sound like you?" - Cross-check with support tickets - Verify against analytics data

5. Reference: See references/persona-methodology.md for validity criteria

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Workflow 2: Create Journey Map

Situation: You need to visualize the end-to-end user experience for a specific goal.

Steps:

1. Define scope

| Element | Description | |---------|-------------| | Persona | Which user type | | Goal | What they're trying to achieve | | Start | Trigger that begins journey | | End | Success criteria | | Timeframe | Hours/days/weeks |

2. Gather journey data

Sources: - User interviews (ask "walk me through...") - Session recordings - Analytics (funnel, drop-offs) - Support tickets

3. Map the stages

Typical B2B SaaS stages:

   Awareness → Evaluation → Onboarding → Adoption → Advocacy
   

4. Fill in layers for each stage

   Stage: [Name]
   ├── Actions: What does user do?
   ├── Touchpoints: Where do they interact?
   ├── Emotions: How do they feel? (1-5)
   ├── Pain Points: What frustrates them?
   └── Opportunities: Where can we improve?
   

5. Identify opportunities

Priority Score = Frequency × Severity × Solvability

6. Reference: See references/journey-mapping-guide.md for templates

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Workflow 3: Plan Usability Test

Situation: You need to validate a design with real users.

Steps:

1. Define research questions

Transform vague goals into testable questions:

| Vague | Testable | |-------|----------| | "Is it easy to use?" | "Can users complete checkout in <3 min?" | | "Do users like it?" | "Will users choose Design A or B?" | | "Does it make sense?" | "Can users find settings without hints?" |

2. Select method

| Method | Participants | Duration | Best For | |--------|--------------|----------|----------| | Moderated remote | 5-8 | 45-60 min | Deep insights | | Unmoderated remote | 10-20 | 15-20 min | Quick validation | | Guerrilla | 3-5 | 5-10 min | Rapid feedback |

3. Design tasks

Good task format:

   SCENARIO: "Imagine you're planning a trip to Paris..."
   GOAL: "Book a hotel for 3 nights in your budget."
   SUCCESS: "You see the confirmation page."
   

Task progression: Warm-up → Core → Secondary → Edge case → Free exploration

4. Define success metrics

| Metric | Target | |--------|--------| | Completion rate | >80% | | Time on task | <2× expected | | Error rate | <15% | | Satisfaction | >4/5 |

5. Prepare moderator guide

- Think-aloud instructions - Non-leading prompts - Post-task questions

6. Reference: See references/usability-testing-frameworks.md for full guide

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Workflow 4: Synthesize Research

Situation: You have raw research data (interviews, surveys, observations) and need actionable insights.

Steps:

1. Code the data

Tag each data point: - [GOAL] - What they want to achieve - [PAIN] - What frustrates them - [BEHAVIOR] - What they actually do - [CONTEXT] - When/where they use product - [QUOTE] - Direct user words

2. Cluster similar patterns

   User A: Uses daily, advanced features, shortcuts
   User B: Uses daily, complex workflows, automation
   User C: Uses weekly, basic needs, occasional
   Cluster 1: A, B (Power Users)
   Cluster 2: C (Casual User)
   

3. Calculate segment sizes

| Cluster | Users | % | Viability | |---------|-------|---|-----------| | Power Users | 18 | 36% | Primary persona | | Business Users | 15 | 30% | Primary persona | | Casual Users | 12 | 24% | Secondary persona |

4. Extract key findings

For each theme: - Finding statement - Supporting evidence (quotes, data) - Frequency (X/Y participants) - Business impact - Recommendation

5. Prioritize opportunities

| Factor | Score 1-5 | |--------|-----------| | Frequency | How often does this occur? | | Severity | How much does it hurt? | | Breadth | How many users affected? | | Solvability | Can we fix this? |

6. Reference: See references/persona-methodology.md for analysis framework

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Tool Reference

persona_generator.py

Generates data-driven personas from user research data.

| Argument | Values | Default | Description | |----------|--------|---------|-------------| | format | (none), json | (none) | Output format |

Sample Output:

============================================================
PERSONA: Alex the Power User
============================================================
📝 A daily user who primarily uses the product for work purposes
Archetype: Power User
Quote: "I need tools that can keep up with my workflow"
👤 Demographics:
  • Age Range: 25-34
  • Location Type: Urban
  • Tech Proficiency: Advanced
🎯 Goals & Needs:
  • Complete tasks efficiently
  • Automate workflows
  • Access advanced features
😤 Frustrations:
  • Slow loading times (14/20 users)
  • No keyboard shortcuts
  • Limited API access
💡 Design Implications:
  → Optimize for speed and efficiency
  → Provide keyboard shortcuts and power features
  → Expose API and automation capabilities
📈 Data: Based on 45 users
    Confidence: High

Archetypes Generated:

| Archetype | Signals | Design Focus | |-----------|---------|--------------| | power_user | Daily use, 10+ features | Efficiency, customization | | casual_user | Weekly use, 3-5 features | Simplicity, guidance | | business_user | Work context, team use | Collaboration, reporting | | mobile_first | Mobile primary | Touch, offline, speed |

Output Components:

| Component | Description | |-----------|-------------| | demographics | Age range, location, occupation, tech level | | psychographics | Motivations, values, attitudes, lifestyle | | behaviors | Usage patterns, feature preferences | | needs_and_goals | Primary, secondary, functional, emotional | | frustrations | Pain points with evidence | | scenarios | Contextual usage stories | | design_implications | Actionable recommendations | | data_points | Sample size, confidence level |

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Quick Reference Tables

Research Method Selection

| Question Type | Best Method | Sample Size | |---------------|-------------|-------------| | "What do users do?" | Analytics, observation | 100+ events | | "Why do they do it?" | Interviews | 8-15 users | | "How well can they do it?" | Usability test | 5-8 users | | "What do they prefer?" | Survey, A/B test | 50+ users | | "What do they feel?" | Diary study, interviews | 10-15 users |

Persona Confidence Levels

| Sample Size | Confidence | Use Case | |-------------|------------|----------| | 5-10 users | Low | Exploratory | | 11-30 users | Medium | Directional | | 31+ users | High | Production |

Usability Issue Severity

| Severity | Definition | Action | |----------|------------|--------| | 4 - Critical | Prevents task completion | Fix immediately | | 3 - Major | Significant difficulty | Fix before release | | 2 - Minor | Causes hesitation | Fix when possible | | 1 - Cosmetic | Noticed but not problematic | Low priority |

Interview Question Types

| Type | Example | Use For | |------|---------|---------| | Context | "Walk me through your typical day" | Understanding environment | | Behavior | "Show me how you do X" | Observing actual actions | | Goals | "What are you trying to achieve?" | Uncovering motivations | | Pain | "What's the hardest part?" | Identifying frustrations | | Reflection | "What would you change?" | Generating ideas |

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Knowledge Base

Detailed reference guides in references/:

| File | Content | |------|---------| | persona-methodology.md | Validity criteria, data collection, analysis framework | | journey-mapping-guide.md | Mapping process, templates, opportunity identification | | example-personas.md | 3 complete persona examples with data | | usability-testing-frameworks.md | Test planning, task design, analysis |

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Validation Checklist

Persona Quality

[ ] Based on 20+ users (minimum)

[ ] At least 2 data sources (quant + qual)

[ ] Specific, actionable goals

[ ] Frustrations include frequency counts

[ ] Design implications are specific

[ ] Confidence level stated

Journey Map Quality

[ ] Scope clearly defined (persona, goal, timeframe)

[ ] Based on real user data, not assumptions

[ ] All layers filled (actions, touchpoints, emotions)

[ ] Pain points identified per stage

[ ] Opportunities prioritized

Usability Test Quality

[ ] Research questions are testable

[ ] Tasks are realistic scenarios, not instructions

[ ] 5+ participants per design

[ ] Success metrics defined

[ ] Findings include severity ratings

Research Synthesis Quality

[ ] Data coded consistently

[ ] Patterns based on 3+ data points

[ ] Findings include evidence

[ ] Recommendations are actionable

[ ] Priorities justified