UX Researcher & Designer

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

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

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"

Clarify First

Before generating the research artifact, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• ] Which deliverable — persona, journey map, usability test plan, or research synthesis (sets which workflow and template applies)
• ] Available data and volume — analytics/interviews/surveys and how many users (drives persona confidence and proto- vs data-driven persona)
• ] The user goal and scope — the persona, the goal being mapped, and start/end (drives journey-map stages and research questions)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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): 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"] } ]

1. Run persona generator

bash # Human-readable output python scripts/persona_generator.py

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

1. 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? |

1. Validate persona

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

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

Proto-Persona Canvas (Lightweight Alternative)

When you lack research data but need a hypothesis-driven persona to align the team, use a proto-persona canvas. Proto-personas are assumption tools -- not validated truth -- meant to be tested and refined.

Use when: Starting a new initiative with no research budget, aligning a cross-functional team quickly, or creating a testable hypothesis about your user.

Proto-Persona Canvas Template:

markdown
### [Alliterative Name] (e.g., "Careful Carlos")

**Bio & Demographics:**
- Age, geography, social status, career stage
- Online presence, leisure activities, partner status

**Quotes** (what they say, feel, think):
- "[Direct quote capturing their perspective]"
- "[Quote revealing frustration or aspiration]"

**Pains:**
- [Pain related to the problem space]
- [Pain related to current workarounds]

**What They're Trying to Accomplish:**
- [Observable behavior 1]
- [Observable behavior 2]

**Goals** (wants, needs, dreams):
- [Short-term goal]
- [Long-term aspiration]

**Attitudes & Influences:**
- Decision Making Authority: [Can they buy/adopt your solution?]
- Decision Influencers: [Who influences their decisions?]
- Beliefs & Attitudes: [What beliefs impact their choices?]

**Assumptions to Validate:**
- [Top assumption that must be true for this persona to be viable]
- [Second assumption]
- [Third assumption]

Next steps after proto-persona:

1. Generate interview questions to validate assumptions (Recommended)
2. Generate an anti-persona to define scope boundaries
3. Convert into a one-page stakeholder brief

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 |

1. Gather journey data

Sources:

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

1. Map the stages

Typical B2B SaaS stages: Awareness → Evaluation → Onboarding → Adoption → Advocacy

1. 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?

1. Map three experience paths (not just the happy path)

| Stage | Happy Path | Fail Path | Difficult Path | |---|---|---|---| | Awareness | Finds product via search | Never discovers product | Finds competitor first | | Consideration | Clear value proposition | Confused by pricing | Needs manager approval | | Decision | Easy signup flow | Form errors, abandons | Legal review delays | | Delivery & Use | Smooth onboarding | Can't import data | Workaround needed | | Loyalty | Becomes advocate | Churns silently | Stays but complains |

• Happy Path: Everything works as designed.
• Fail Path: User cannot complete their goal and drops off.
• Difficult Path: User completes the goal but with friction, workarounds, or frustration.

1. Add KPIs and ownership per stage

| Stage | Leading KPI | Lagging KPI | Team Owner | |---|---|---|---| | Awareness | Site visits, ad impressions | Brand recall | Marketing | | Consideration | Demo requests, pricing page views | MQL conversion | Marketing/Sales | | Decision | Trial starts, contract sent | Close rate | Sales | | Use | Feature adoption, DAU | Retention rate | Product | | Loyalty | NPS, referral count | LTV, expansion revenue | Customer Success |

1. Identify top friction points and interventions

For each friction point, document:

| Friction Point | Why It Matters | Intervention | Expected Impact | Effort | Confidence | |---|---|---|---|---|---| | Description] | User/business impact] | Proposed fix] | High/Med/Low | S/M/L | High/Med/Low |

Priority Score = Frequency x Severity x Solvability

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

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?" |

1. 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 |

1. 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

1. Define success metrics

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

1. Prepare moderator guide

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

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

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

1. 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)

1. Calculate segment sizes

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

1. Extract key findings

For each theme:

• Finding statement
• Supporting evidence (quotes, data)
• Frequency (X/Y participants)
• Business impact
• Recommendation

1. 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? |

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

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 |

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 |

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 |

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

Tool Reference

persona_generator.py

Generates data-driven personas from user research data, classifying users into archetypes with demographics, psychographics, behaviors, goals, frustrations, and design implications.

| Argument | Type | Default | Description | |----------|------|---------|-------------| | format | positional | (none) | Add json for JSON output; omit for human-readable |

Archetypes supported: power_user, casual_user, business_user, mobile_first

Output components: name, archetype, tagline, quote, demographics, psychographics, behaviors, needs_and_goals, frustrations, scenarios, data_points, design_implications

bash
python scripts/persona_generator.py           # Human-readable formatted output
python scripts/persona_generator.py json      # JSON for programmatic use

Data input format (customize in script):

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"]
}]

Troubleshooting

| Problem | Cause | Solution | |---------|-------|----------| | Persona confidence level is "Low" | Fewer than 20 users in sample data | Collect more data points; combine quantitative analytics with qualitative interviews | | All users classified as same archetype | Insufficient variation in input data | Ensure data includes diverse usage frequencies, devices, and contexts | | Frustrations are generic (fallback defaults) | Not enough pain_points in user data | Enrich user data with pain_points from interviews and support tickets | | Design implications too vague | Patterns don't strongly differentiate | Add more behavioral signals (features_used, session duration, task completion) | | Journey map has flat emotion curve | All stages scored similarly | Re-evaluate with actual user data; conduct contextual interviews per stage | | Usability test sample too small | Fewer than 5 participants | 5 participants find ~85% of usability issues; recruit to minimum 5 | | Research synthesis has no clear patterns | Data not coded consistently | Use consistent tagging scheme (GOAL, PAIN, BEHAVIOR, CONTEXT, QUOTE) |

Success Criteria

| Criterion | Target | How to Measure | |-----------|--------|----------------| | Persona validity | Validated by 3+ real users ("sounds like me") | Post-creation validation interviews | | Persona coverage | All key segments represented | Count of personas vs identified user segments | | Data confidence level | "High" (31+ users) | persona_generator data_points.confidence_level | | Research cadence | 5-8 interviews per segment per quarter | Count of completed research sessions | | Insight-to-action rate | >70% of findings result in design changes | Track findings through to implementation | | Usability issue resolution | All critical/major issues fixed before release | Issue severity tracking | | Journey map freshness | Updated at least quarterly | Last-updated date on each journey map |

Scope & Limitations

In scope:

• Data-driven persona generation from user research
• Archetype classification (power, casual, business, mobile-first)
• User journey mapping frameworks
• Usability test planning and scoring
• Research synthesis and coding methodology
• Interview question frameworks
• Empathy map and opportunity identification

Out of scope:

• Automated user interview recording/transcription
• Real-time analytics integration (use analytics platforms)
• Quantitative survey design and distribution (use Typeform/SurveyMonkey)
• Eye tracking or biometric data analysis
• AI-powered sentiment analysis (tool uses heuristic classification)
• Persona illustration or visual asset generation
• Accessibility auditing (see product-designer or design-system-lead skills)

Integration Points

| Tool / Platform | Integration Method | Use Case | |-----------------|-------------------|----------| | Dovetail / Condens | Export research data, import persona JSON | Centralize research insights | | Figma / Miro | Paste persona output as design artifact | Reference personas during design work | | Notion / Confluence | Human-readable output | Document and share personas with team | | product-manager-toolkit | Persona pain points inform RICE scoring | Connect user needs to feature prioritization | | agile-product-owner | Persona data informs user story personas | Write stories grounded in research | | product-designer | Persona feeds into journey mapping and usability test recruitment | End-to-end design research workflow |