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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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):

   [
     {
       "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

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:

### [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

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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. 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.

6. 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

7. 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

8. 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

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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

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):

[{
  "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"]
}]

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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)

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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

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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)

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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