Persona Development

When to Use This Skill

Use this skill when:

• Persona Development tasks - Working on create evidence-based user personas with empathy maps. develop detailed user archetypes from research data to guide product decisions and maintain user-centered focus
• Planning or design - Need guidance on Persona Development approaches
• Best practices - Want to follow established patterns and standards

Overview

Personas are fictional representations of key user segments based on real research data. They help teams maintain empathy for users throughout the product development process by making abstract user segments concrete and memorable.

Persona Types

Type	Based On	Best For
Research-Based	Extensive user research	Mature products, significant investment
Proto-Persona	Team assumptions	Early stage, hypothesis generation
Jobs Persona	JTBD analysis	Outcome-focused products
Buyer Persona	Purchase behavior	B2B, sales-focused products
Negative Persona	Anti-patterns	Avoiding wrong customers

Persona Components

Core Elements

Component	Description	Example
Name	Memorable, representative	"Developer Dana"
Photo	Representative image	Stock photo or illustration
Quote	Captures essence	"I need to ship fast without breaking things"
Demographics	Relevant background	Role, experience, company size
Goals	What they want to achieve	"Reduce code review time"
Frustrations	Pain points and blockers	"Manual processes slow me down"
Behaviors	How they work	"Reviews 5-10 PRs daily"
Technology	Tools and preferences	"VS Code, GitHub, Slack"

Extended Elements

Component	Description
Bio	Brief narrative background
Day in the Life	Typical workflow
Influence Map	Who influences their decisions
Success Metrics	How they measure success
Objections	Concerns about solutions

Empathy Map

The empathy map captures four quadrants of user experience:

┌─────────────────────────────────────────────────────────────────┐
│                           SAYS                                  │
│  What do they say out loud? Direct quotes from interviews.      │
│  "I spend half my day in code reviews"                          │
│  "The process is frustrating but necessary"                     │
├────────────────────────────────┬────────────────────────────────┤
│            THINKS              │            DOES                │
│  What are they thinking?       │  What actions do they take?    │
│  (May differ from says)        │  Observable behaviors          │
│  "Is this code really safe?"   │  Reads every line carefully    │
│  "I might miss something"      │  Cross-references documentation│
│  "This is taking too long"     │  Uses multiple browser tabs    │
├────────────────────────────────┴────────────────────────────────┤
│                           FEELS                                 │
│  Emotional state during the experience                          │
│  😰 Anxious about missing bugs    😤 Frustrated with slow tools │
│  😊 Satisfied when helping team   😩 Overwhelmed by PR volume   │
└─────────────────────────────────────────────────────────────────┘

Extended Empathy Map

┌─────────────────────────────────────────────────────────────────┐
│                          PAINS                                  │
│  Obstacles, frustrations, risks                                 │
│  • Repetitive manual tasks                                      │
│  • Context switching overhead                                   │
│  • Fear of missing critical issues                              │
├─────────────────────────────────────────────────────────────────┤
│                          GAINS                                  │
│  Wants, needs, success measures                                 │
│  • Faster time to merge                                         │
│  • Confidence in code quality                                   │
│  • Recognition from team                                        │
└─────────────────────────────────────────────────────────────────┘

Creating Personas

Step 1: Gather Research Data

Data Sources:

• User interviews (primary)
• Customer support tickets
• Survey responses
• Usage analytics
• Sales call notes
• Social media / forum discussions

Minimum Viable Research:

• 5-8 user interviews
• Support ticket themes analysis
• Basic usage data

Step 2: Identify Patterns

Affinity Mapping:

1. Write key observations on sticky notes
2. Group similar observations
3. Name each group
4. Look for persona-defining clusters

Behavioral Variables:

• Frequency of use
• Feature preferences
• Decision-making process
• Information sources
• Pain tolerance

Step 3: Draft Personas

For each distinct user segment:

1. Synthesize demographics from research data
2. Craft representative quote capturing essence
3. List goals and frustrations in user's language
4. Describe typical behaviors with specifics
5. Create empathy map from interview data

Step 4: Validate and Refine

• Review with team for accuracy
• Check against additional data
• Validate with users if possible
• Iterate based on feedback

Persona Template

[Persona Name]

Role: [Job Title / Role] Experience: [Years in role, expertise level] Context: [Company size, industry, team structure]

Quote: "[Memorable quote that captures their perspective]"

Goals

• [Primary goal]
• [Secondary goal]
• [Tertiary goal]

Frustrations

• [Major pain point]
• [Secondary frustration]
• [Minor annoyance]

Behaviors

• [Key behavior 1]
• [Key behavior 2]
• [Key behavior 3]

Technology Profile

• Primary tools: [List]
• Preferred platforms: [List]
• Tech savviness: [Low/Medium/High]

A Day in Their Life

[2-3 paragraph narrative of typical day]

Success Metrics

• [How they measure their own success]
• [What their manager measures]

Objections / Concerns

• [Potential concerns about your solution]
• [What would make them hesitant]

---

Example Persona

Developer Dana

Role: Senior Software Engineer Experience: 6 years, full-stack development Context: Mid-size SaaS company (200 employees), 8-person engineering team

Quote: "I want to ship quality code fast. The review process shouldn't be the bottleneck."

Goals

• Maintain high code quality across the team
• Reduce time spent on repetitive review tasks
• Help junior developers learn best practices

Frustrations

• Inconsistent code review standards across team
• Context switching between reviews and coding
• Catching the same types of issues repeatedly

Behaviors

• Reviews 5-10 pull requests per day
• Uses VS Code with multiple extensions
• Writes detailed PR descriptions
• Creates documentation for complex changes

Technology Profile

• Primary tools: VS Code, GitHub, Slack, Jira
• Preferred platforms: macOS, Linux
• Tech savviness: High

A Day in Their Life

Dana starts the day by checking Slack and email for urgent reviews. After the daily standup, she spends about 2 hours reviewing PRs from overnight work. She tries to batch reviews to minimize context switching, but urgent PRs often interrupt her coding blocks.

Afternoons are for her own development work, but she's frequently pulled into discussions about code patterns and architecture decisions. She mentors two junior developers who often ask for quick reviews of their work.

Success Metrics

• Sprint velocity (team metric)
• Bugs found in production (team metric)
• PR review turnaround time (personal goal: < 4 hours)

Objections / Concerns

• "Will this slow down my workflow?"
• "How accurate are AI suggestions really?"
• "I need to maintain my own judgment, not outsource it"

---

Proto-Personas

When research data is limited, create proto-personas based on team assumptions.

Proto-Persona Template

┌─────────────────────────────────────────────────────────────────┐
│  Name: [Descriptive Name]                         [Sketch/Icon] │
│  Role: [Job Title]                                              │
├─────────────────────────────────────────────────────────────────┤
│  Behaviors:               │  Needs/Goals:                       │
│  • [Assumed behavior 1]   │  • [Assumed need 1]                 │
│  • [Assumed behavior 2]   │  • [Assumed need 2]                 │
│  • [Assumed behavior 3]   │  • [Assumed need 3]                 │
├─────────────────────────────────────────────────────────────────┤
│  Pain Points:             │  Demographics:                      │
│  • [Assumed pain 1]       │  • [Age range, if relevant]         │
│  • [Assumed pain 2]       │  • [Company size]                   │
│  • [Assumed pain 3]       │  • [Tech experience]                │
└─────────────────────────────────────────────────────────────────┘

Important: Proto-personas are hypotheses. Validate with research!

Jobs Personas

When using JTBD, create personas around jobs rather than demographics.

Jobs Persona Template

Job: [The main job to be done]

When I... [situation/context] I want to... [motivation] So I can... [expected outcome]

Hiring Criteria:

• [What they look for in a solution]
• [What would make them "hire" a product]

Firing Triggers:

• [What would make them "fire" a product]
• [Deal breakers]

AI-Assisted Persona Development

From Interview Data

Given interview transcripts:

1. Extract behavioral patterns
2. Identify goals and frustrations
3. Find representative quotes
4. Create empathy map
5. Draft persona narrative

From Analytics Data

Given usage data:

1. Identify behavioral segments
2. Characterize each segment
3. Hypothesize goals/frustrations
4. Flag areas needing interview validation

Persona Validation

Review persona against:

1. Does it represent real users?
2. Is it specific enough to be useful?
3. Does it help make decisions?
4. Would users recognize themselves?

Using Personas

In Product Development

• Reference personas in user stories
• Include persona name in requirements
• Design for specific personas first
• Prioritize features by persona value

In Meetings

• "What would [Persona] think?"
• "How does this help [Persona]'s goal?"
• "Would [Persona] understand this?"

Anti-Patterns

❌ Creating too many personas (3-5 is usually enough) ❌ Including irrelevant demographics (if it doesn't affect behavior, skip it) ❌ Making personas too perfect (include realistic flaws) ❌ Letting personas become stale (update quarterly)

Integration Points

Inputs from:

• design-thinking skill: Empathy research → Persona data
• jtbd-analysis skill: Job context → Jobs personas
• User interviews → Quotes, behaviors

Outputs to:

• User stories → "As [Persona]..."
• design-sprint skill: Personas for prototype testing
• Marketing → Messaging and positioning

References

For additional Persona Development resources, see: