proto-persona

## Purpose
Create an initial, assumption-based persona profile that synthesizes available user research, market data, and stakeholder knowledge into a working hypothesis about your target user. Use this to align teams early in product development, guide initial design decisions, and identify gaps in understanding that require validation through research.

This is not a validated persona—it's a "proto" (prototype) persona that evolves as you learn more. Think of it as a structured placeholder that prevents design-by-committee while acknowledging you don't have all the answers yet.

## Key Concepts

### What is a Proto-Persona?
A proto-persona is a lightweight, hypothesis-driven persona created from:
- **Existing research:** User interviews, surveys, analytics (if available)
- **Market data:** Industry reports, competitor analysis, demographic trends
- **Stakeholder knowledge:** Sales, support, and team insights
- **Informed assumptions:** Best guesses that need validation

### Proto vs. Validated Persona
| Proto-Persona | Validated Persona |
|---------------|-------------------|
| Created in hours/days | Created over weeks/months |
| Based on assumptions + limited research | Based on extensive user research |
| Used to align teams early | Used to guide detailed design |
| Evolves rapidly | Stable over time |
| Good enough to start | High confidence |

### Why Use Proto-Personas?
- **Speed:** Align teams quickly without waiting for months of research
- **Focus:** Provides a shared reference point for "who we're building for"
- **Hypothesis framing:** Makes assumptions explicit, which can then be validated
- **Prevents generic design:** "Design for everyone" = design for no one

### Anti-Patterns (What This Is NOT)
- **Not validated research:** Don't treat it as fact—it's a hypothesis
- **Not a replacement for user research:** Use it to *guide* research, not avoid it
- **Not demographic data alone:** Age and location don't explain behavior
- **Not permanent:** Proto-personas should evolve as you learn

### When to Use This
- Early-stage product development (before extensive user research)
- Kicking off a new feature or pivot
- Aligning stakeholders on target users
- Identifying research gaps (who do we need to interview?)

### When NOT to Use This
- After you've done extensive user research (create a validated persona instead)
- For mature products with known user segments (you should already have validated personas)
- As a substitute for quantitative data (proto-personas inform research; research validates them)

---

## Application

Use `template.md` for the full fill-in structure.

### Step 1: Gather Available Context
Before creating a proto-persona, collect:
- **User research:** Interview notes, survey results, support tickets
- **Analytics:** Usage data, demographics, behavioral patterns
- **Market data:** Industry reports, competitor user bases
- **Stakeholder insights:** Sales/support/CS teams who interact with users
- **Product context:** What problem are you solving? (reference `skills/problem-statement/SKILL.md`)

**If missing context:** Don't fabricate—note gaps and plan research to fill them.

---

### Step 2: Define the Persona's Identity

#### Name
Give the persona an **alliterative, memorable name** (makes it easier to reference).

```markdown
### Name
- [Alliterative name, e.g., "Manager Mike," "Startup Sarah," "Enterprise Emma"]
```

**Quality checks:**
- **Memorable:** Can the team recall it easily?
- **Not generic:** Avoid "User 1" or "Persona A"

---

#### Bio & Demographics
Describe who this person is in the real world.

```markdown
### Bio & Demographics
- [Age range]
- [Geographic location]
- [Social status (married, single, family, etc.)]
- [Online presence (active on LinkedIn, avoids social media, etc.)]
- [Leisure activities]
- [Career status (job title, industry, seniority)]
```

**Quality checks:**
- **Behavioral, not just demographic:** Don't stop at "30-40 years old, lives in SF"—add "Works remotely, active in Slack communities, juggles 3 side projects"
- **Context-relevant:** Only include demographics that influence product decisions

**Example:**
- "35-45 years old, lives in urban areas (NYC, SF, Austin)"
- "Director-level at mid-sized tech companies (50-500 employees)"
- "Active on LinkedIn and Twitter, attends 2-3 conferences per year"
- "Married with young kids, values work-life balance"
- "Plays rec sports on weekends, listens to business podcasts during commute"

---

### Step 3: Capture Their Voice

#### Quotes
Use real or representative quotes that reveal how they think and speak.

```markdown
### Quotes
- "[Quote 1 revealing what they say, feel, or think]"
- "[Quote 2 revealing frustrations or motivations]"
- "[Quote 3 revealing attitudes or beliefs]"
```

**Quality checks:**
- **Authentic:** Use real quotes from interviews/support tickets if available
- **Revealing:** Quotes should expose mindset, not just facts ("I need better tools" is weak; "I'm drowning in manual work and can't focus on strategy" is strong)

**Example:**
- "I spend 10 hours a week in status meetings that could be emails."
- "I'm tired of tools that promise automation but require a developer to set up."
- "My team expects me to have answers immediately, but I'm constantly searching for data."

---

### Step 4: Document Their Context

#### Pains
What problems or frustrations does this persona experience? (Reference `skills/jobs-to-be-done/SKILL.md` for structure.)

```markdown
### Pains
- [Pain point 1 related to the problem space]
- [Pain point 2 related to the problem space]
- [Pain point 3 related to the problem space]
```

**Quality checks:**
- **Specific:** "Frustrated with tools" is vague; "Spends 3 hours/week manually copying data between tools" is specific
- **Related to your product:** Focus on pains your product could address

---

#### What is This Person Trying to Accomplish?
What behaviors, actions, or outcomes are they pursuing?

```markdown
### What is This Person