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.

Input

Works best with: The target user or segment you need a working profile for. Also useful: Whatever signal exists — support themes, sales anecdotes, analytics, prior research — plus the decision the persona will guide.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The skill asks who you think the user is and what you already know, then structures it and flags the assumptions needing validation.

Example invocation: Proto-persona for solo bookkeepers adopting our receipt-scanning app — signal: 30 support tickets and 4 sales call notes.

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

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).

### 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.

### Bio & Demographics
- [Age range]
- [Geographic location]
-

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.

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.)

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?

Quality checks:

• Observable: Can you see this behavior? ("Get promoted" is internal; "Deliver projects 2 weeks ahead of schedule" is observable)
• Outcome-focused: Not tasks ("use dashboards") but results ("make data-driven decisions faster")

Goals

What are their wants, needs, dreams?

Quality checks:

• Short-term and long-term: Include tactical goals ("ship feature by Q2") and aspirational goals ("become VP within 3 years")
• Personal and professional: "Spend more time with family" can be as relevant as "increase team productivity"

Step 5: Understand Their Influences

Decision-Making Authority

Do they have the power to buy your solution?

Quality checks:

• Procurement reality: If they're a user but not a buyer, note who approves the purchase

Decision Influencers

Who influences their decisions?

Quality checks:

• Specific: Not just "their manager"—name the types of influences (peer recommendations, Gartner reports, Twitter threads, etc.)

Beliefs & Attitudes

What beliefs and attitudes shape their decisions?

Quality checks:

• Relevant to adoption: Focus on beliefs that affect whether they'd use your product

Step 6: Validate and Iterate

• Share with the team: Does this persona resonate? Do they recognize this person?
• Identify gaps: What don't we know? (Add "[ASSUMPTION—VALIDATE]" tags where uncertain)
• Plan research: Use the proto-persona to guide who to interview next
• Evolve it: As you learn, update the proto-persona (or graduate it to a validated persona)

Examples

See examples/sample.md for full proto-persona examples.

Mini example excerpt:

Common Pitfalls

Pitfall 1: Demographics Without Behavior

Symptom: "28 years old, lives in NYC, has a dog"

Consequence: Demographics don't explain why someone would use your product.

Fix: Add behavioral context: "Works remotely, active in 5 Slack communities, values async communication tools."

Pitfall 2: Treating Proto-Persona as Fact

Symptom: "Manager Mike would never use feature X because he hates complexity"

Consequence: You're treating an assumption as validated research.

Fix: Add "[ASSUMPTION—VALIDATE]" tags and plan interviews to test hypotheses.

Pitfall 3: Creating 10 Proto-Personas

Symptom: Trying to model every possible user type upfront

Consequence: Analysis paralysis. Teams can't focus on a primary user.

Fix: Start with 1-2 proto-personas (primary + secondary). Add more as you validate and expand.

Pitfall 4: Fabricating Quotes

Symptom: Quotes that sound like marketing copy: "I love products that delight me!"

Consequence: Fake personas lead to fake empathy.

Fix: Use real quotes from interviews, support tickets, or sales calls. If you don't have quotes yet, note "[PLACEHOLDER—NEEDS RESEARCH]."

Pitfall 5: Never Validating

Symptom: Proto-persona created 6 months ago, never updated

Consequence: You're designing for a hypothesis that may be wrong.

Fix: Plan research sprints to validate key assumptions. Evolve the proto-persona as you learn. Graduate it to a validated persona when confidence is high.

References

Related Skills

• skills/problem-statement/SKILL.md — Persona informs the "I am" section
• skills/jobs-to-be-done/SKILL.md — JTBD informs persona pains/goals
• skills/positioning-statement/SKILL.md — Persona is the "For [target]"
• skills/user-story/SKILL.md — Stories use "As a [persona]"

External Frameworks

• Alan Cooper, The Inmates Are Running the Asylum (1998) — Origin of persona concept
• Jeff Gothelf, Lean UX (2013) — Proto-personas as hypothesis-driven research tools
• Indi Young, Mental Models (2008) — Behavior-driven persona development

Dean's Work

• Proto-Persona Profile Prompt (inspired by Productside Product Manager's Playbook)

Provenance

• Adapted from prompts/proto-persona-profile.md in the https://github.com/deanpeters/product-manager-prompts repo.

Skill type: Component Suggested filename: proto-persona.md Suggested placement: /skills/components/ Dependencies: References skills/jobs-to-be-done/SKILL.md, skills/problem-statement/SKILL.md Used by: skills/positioning-statement/SKILL.md, skills/user-story/SKILL.md, skills/problem-statement/SKILL.md