customer-persona

name	customer-persona
description	Research-backed customer persona creation with market data and avatar generation. Covers demographics, psychographics, jobs-to-be-done, journey mapping, and anti-personas. Use for: marketing strategy, product development, UX research, sales enablement, content strategy. Triggers: customer persona, buyer persona, user persona, target audience, ideal customer, customer profile, audience research, user research, icp, ideal customer profile, target market, customer avatar, audience persona
allowed-tools	Bash(belt *)

Install the belt CLI skill: npx skills add belt-sh/cli

Customer Persona

Create data-backed customer personas with research and visuals via inference.sh CLI.

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Research your target market
belt app run tavily/search-assistant --input '{
  "query": "SaaS product manager demographics pain points 2024 survey"
}'

# Generate a persona avatar
belt app run falai/flux-dev-lora --input '{
  "prompt": "professional headshot photograph of a 35-year-old woman, product manager, friendly confident expression, modern office background, natural lighting, business casual attire, realistic portrait",
  "width": 1024,
  "height": 1024
}'

Persona Template

┌──────────────────────────────────────────────────────┐
│  [Avatar Photo]                                      │
│                                                      │
│  SARAH CHEN, 34                                      │
│  Product Manager at a Series B SaaS startup          │
│                                                      │
│  "I spend more time making reports than making       │
│   decisions."                                        │
│                                                      │
├──────────────────────────────────────────────────────┤
│  DEMOGRAPHICS          │  PSYCHOGRAPHICS             │
│  Age: 30-38            │  Values: efficiency, data   │
│  Income: $120-160K     │  Personality: analytical,   │
│  Education: BS/MBA     │    organized, collaborative │
│  Location: Urban US    │  Interests: productivity,   │
│  Role: Product/PM      │    leadership, AI tools     │
├──────────────────────────────────────────────────────┤
│  GOALS                 │  PAIN POINTS                │
│  • Ship features       │  • Too many meetings        │
│  faster                │  • Manual reporting (15     │
│  • Data-driven         │    hrs/week)                │
│  decisions             │  • Stakeholder alignment    │
│  • Team alignment      │    is slow                  │
│  • Career growth to    │  • Tool sprawl (8+ apps)   │
│    Director            │  • No single source of      │
│                        │    truth                    │
├──────────────────────────────────────────────────────┤
│  CHANNELS              │  BUYING TRIGGERS            │
│  • LinkedIn (daily)    │  • Peer recommendation      │
│  • Product Hunt        │  • Free trial experience    │
│  • Podcasts (commute)  │  • Integration with Jira    │
│  • Lenny's Newsletter  │  • Team plan pricing        │
│  • Twitter/X           │  • ROI calculator           │
└──────────────────────────────────────────────────────┘

Building a Persona Step-by-Step

Step 1: Research

Start with data, not assumptions.

# Market demographics
belt app run tavily/search-assistant --input '{
  "query": "product manager salary demographics 2024 survey report"
}'

# Pain points and challenges
belt app run exa/search --input '{
  "query": "biggest challenges facing product managers SaaS companies"
}'

# Tool usage patterns
belt app run tavily/search-assistant --input '{
  "query": "most popular tools product managers use 2024 survey"
}'

# Content consumption habits
belt app run exa/answer --input '{
  "question": "Where do product managers get their industry news and professional development?"
}'

Step 2: Demographics

Use ranges, not exact values. Personas represent a segment, not one person.

Field	Format	Example
Age range	X-Y	30-38
Income range	$X-$Y	$120,000-$160,000
Education	Common degrees	BS Computer Science, MBA
Location	Region/type	Urban US, major tech hubs
Job title	Role level	Senior PM, Product Lead
Company size	Range	50-500 employees
Industry	Sector	B2B SaaS

Step 3: Psychographics

What they think, value, and believe.

Category	Questions to Answer
Values	What matters most to them professionally?
Attitudes	How do they feel about their industry's direction?
Motivations	What drives them at work?
Personality	Analytical vs intuitive? Leader vs collaborator?
Interests	What do they read/watch/listen to professionally?
Lifestyle	Work-life balance preference? Remote/hybrid/office?

Step 4: Goals

What they're trying to achieve (both professional and personal).

Professional:
- Ship features faster with fewer meetings
- Make data-driven decisions (not gut feelings)
- Get promoted to Director of Product within 2 years
- Build a more autonomous product team

Personal:
- Leave work by 6pm more often
- Be seen as a strategic leader, not a ticket manager
- Stay current with industry trends without information overload

Step 5: Pain Points

Quantify whenever possible. Vague pain = vague persona.

❌ "Has trouble with reporting"
✅ "Spends 15 hours per week creating manual reports for 4 different stakeholders"

❌ "Too many tools"
✅ "Uses 8 different tools daily (Jira, Slack, Notion, Figma, Analytics, Sheets, Docs, Email) with no unified view"

❌ "Meetings are a problem"
✅ "Averages 6 hours of meetings per day, leaving only 2 hours for deep work"

Step 6: Jobs-to-be-Done (JTBD)

Three types of jobs:

Job Type	Description	Example
Functional	The task they need to accomplish	"Prioritize the product backlog based on customer impact data"
Emotional	How they want to feel	"Feel confident presenting to the exec team"
Social	How they want to be perceived	"Be seen as the person who makes data-driven decisions"

Step 7: Buying Process

Stage	Behavior
Awareness	Reads blog posts, sees peer recommendations on LinkedIn
Consideration	Compares 3-4 tools, reads G2/Capterra reviews, asks in Slack communities
Decision	Requests demo, needs IT/security approval, evaluates team pricing
Influencers	Engineering lead, VP of Product, CFO (for budget)
Objections	"Will my team actually adopt it?", "Does it integrate with Jira?"
Trigger event	New quarter with aggressive goals, new VP demanding better reporting

Step 8: Generate Avatar

# Match demographics: age, gender, ethnicity, professional context
belt app run falai/flux-dev-lora --input '{
  "prompt": "professional headshot photograph of a 34-year-old Asian American woman, product manager, warm confident smile, modern tech office background, natural lighting, wearing smart casual blouse, realistic portrait photography, sharp focus",
  "width": 1024,
  "height": 1024
}'

Avatar tips:

• Match the age range, ethnicity representation, and professional context
• Use "professional headshot photograph" for realistic results
• Friendly, approachable expression (not stock-photo-stiff)
• Background suggests their work environment
• Business casual or industry-appropriate attire

The Anti-Persona

Equally important: who is NOT your customer.

ANTI-PERSONA: "Enterprise Earl"
- CTO at a 5,000+ person enterprise
- Needs SOC 2, HIPAA, on-premise deployment
- 18-month procurement cycles
- Wants white-glove onboarding and dedicated CSM
- WHY NOT: Our product is self-serve SaaS for SMB/mid-market.
  Enterprise needs would require 2+ years of product investment.

Anti-personas prevent wasted effort on customers you can't serve.

Multiple Personas

Most products have 2-4 personas. More than 4 = too many to serve well.

Priority	Persona	Role
Primary	The main user and buyer	Who you optimize for
Secondary	Influences the buying decision	Who you need to convince
Tertiary	Uses the product occasionally	Who you support, not target

Validation

Personas based on assumptions are fiction. Validate with:

Method	What You Learn
Customer interviews (5-10)	Real language, real pain points
Support ticket analysis	Actual problems, not assumed ones
Analytics data	Actual behavior, not reported behavior
Survey (50+ responses)	Quantified patterns across segments
Sales call recordings	Objections, buying triggers, language

Common Mistakes

Mistake	Problem	Fix
Based on assumptions	Fiction, not research	Start with data
Too many personas (6+)	Can't serve everyone well	Max 3-4
Vague pain points	Not actionable	Quantify everything
Demographics only	Misses motivations and behavior	Add psychographics, JTBD
Never updated	Becomes outdated	Review quarterly
No anti-persona	Wasted effort on wrong customers	Define who you're NOT for
Single persona for all	Different users have different needs	Primary/secondary/tertiary

Related Skills

npx skills add inference-sh/skills@web-search
npx skills add inference-sh/skills@ai-image-generation
npx skills add inference-sh/skills@prompt-engineering

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