mediar-ai/customer-persona

Customer Persona

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

Quick Start

curl -fsSL https://cli.inference.sh | sh && infsh login

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

# Generate a persona avatar
infsh 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
}'

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

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

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

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

# Content consumption habits
infsh 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
infsh 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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