Customer Persona Create data-backed customer personas with research and visuals via inference.sh CLI. Quick Start Requires inference.sh CLI ( infsh ). Get installation instructions: npx skills add inference-sh/skills@agent-tools 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 }' 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