Product-Market Fit Assessment - AiPro Institute\u2122 Prompt Library<\/title>\n <style>\n * {\n margin: 0;\n padding: 0;\n box-sizing: border-box;\n }\n\n body {\n font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, sans-serif;\n background: #ffffff;\n padding: 2rem;\n line-height: 1.6;\n }\n\n .container {\n max-width: 900px;\n margin: 0 auto;\n }\n\n .header {\n text-align: center;\n margin-bottom: 2rem;\n }\n\n .header h1 {\n font-size: 2.5rem;\n background: linear-gradient(135deg, #7c3aed 0%, #3b82f6 100%);\n -webkit-background-clip: text;\n -webkit-text-fill-color: transparent;\n background-clip: text;\n font-weight: 800;\n margin-bottom: 0.5rem;\n }\n\n .card {\n background: white;\n border-radius: 16px;\n box-shadow: 0 20px 60px rgba(0,0,0,0.3);\n overflow: hidden;\n }\n\n .card-header {\n background: linear-gradient(135deg, #7c3aed 0%, #3b82f6 100%);\n color: white;\n padding: 2rem;\n }\n\n .card-header h2 {\n font-size: 2rem;\n 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font-size: 2rem;\n }\n\n .card-header h2 {\n font-size: 1.5rem;\n }\n\n .card-body {\n padding: 1.5rem;\n }\n\n .stats-footer {\n flex-direction: column;\n gap: 1.5rem;\n }\n\n .section-header {\n flex-direction: column;\n align-items: flex-start;\n gap: 1rem;\n }\n\n .chain-step {\n padding-left: 1.5rem;\n }\n\n .step-number {\n position: relative;\n left: 0;\n top: 0;\n margin-bottom: 1rem;\n }\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"container\">\n <div class=\"header\">\n <h1>AiPro Institute\u2122 Prompt Library<\/h1>\n <\/div>\n\n <div class=\"card\">\n <div class=\"card-header\">\n <h2>Product-Market Fit Assessment<\/h2>\n <p>Measure and optimize the alignment between your product and customer needs with quantitative metrics and qualitative insights<\/p>\n \n <div class=\"meta-badges\">\n <span class=\"badge badge-skill-intermediate\">Intermediate<\/span>\n <span class=\"badge badge-time\">15-25 minutes<\/span>\n <span class=\"badge badge-cost\">~650 tokens<\/span>\n <span class=\"badge badge-format\">Assessment Framework<\/span>\n <\/div>\n\n <div class=\"tool-compatibility\">\n <h3>Tool Compatibility<\/h3>\n <div class=\"tool-badges\">\n <span class=\"tool-badge model-agnostic\">Model Agnostic<\/span>\n <span class=\"tool-badge\">GPT-4o<\/span>\n <span class=\"tool-badge\">Claude 3.5<\/span>\n <span class=\"tool-badge\">Gemini 2.5<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"card-body\">\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">The Prompt<\/h3>\n <button class=\"copy-button\" onclick=\"copyPrompt()\">\ud83d\udccb Copy Prompt<\/button>\n <\/div>\n \n <div class=\"prompt-box\" id=\"promptText\">You are a product-market fit (PMF) specialist with expertise in customer development, metrics analysis, and product strategy. Conduct a comprehensive assessment to determine if your product has achieved product-market fit and identify gaps to address.\n\n<strong>PRODUCT CONTEXT:<\/strong>\nProduct Name: <span class=\"placeholder\">[PRODUCT_NAME]<\/span>\nProduct Category: <span class=\"placeholder\">[CATEGORY]<\/span> (e.g., SaaS, Mobile App, Hardware, Marketplace, DTC)\nTarget Customer: <span class=\"placeholder\">[TARGET_ICP]<\/span> (describe ideal customer profile)\nCore Value Proposition: <span class=\"placeholder\">[VALUE_PROP]<\/span> (what problem you solve, how)\nStage: <span class=\"placeholder\">[STAGE]<\/span> (Pre-launch, Early traction 0-100 customers, Growth 100-1000, Scale 1000+)\nCurrent Metrics: <span class=\"placeholder\">[KEY_METRICS]<\/span> (Users, revenue, retention, NPS, etc.)\n\n<strong>PRODUCT-MARKET FIT ASSESSMENT FRAMEWORK:<\/strong>\n\n<strong>1. QUANTITATIVE PMF SIGNALS<\/strong>\n Measure these key indicators:\n \n <strong>A. The Sean Ellis Test (PMF Litmus Test)<\/strong>\n \u2022 Survey Question: \"How would you feel if you could no longer use [PRODUCT]?\"\n - Very disappointed\n - Somewhat disappointed\n - Not disappointed\n \u2022 PMF Threshold: \u226540% answering \"Very disappointed\" = strong PMF signal\n \u2022 Your Score: <span class=\"placeholder\">[X%]<\/span> very disappointed\n \u2022 Interpretation: \n - 40%+: Strong PMF, focus on growth\n - 25-40%: Moderate PMF, optimize product\n - <25%: Weak PMF, pivot or iterate core offering\n \n <strong>B. Retention Cohort Analysis<\/strong>\n \u2022 Cohort Retention Curves: Do they flatten or decay to zero?\n - Flattening curve (plateau above 0%) = PMF signal (users stick around)\n - Decaying to 0% = No PMF (everyone churns eventually)\n \u2022 Month 1 \u2192 Month 3 \u2192 Month 6 Retention Rates\n \u2022 Current Retention: <span class=\"placeholder\">[RETENTION_DATA]<\/span>\n \u2022 Benchmark: Best-in-class SaaS = 90% Month 1, 70% Month 3, 60% Month 6\n \n <strong>C. Organic Growth Rate<\/strong>\n \u2022 Word-of-Mouth Growth: % of new users from referrals\/organic vs. paid\n \u2022 Viral Coefficient: How many new users does each user bring? (K-factor)\n \u2022 Target: K > 1.0 = viral growth, K = 0.5-1.0 = strong WOM\n \u2022 Your K-factor: <span class=\"placeholder\">[K_VALUE]<\/span>\n \n <strong>D. Net Promoter Score (NPS)<\/strong>\n \u2022 NPS Question: \"On 0-10, how likely are you to recommend [PRODUCT] to a colleague\/friend?\"\n - Promoters (9-10): Subtract Detractors (0-6) = NPS\n \u2022 Your NPS: <span class=\"placeholder\">[NPS_SCORE]<\/span>\n \u2022 Benchmark: NPS >50 = Excellent, 30-50 = Good, <30 >E. Revenue & Unit Economics<\/strong>\n \u2022 LTV:CAC Ratio: Lifetime Value \/ Customer Acquisition Cost\n - Target: 3:1 or higher = sustainable economics\n - Your Ratio: <span class=\"placeholder\">[LTV_CAC_RATIO]<\/span>\n \u2022 CAC Payback Period: Months to recover acquisition cost\n - Target: <12 months\n - your payback: <span class=\"placeholder\">[PAYBACK_MONTHS]<\/span> months\n \u2022 Gross Margin: Revenue - COGS \/ Revenue\n - Target: 70%+ for SaaS, 40%+ for e-commerce\n - Your Margin: <span class=\"placeholder\">[GROSS_MARGIN]<\/span>%\n \n <strong>F. Usage Intensity & Engagement<\/strong>\n \u2022 DAU\/MAU Ratio: Daily Active Users \/ Monthly Active Users\n - Target: 20%+ = habit-forming product\n - Your DAU\/MAU: <span class=\"placeholder\">[DAU_MAU]<\/span>%\n \u2022 Feature Adoption: % of users using core features weekly\n \u2022 Session Frequency: How often do users return? (daily, weekly, monthly)\n \u2022 Time Spent: Average session duration \/ time in product\n\n<strong>2. QUALITATIVE PMF SIGNALS<\/strong>\n \n <strong>A. Customer Interview Insights<\/strong>\n Conduct 15-20 customer interviews exploring:\n \u2022 Problem Recognition: Do they deeply feel the pain you're solving?\n - \"On a scale of 1-10, how painful is [PROBLEM] for you?\" (Target: 8+)\n - \"What have you tried to solve this before?\" (workarounds = real pain)\n \u2022 Solution Value: Does your product solve it significantly better?\n - \"How much better is our product vs. your previous solution?\" (Target: 10x better)\n - \"What would you do if we shut down tomorrow?\" (scramble to find alternative = PMF)\n \u2022 Willingness to Pay: Do they value it enough to pay?\n - \"At what price would this be too expensive? Too cheap? About right?\" (Van Westendorp pricing)\n - \"Have you recommended us to others? Why or why not?\"\n \u2022 Must-Have vs. Nice-to-Have: Is your product a vitamin (optional) or painkiller (essential)?\n - \"Could you do your job\/life without this product?\" (No = must-have)\n \n <strong>B. Customer Segmentation Analysis<\/strong>\n Identify who loves your product most:\n \u2022 Power Users: Which segment has highest engagement, retention, NPS?\n \u2022 Best-Fit ICP: Company size, industry, role, use case where you win\n \u2022 Poor-Fit Segments: Who churns fast or doesn't engage? Stop targeting them\n \u2022 Ideal Customer Profile (ICP) Definition:\n - Demographics\/Firmographics\n - Behavioral patterns\n - Psychographic traits (values, pain points, goals)\n \n <strong>C. Competitive Positioning<\/strong>\n \u2022 Win\/Loss Analysis: Why do customers choose you vs. competitors?\n - Wins: What differentiation resonates? (features, price, brand, UX)\n - Losses: Where do competitors beat you?\n \u2022 Switching Behavior: Are customers switching TO you from competitors? (strong signal)\n \u2022 Switching Friction: Would customers switch AWAY if competitor caught up? (weak moat)\n\n<strong>3. PMF SCORING FRAMEWORK<\/strong>\n Rate your product on each dimension (1-10 scale):\n \n \u2022 <strong>Customer Love<\/strong>: Sean Ellis score \u226540%, NPS \u226550\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Retention Strength<\/strong>: Cohorts flatten above 60% retention\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Organic Growth<\/strong>: 30%+ users from WOM\/referral, K-factor >0.5\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Unit Economics<\/strong>: LTV:CAC \u22653:1, payback \u226412 months\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Engagement Depth<\/strong>: DAU\/MAU \u226520%, high feature adoption\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Problem-Solution Fit<\/strong>: Customers rate pain 8+\/10, solution 10x better\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n <strong>Overall PMF Score:<\/strong> Average of 6 dimensions = <span class=\"placeholder\">[TOTAL_SCORE]<\/span>\/10\n \n <strong>PMF Stage Assessment:<\/strong>\n \u2022 8-10: Strong PMF \u2014 Scale aggressively, invest in growth\n \u2022 6-8: Moderate PMF \u2014 Optimize product, improve retention, refine ICP\n \u2022 4-6: Weak PMF \u2014 Iterate core product, talk to more customers, consider pivot\n \u2022 0-4: No PMF \u2014 Major pivot needed or shut down\n\n<strong>4. GAP ANALYSIS & PRIORITIZATION<\/strong>\n For each dimension scoring below 8, identify:\n \n <strong>A. Root Cause Analysis<\/strong>\n \u2022 Why is this dimension underperforming?\n \u2022 What customer feedback or data supports this?\n \u2022 What assumptions were wrong?\n \n <strong>B. Improvement Hypotheses<\/strong>\n \u2022 Hypothesis 1: \"If we [CHANGE], then [METRIC] will improve because [REASON]\"\n \u2022 Hypothesis 2: [...]\n \u2022 Hypothesis 3: [...]\n \n <strong>C. Prioritization Matrix<\/strong>\n For each hypothesis, score:\n \u2022 Impact: How much will this move the PMF needle? (1-10)\n \u2022 Confidence: How sure are we this will work? (1-10)\n \u2022 Effort: How much work required? (1-10, lower = easier)\n \u2022 Priority Score: (Impact \u00d7 Confidence) \/ Effort\n \n Sort by Priority Score \u2014 tackle highest-scoring initiatives first\n\n<strong>5. PMF IMPROVEMENT ROADMAP<\/strong>\n \n <strong>Immediate Actions (Next 30 Days)<\/strong>\n \u2022 Customer Research: Interview 10-15 users, especially churned users\n \u2022 Metric Instrumentation: Ensure you're tracking all key PMF metrics\n \u2022 Quick Wins: Top 3 highest-priority, lowest-effort improvements\n \n <strong>Short-Term (30-90 Days)<\/strong>\n \u2022 Product Iterations: Implement top 5 prioritized hypotheses\n \u2022 ICP Refinement: Double down on best-fit segments, de-prioritize poor fits\n \u2022 Onboarding Optimization: Improve activation rate (% of signups becoming active users)\n \n <strong>Medium-Term (3-6 Months)<\/strong>\n \u2022 Core Product Evolution: Major feature builds or pivots if needed\n \u2022 Retention Programs: Engagement loops, notification systems, community\n \u2022 Economic Optimization: Pricing changes, CAC reduction, LTV expansion\n \n <strong>Success Metrics (6-Month Targets)<\/strong>\n \u2022 Sean Ellis score: From <span class=\"placeholder\">[CURRENT]<\/span>% \u2192 <span class=\"placeholder\">[TARGET]<\/span>%\n \u2022 Retention: From <span class=\"placeholder\">[CURRENT]<\/span>% \u2192 <span class=\"placeholder\">[TARGET]<\/span>%\n \u2022 NPS: From <span class=\"placeholder\">[CURRENT]<\/span> \u2192 <span class=\"placeholder\">[TARGET]<\/span>\n \u2022 LTV:CAC: From <span class=\"placeholder\">[CURRENT]<\/span> \u2192 <span class=\"placeholder\">[TARGET]<\/span>\n\n<strong>6. PMF DECISION FRAMEWORK<\/strong>\n \n <strong>If Strong PMF (8-10 score):<\/strong>\n \u2022 Decision: SCALE \u2014 Pour fuel on the fire\n \u2022 Focus: Growth marketing, sales team scaling, infrastructure\n \u2022 Investment: Fundraising for growth, aggressive hiring\n \n <strong>If Moderate PMF (6-8 score):<\/strong>\n \u2022 Decision: OPTIMIZE \u2014 Improve before scaling\n \u2022 Focus: Product iteration, retention optimization, ICP refinement\n \u2022 Investment: Moderate, focus on efficiency over growth\n \n <strong>If Weak\/No PMF (<6 score):<\/strong>\n \u2022 Decision: PIVOT or ITERATE \u2014 Don't scale prematurely\n \u2022 Focus: Customer discovery, product experiments, hypothesis testing\n \u2022 Investment: Minimal, extend runway, avoid premature scaling\n \u2022 Pivot Triggers: If no improvement after 2-3 iteration cycles (6 months), consider major pivot or shutdown\n\n<strong>OUTPUT FORMAT:<\/strong>\n\u2022 Executive Summary: Overall PMF score, stage, key strengths\/gaps\n\u2022 Quantitative Dashboard: All 6 PMF metrics with scores and benchmarks\n\u2022 Qualitative Insights: Customer interview themes, positioning analysis\n\u2022 Gap Analysis: Root causes, hypotheses, prioritization matrix\n\u2022 90-Day Action Plan: Specific initiatives with owners, timelines, success metrics\n\u2022 Decision Recommendation: Scale \/ Optimize \/ Pivot with clear rationale\n\n<strong>TONE & STYLE:<\/strong>\n\u2022 Data-driven and evidence-based\n\u2022 Honest and unbiased (acknowledge weaknesses)\n\u2022 Action-oriented (focus on what to do next)\n\u2022 Strategic (connect metrics to business outcomes)<\/div>\n <\/div>\n\n <div class=\"tip-box\">\n <strong>Tip:<\/strong> Fill in the <span class=\"placeholder\">[orange placeholders]<\/span> with your product metrics and context before using this prompt.\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">The Logic (Why This Prompt Works)<\/h3>\n <\/div>\n \n <div class=\"logic-grid\">\n <div class=\"logic-item\">\n <h4>Sean Ellis 40% Test (Validated PMF Benchmark)<\/h4>\n <p>The prompt uses Sean Ellis's empirically validated PMF threshold: \u226540% of users answering \"very disappointed\" if product disappeared. This simple question correlates strongly with sustainable growth\u2014companies above 40% scale successfully; those below struggle. Ellis tested this across 100+ startups and found it's the most predictive single PMF metric, better than NPS or retention alone.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Quantitative + Qualitative Dual Assessment<\/h4>\n <p>The framework balances hard metrics (retention curves, LTV:CAC, NPS) with soft signals (customer interviews, problem intensity, must-have perception). Metrics can mislead\u2014high retention with low growth could mean small niche; customer interviews reveal if you're solving a hair-on-fire problem or nice-to-have. Instagram had both before scaling; Color (photo-sharing app) had growth but weak qualitative signals and failed.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Retention Curve Flattening (True North PMF Signal)<\/h4>\n <p>The prompt emphasizes retention curve shape over absolute percentages. A curve that flattens (plateaus above 0%) indicates a core group finding lasting value\u2014the foundation for growth. A curve decaying to 0% means everyone eventually leaves\u2014no PMF regardless of initial virality. Facebook's curves flattened at colleges before expanding; many viral apps (Yo, Clubhouse) showed decay curves and couldn't sustain.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>ICP Segmentation (Who Loves You Most?)<\/h4>\n <p>Rather than treating all customers equally, the prompt requires identifying which segments show strongest PMF signals (highest retention, NPS, engagement). Early PMF often exists in narrow segments\u2014expanding too broadly before deeply owning a niche dilutes focus. Slack found PMF with tech teams before expanding to non-tech; targeting everyone initially would have failed. This lens prevents \"average\" PMF that masks strong fit in one segment and poor fit elsewhere.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Unit Economics Constraint (Sustainable PMF)<\/h4>\n <p>The framework includes LTV:CAC \u22653:1 as a PMF requirement\u2014product-market fit without economic fit isn't sustainable. Many products achieve customer love but can't acquire customers profitably (CAC too high) or retain them long enough (LTV too low). WeWork had customer demand but unit economics never worked at scale. This economic lens prevents chasing PMF metrics while building unprofitable businesses.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Stage-Specific Decision Framework (Scale\/Optimize\/Pivot)<\/h4>\n <p>The prompt maps PMF score (8-10 strong, 6-8 moderate, <6 weak) to clear strategic actions\u2014preventing premature scaling (the #1 startup killer per CB Insights). Strong PMF = scale aggressively; moderate = optimize before scaling; weak = iterate or pivot. This disciplined gate-keeping saved companies like Segment (pivoted 3 times before PMF) and prevented waste at companies that scaled too early (e.g., Quibi's $1.75B loss despite weak PMF signals).<\/p>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">Output Preview<\/h3>\n <\/div>\n \n <div class=\"preview-section\">\n <div class=\"preview-title\">Example Result:<\/div>\n <div class=\"preview-content\">\n <p><strong>PRODUCT: Team Communication SaaS (Slack-like product, 6 months post-launch)<\/strong><\/p>\n\n <h4 class=\"subsection-title\">EXECUTIVE SUMMARY<\/h4>\n <p><strong>Overall PMF Score: 6.2\/10<\/strong> \u2014 Moderate PMF with strong signals in specific segments but gaps in retention and economics<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>Assessment: OPTIMIZE<\/strong> \u2014 Don't scale yet. Focus on improving retention, narrowing ICP, and optimizing unit economics before growth investment.<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>Key Strengths:<\/strong><\/p>\n <ul>\n <li>Strong customer love in tech\/startup segment (Sean Ellis: 48% \"very disappointed\" among 0-50 employee companies)<\/li>\n <li>Viral growth within teams (K-factor: 0.7 \u2014 each user brings 0.7 new users)<\/li>\n <li>High engagement for power users (DAU\/MAU: 35% for tech teams)<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>Critical Gaps:<\/strong><\/p>\n <ul>\n <li>Poor retention in enterprise segment (Month 3 retention: 32% vs. 78% in startups)<\/li>\n <li>Weak unit economics (LTV:CAC = 1.8:1, need 3:1+)<\/li>\n <li>Low NPS in non-tech industries (NPS: 12 vs. 62 in tech)<\/li>\n <\/ul>\n\n <h4 class=\"subsection-title\">QUANTITATIVE PMF METRICS<\/h4>\n <p><strong>1. Sean Ellis Test:<\/strong> 37% overall \"very disappointed\" (Below 40% threshold)<\/p>\n <ul>\n <li>Tech startups (0-50 employees): 48% \u2713 Strong PMF<\/li>\n <li>Enterprise (500+ employees): 18% \u2717 No PMF<\/li>\n <li>Non-tech SMBs: 22% \u2717 Weak PMF<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Insight:<\/strong> Clear PMF in tech startup segment; diluted by poor fit elsewhere<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>2. Retention Curves:<\/strong> Mixed signals<\/p>\n <ul>\n <li>Tech startups: Month 1: 85% \u2192 Month 3: 78% \u2192 Month 6: 74% (Flattening \u2713)<\/li>\n <li>Enterprise: Month 1: 65% \u2192 Month 3: 32% \u2192 Month 6: 12% (Decaying \u2717)<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Insight:<\/strong> Retention curves flatten only in tech startup segment\u2014PMF exists there<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>3. Organic Growth:<\/strong><\/p>\n <ul>\n <li>Referral\/Organic: 62% of new users (Strong WOM)<\/li>\n <li>K-factor: 0.7 (Good, approaching viral threshold of 1.0)<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>4. NPS:<\/strong> 38 overall (Mediocre)<\/p>\n <ul>\n <li>Tech segment: 62 (Excellent)<\/li>\n <li>Non-tech: 12 (Poor)<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>5. Unit Economics:<\/strong> Weak<\/p>\n <ul>\n <li>CAC: $2,400 (blend of low-touch PLG + high-touch sales)<\/li>\n <li>LTV: $4,300 (24-month avg retention, $180\/month ARPU)<\/li>\n <li>LTV:CAC: 1.8:1 \u2717 (Need 3:1+)<\/li>\n <li>Payback: 14 months \u2717 (Target: <12 months)<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Issue:<\/strong> Acquiring wrong customers (enterprise) drives CAC up; they churn fast, killing LTV<\/p>\n\n <h4 class=\"subsection-title\">PMF SCORE BREAKDOWN<\/h4>\n <ul>\n <li><strong>Customer Love:<\/strong> 6\/10 (37% Sean Ellis score, dragged down by non-PMF segments)<\/li>\n <li><strong>Retention Strength:<\/strong> 7\/10 (Strong in tech, weak elsewhere)<\/li>\n <li><strong>Organic Growth:<\/strong> 8\/10 (62% organic, K=0.7)<\/li>\n <li><strong>Unit Economics:<\/strong> 4\/10 (LTV:CAC too low, payback too long)<\/li>\n <li><strong>Engagement Depth:<\/strong> 7\/10 (DAU\/MAU 35% in best-fit segment)<\/li>\n <li><strong>Problem-Solution Fit:<\/strong> 7\/10 (Tech users rate pain 9\/10, love solution)<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Overall:<\/strong> 6.2\/10 = Moderate PMF<\/p>\n\n <h4 class=\"subsection-title\">GAP ANALYSIS & PRIORITIZATION<\/h4>\n <p><strong>Gap #1: Unit Economics (4\/10 score)<\/strong><\/p>\n <p><strong>Root Cause:<\/strong> Targeting enterprise customers who don't have PMF\u2014high CAC (sales-heavy), low retention (wrong ICP), kills LTV:CAC ratio<\/p>\n <p><strong>Hypotheses:<\/strong><\/p>\n <ul>\n <li><strong>H1:<\/strong> If we stop targeting enterprise (500+ employees) and focus only on tech startups (0-200 employees), CAC will drop 40% (less sales effort) and LTV will increase 60% (better retention) \u2192 LTV:CAC improves from 1.8 to 4.2<\/li>\n <li><strong>H2:<\/strong> If we move enterprise to pure PLG (no sales assist), CAC drops but conversion rate may drop\u2014test with subset<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Priority Score:<\/strong> H1 = (9 impact \u00d7 8 confidence) \/ 3 effort = 24 \u2713 High priority<\/p>\n\n <h4 class=\"subsection-title\">90-DAY ACTION PLAN<\/h4>\n <p><strong>Immediate (Days 1-30):<\/strong><\/p>\n <ul>\n <li>Pause all enterprise outbound sales\/marketing (save $40k\/month burn)<\/li>\n <li>Interview 15 churned enterprise users: Why did you leave? What were you hoping for?<\/li>\n <li>Re-instrument analytics to segment all metrics by company size\/industry<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>Short-Term (Days 31-90):<\/strong><\/p>\n <ul>\n <li>Launch \"Startups Only\" positioning\u2014rebrand marketing to tech\/startup segment<\/li>\n <li>Optimize onboarding for tech teams (developer integrations, GitHub\/Jira\/Figma)<\/li>\n <li>Build viral loop features (invite teammates, public community channels)<\/li>\n <li>Target: Increase tech startup Sean Ellis score from 48% \u2192 55%+<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>Success Metrics (90-day targets):<\/strong><\/p>\n <ul>\n <li>Sean Ellis (tech startups only): 48% \u2192 55%<\/li>\n <li>Overall LTV:CAC: 1.8 \u2192 3.5 (by focusing on high-retention segment)<\/li>\n <li>CAC: $2,400 \u2192 $1,600 (less enterprise sales waste)<\/li>\n <li>Month 3 retention: 78% \u2192 85% (optimize tech segment onboarding)<\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">Chain Strategy (Advanced Workflow)<\/h3>\n <\/div>\n <p style=\"margin-bottom: 1.5rem;\">For best results, use this 3-step sequential prompting strategy:<\/p>\n \n <div class=\"chain-steps\">\n <div class=\"chain-step\">\n <div class=\"step-number\">1<\/div>\n <h4>Quantitative Metric Collection & Segmentation<\/h4>\n <p><strong>Goal:<\/strong> Gather all PMF metrics and segment by customer type<\/p>\n <p><strong>Prompt:<\/strong> \"Extract and calculate all PMF metrics from our data for <span class=\"placeholder\">[PRODUCT]<\/span>. Required metrics: (1) Sean Ellis score: Survey 100+ users with 'How would you feel if you could no longer use [PRODUCT]?' Calculate % answering 'Very disappointed'. Segment by: customer size, industry, use case, tenure. (2) Retention cohorts: For each monthly signup cohort (last 12 months), calculate Month 1, Month 3, Month 6 retention rates. Plot curves\u2014do they flatten or decay? (3) NPS: Calculate Net Promoter Score overall and by segment. (4) Organic growth: % of new signups from referral\/organic vs. paid channels. (5) Unit economics: CAC (total sales\/marketing spend \u00f7 new customers), LTV (ARPU \u00d7 avg customer lifetime), LTV:CAC ratio, payback period in months. (6) Engagement: DAU\/MAU ratio, feature adoption rates, session frequency. Organize output as: Overall scores + Segment breakdown table (show which segments have strong vs. weak PMF signals). Highlight: Best-fit segment (highest scores) vs. Worst-fit segment (lowest scores).\"<\/p>\n <p><strong>Expected Output:<\/strong> PMF metrics dashboard segmented by customer type, with identification of high-PMF and low-PMF segments.<\/p>\n <\/div>\n \n <div class=\"chain-step\">\n <div class=\"step-number\">2<\/div>\n <h4>Qualitative Customer Interview Synthesis<\/h4>\n <p><strong>Goal:<\/strong> Understand why metrics are what they are through customer voice<\/p>\n <p><strong>Prompt:<\/strong> \"Conduct or analyze 15-20 customer interviews to understand PMF qualitatively. Interview split: 10 power users (high engagement, long tenure), 5 churned users (canceled in last 90 days). Interview guide: (1) Problem intensity: 'On 1-10, how painful was [PROBLEM] before our product? What did you try before us? How much time\/money did it cost you?' (2) Solution value: 'How much better is our product vs. your previous solution? 2x? 5x? 10x? What would you do if we disappeared tomorrow?' (3) Must-have vs. nice-to-have: 'Could you do your job without this product? What would break?' (4) Willingness to pay: 'At what price is this too expensive? Too cheap (seems low quality)? Just right?' (5) Recommendation behavior: 'Have you told others about us? Who? Why or why not?' (6) For churned users: 'Why did you leave? What were we missing? What would bring you back?' Synthesize findings into: (a) Themes from power users (what drives love), (b) Themes from churned users (why we lose them), (c) Problem intensity score (avg 1-10), (d) Solution superiority (2x, 5x, 10x better?), (e) Must-have % (% saying they couldn't live without it). Compare themes across segments identified in Step 1.\"<\/p>\n <p><strong>Expected Output:<\/strong> Qualitative PMF report with customer quotes, thematic analysis, and alignment (or misalignment) with quantitative findings.<\/p>\n <\/div>\n \n <div class=\"chain-step\">\n <div class=\"step-number\">3<\/div>\n <h4>Gap Prioritization & Roadmap Development<\/h4>\n <p><strong>Goal:<\/strong> Translate insights into prioritized action plan<\/p>\n <p><strong>Prompt:<\/strong> \"Based on PMF metrics [INSERT STEP 1 DATA] and customer insights [INSERT STEP 2 THEMES], create a prioritized PMF improvement roadmap. Steps: (1) Identify gaps: Which PMF dimensions scored <8>\n <p><strong>Expected Output:<\/strong> Action-ready PMF improvement roadmap with prioritized initiatives, owners, timelines, success metrics, and decision gates.<\/p>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">Human-in-the-Loop Refinement Tips<\/h3>\n <\/div>\n <p style=\"margin-bottom: 1.5rem;\">Enhance your results with these follow-up prompts:<\/p>\n \n <div class=\"hitl-grid\">\n <div class=\"hitl-item\">\n <h4>\ud83d\udcca Cohort Retention Deep Dive<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Analyze retention cohorts in detail for <span class=\"placeholder\">[PRODUCT]<\/span>. For each monthly cohort from the last 12 months: (1) Plot retention curve showing Week 1, Week 2, Week 4, Month 2, Month 3, Month 6, Month 12 retention. (2) Identify: At what point do curves start to flatten? What % retention do they plateau at? (3) Compare: Early cohorts (12 months ago) vs. recent cohorts (last 3 months)\u2014are recent cohorts retaining better (product improving) or worse (lower quality users)? (4) Segment analysis: Plot separate retention curves for high-value vs. low-value customers, different acquisition channels (organic vs. paid), different personas\/use cases. (5) Leading indicators: What Week 1 behaviors predict Month 6 retention? (activation events, feature usage, invites sent). Identify: What's the 'aha moment' that correlates with long-term retention? (6) Churn analysis: For users who churned, when did they churn (Day 1, Week 1, Month 1, Month 3+)? Why? (survey churned users). Create: Retention curve dashboard, churn reason taxonomy, activation metric recommendation (what predicts retention).\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83c\udfaf ICP Refinement & Segmentation<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Refine our Ideal Customer Profile (ICP) based on PMF signals. Analysis: (1) Segment all customers by: company size, industry, role\/title, use case, geography, acquisition channel. (2) For each segment, calculate: Sean Ellis score, retention rate, NPS, LTV, CAC, engagement (DAU\/MAU), expansion revenue, referral rate. (3) Identify 'PMF segments': Which 2-3 segments score highest across all metrics? These are your best-fit customers. (4) Identify 'no-PMF segments': Which segments score lowest? These are poor fits\u2014stop targeting them. (5) Create detailed ICP profile for your #1 PMF segment: Demographics\/firmographics (company size, industry, revenue, employee count, tech stack), Psychographics (pain points, goals, values, buying behavior), Behavioral signals (what do they do before buying? what triggers purchase?), Where to find them (channels, communities, events). (6) Recommendation: What % of marketing\/sales resources should focus on PMF segments vs. others? Should we explicitly de-position from no-PMF segments (e.g., 'We're not for enterprise')? Provide: ICP document, segment prioritization matrix, GTM strategy recommendation (channels to double down, channels to cut).\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udcac Customer Interview Script & Analysis<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Create a detailed customer interview script for PMF assessment of <span class=\"placeholder\">[PRODUCT]<\/span>. Interview structure (45-60 min): (1) Intro (5 min): Explain purpose (learning, not selling), ask permission to record. (2) Context setting (10 min): What's your role? What does a typical day look like? What are your biggest challenges? (3) Pre-product state (10 min): Before our product, how did you handle [PROBLEM]? What tools\/workarounds? How much time\/money did it cost? On 1-10, how painful was this problem? What triggered you to look for a solution? (4) Product discovery & adoption (5 min): How did you find us? What made you try us? What were you skeptical about? (5) Current usage (15 min): How do you use our product? Walk me through your workflow. What do you love? What frustrates you? If we disappeared, what would you do? Could you do your job without us? (6) Value perception (5 min): How much better are we vs. previous solution? (2x, 5x, 10x?) At what price would this be: too expensive? too cheap (suspicious)? just right? (7) Recommendation (5 min): Have you recommended us? To whom? Why or why not? (8) Future needs (5 min): What's missing? What would make this a 10\/10 product for you? After 15-20 interviews: Synthesize themes, calculate avg pain score, identify must-have %, map feature requests by frequency, extract powerful quotes for case studies\/marketing. Provide: Full interview script with follow-up questions, synthesis template for organizing findings.\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udcc8 Growth Lever Identification<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Identify the highest-leverage growth opportunities for <span class=\"placeholder\">[PRODUCT]<\/span> given current PMF state. Analysis: (1) If PMF score is 6-8 (moderate): Growth levers ranked by priority: (a) Improve retention (cohort optimization\u2014get more users to 'aha moment'), (b) Expand within existing customers (upsell\/cross-sell to increase LTV), (c) Double down on best-fit ICP (narrow targeting for better unit economics), (d) Referral program (leverage existing love in PMF segments), (e) Delay heavy growth marketing until PMF improves to 8+. (2) If PMF score is 8-10 (strong): Growth levers ranked: (a) Scale paid acquisition in PMF segments (CAC is justified by high LTV), (b) Sales team scaling (outbound in high-PMF verticals), (c) Strategic partnerships (distribution deals, integrations), (d) International expansion (replicate playbook in new geos), (e) Platform plays (APIs, marketplace, ecosystem). (3) For each lever: Estimated impact on revenue (12-month projection), Required investment, Key risks, Success metrics to track. (4) Sequencing: What order to pull levers? Which are prerequisites for others? (5) Anti-patterns: What should we NOT do given our PMF stage? (e.g., don't scale paid ads if LTV:CAC <3:1). Provide: Prioritized growth lever roadmap with investment requirements and projected ROI.\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udd04 PMF Monitoring Dashboard Design<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Design a real-time PMF monitoring dashboard for <span class=\"placeholder\">[PRODUCT]<\/span>. Dashboard sections: (1) PMF Health Score (center): Overall score 0-10 calculated from 6 dimensions, color-coded (red <6, yellow 6-8, green 8-10). (2) Core metrics panel: Sean Ellis score (% 'very disappointed'), Month 3 retention rate, NPS, LTV:CAC ratio, DAU\/MAU, Organic growth %. Each with: current value, 30-day trend (\u2191\u2193), benchmark comparison, segment breakdown on hover. (3) Cohort retention curves: Visual chart showing retention curves for last 6 monthly cohorts\u2014check if flattening or decaying. (4) Segment performance matrix: Table showing all customer segments with PMF scores\u2014highlight best-fit (green) and poor-fit (red) segments. (5) Leading indicators: Week 1 activation rate, time to 'aha moment', referral rate\u2014these predict future retention. (6) Alert triggers: Red flags when: Sean Ellis drops below 35%, Month 3 retention declines >5% MoM, LTV:CAC drops below 2:1. (7) Qualitative summary: Latest customer interview themes, NPS verbatim comments, feature requests ranked by frequency. Refresh frequency: Core metrics (daily), cohort analysis (weekly), qualitative synthesis (monthly). Provide: Dashboard wireframe\/mockup, data source requirements (what needs to be tracked), KPI definitions, recommended review cadence (who reviews what, how often).\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udd00 Pivot Decision Framework<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Develop a structured pivot decision framework for <span class=\"placeholder\">[PRODUCT]<\/span> if PMF doesn't improve. Scenario: After 6 months of iteration, PMF score remains <6>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"stats-footer\">\n <div class=\"stat\">\n <div class=\"stat-value\">\u2b50 4.9<\/div>\n <div class=\"stat-label\">(823 ratings)<\/div>\n <\/div>\n <div class=\"stat\">\n <div class=\"stat-value\">\ud83d\udccb 15,234<\/div>\n <div class=\"stat-label\">copies<\/div>\n <\/div>\n <div class=\"stat\">\n <div class=\"stat-value\">\ud83d\udcac 672<\/div>\n <div class=\"stat-label\">reviews<\/div>\n <\/div>\n <\/div>\n <\/div>\n\n <script>\n function copyPrompt() {\n const promptText = document.getElementById('promptText').innerText;\n const button = document.querySelector('.copy-button');\n \n navigator.clipboard.writeText(promptText).then(() => {\n button.textContent = '\u2713 Copied!';\n button.classList.add('copied');\n \n setTimeout(() => {\n button.textContent = '\ud83d\udccb Copy Prompt';\n button.classList.remove('copied');\n }, 2000);\n });\n }\n <\/script>\n<\/body>\n<\/html>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Product-Market Fit Assessment – AiPro Institute\u2122 Prompt Library AiPro Institute\u2122 Prompt Library Product-Market Fit Assessment Measure and optimize the alignment between your product and customer needs with quantitative metrics and qualitative insights Intermediate 15-25 minutes ~650 tokens Assessment Framework Tool Compatibility Model Agnostic GPT-4o Claude 3.5 Gemini 2.5 The Prompt \ud83d\udccb Copy Prompt You are…<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[182],"tags":[],"class_list":["post-4781","post","type-post","status-publish","format-standard","hentry","category-market-strategy"],"acf":[],"_links":{"self":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts\/4781","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/comments?post=4781"}],"version-history":[{"count":4,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts\/4781\/revisions"}],"predecessor-version":[{"id":4819,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts\/4781\/revisions\/4819"}],"wp:attachment":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/media?parent=4781"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/categories?post=4781"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/tags?post=4781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

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.header h1 {\n font-size: 2rem;\n }\n\n .card-header h2 {\n font-size: 1.5rem;\n }\n\n .card-body {\n padding: 1.5rem;\n }\n\n .stats-footer {\n flex-direction: column;\n gap: 1.5rem;\n }\n\n .section-header {\n flex-direction: column;\n align-items: flex-start;\n gap: 1rem;\n }\n\n .chain-step {\n padding-left: 1.5rem;\n }\n\n .step-number {\n position: relative;\n left: 0;\n top: 0;\n margin-bottom: 1rem;\n }\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"container\">\n <div class=\"header\">\n <h1>AiPro Institute\u2122 Prompt Library<\/h1>\n <\/div>\n\n <div class=\"card\">\n <div class=\"card-header\">\n <h2>Product-Market Fit Assessment<\/h2>\n <p>Measure and optimize the alignment between your product and customer needs with quantitative metrics and qualitative insights<\/p>\n \n <div class=\"meta-badges\">\n <span class=\"badge badge-skill-intermediate\">Intermediate<\/span>\n <span class=\"badge badge-time\">15-25 minutes<\/span>\n <span class=\"badge badge-cost\">~650 tokens<\/span>\n <span class=\"badge badge-format\">Assessment Framework<\/span>\n <\/div>\n\n <div class=\"tool-compatibility\">\n <h3>Tool Compatibility<\/h3>\n <div class=\"tool-badges\">\n <span class=\"tool-badge model-agnostic\">Model Agnostic<\/span>\n <span class=\"tool-badge\">GPT-4o<\/span>\n <span class=\"tool-badge\">Claude 3.5<\/span>\n <span class=\"tool-badge\">Gemini 2.5<\/span>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"card-body\">\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">The Prompt<\/h3>\n <button class=\"copy-button\" onclick=\"copyPrompt()\">\ud83d\udccb Copy Prompt<\/button>\n <\/div>\n \n <div class=\"prompt-box\" id=\"promptText\">You are a product-market fit (PMF) specialist with expertise in customer development, metrics analysis, and product strategy. Conduct a comprehensive assessment to determine if your product has achieved product-market fit and identify gaps to address.\n\n<strong>PRODUCT CONTEXT:<\/strong>\nProduct Name: <span class=\"placeholder\">[PRODUCT_NAME]<\/span>\nProduct Category: <span class=\"placeholder\">[CATEGORY]<\/span> (e.g., SaaS, Mobile App, Hardware, Marketplace, DTC)\nTarget Customer: <span class=\"placeholder\">[TARGET_ICP]<\/span> (describe ideal customer profile)\nCore Value Proposition: <span class=\"placeholder\">[VALUE_PROP]<\/span> (what problem you solve, how)\nStage: <span class=\"placeholder\">[STAGE]<\/span> (Pre-launch, Early traction 0-100 customers, Growth 100-1000, Scale 1000+)\nCurrent Metrics: <span class=\"placeholder\">[KEY_METRICS]<\/span> (Users, revenue, retention, NPS, etc.)\n\n<strong>PRODUCT-MARKET FIT ASSESSMENT FRAMEWORK:<\/strong>\n\n<strong>1. QUANTITATIVE PMF SIGNALS<\/strong>\n Measure these key indicators:\n \n <strong>A. The Sean Ellis Test (PMF Litmus Test)<\/strong>\n \u2022 Survey Question: \"How would you feel if you could no longer use [PRODUCT]?\"\n - Very disappointed\n - Somewhat disappointed\n - Not disappointed\n \u2022 PMF Threshold: \u226540% answering \"Very disappointed\" = strong PMF signal\n \u2022 Your Score: <span class=\"placeholder\">[X%]<\/span> very disappointed\n \u2022 Interpretation: \n - 40%+: Strong PMF, focus on growth\n - 25-40%: Moderate PMF, optimize product\n - <25%: Weak PMF, pivot or iterate core offering\n \n <strong>B. Retention Cohort Analysis<\/strong>\n \u2022 Cohort Retention Curves: Do they flatten or decay to zero?\n - Flattening curve (plateau above 0%) = PMF signal (users stick around)\n - Decaying to 0% = No PMF (everyone churns eventually)\n \u2022 Month 1 \u2192 Month 3 \u2192 Month 6 Retention Rates\n \u2022 Current Retention: <span class=\"placeholder\">[RETENTION_DATA]<\/span>\n \u2022 Benchmark: Best-in-class SaaS = 90% Month 1, 70% Month 3, 60% Month 6\n \n <strong>C. Organic Growth Rate<\/strong>\n \u2022 Word-of-Mouth Growth: % of new users from referrals\/organic vs. paid\n \u2022 Viral Coefficient: How many new users does each user bring? (K-factor)\n \u2022 Target: K > 1.0 = viral growth, K = 0.5-1.0 = strong WOM\n \u2022 Your K-factor: <span class=\"placeholder\">[K_VALUE]<\/span>\n \n <strong>D. Net Promoter Score (NPS)<\/strong>\n \u2022 NPS Question: \"On 0-10, how likely are you to recommend [PRODUCT] to a colleague\/friend?\"\n - Promoters (9-10): Subtract Detractors (0-6) = NPS\n \u2022 Your NPS: <span class=\"placeholder\">[NPS_SCORE]<\/span>\n \u2022 Benchmark: NPS >50 = Excellent, 30-50 = Good, <30 >E. Revenue & Unit Economics<\/strong>\n \u2022 LTV:CAC Ratio: Lifetime Value \/ Customer Acquisition Cost\n - Target: 3:1 or higher = sustainable economics\n - Your Ratio: <span class=\"placeholder\">[LTV_CAC_RATIO]<\/span>\n \u2022 CAC Payback Period: Months to recover acquisition cost\n - Target: <12 months\n - your payback: <span class=\"placeholder\">[PAYBACK_MONTHS]<\/span> months\n \u2022 Gross Margin: Revenue - COGS \/ Revenue\n - Target: 70%+ for SaaS, 40%+ for e-commerce\n - Your Margin: <span class=\"placeholder\">[GROSS_MARGIN]<\/span>%\n \n <strong>F. Usage Intensity & Engagement<\/strong>\n \u2022 DAU\/MAU Ratio: Daily Active Users \/ Monthly Active Users\n - Target: 20%+ = habit-forming product\n - Your DAU\/MAU: <span class=\"placeholder\">[DAU_MAU]<\/span>%\n \u2022 Feature Adoption: % of users using core features weekly\n \u2022 Session Frequency: How often do users return? (daily, weekly, monthly)\n \u2022 Time Spent: Average session duration \/ time in product\n\n<strong>2. QUALITATIVE PMF SIGNALS<\/strong>\n \n <strong>A. Customer Interview Insights<\/strong>\n Conduct 15-20 customer interviews exploring:\n \u2022 Problem Recognition: Do they deeply feel the pain you're solving?\n - \"On a scale of 1-10, how painful is [PROBLEM] for you?\" (Target: 8+)\n - \"What have you tried to solve this before?\" (workarounds = real pain)\n \u2022 Solution Value: Does your product solve it significantly better?\n - \"How much better is our product vs. your previous solution?\" (Target: 10x better)\n - \"What would you do if we shut down tomorrow?\" (scramble to find alternative = PMF)\n \u2022 Willingness to Pay: Do they value it enough to pay?\n - \"At what price would this be too expensive? Too cheap? About right?\" (Van Westendorp pricing)\n - \"Have you recommended us to others? Why or why not?\"\n \u2022 Must-Have vs. Nice-to-Have: Is your product a vitamin (optional) or painkiller (essential)?\n - \"Could you do your job\/life without this product?\" (No = must-have)\n \n <strong>B. Customer Segmentation Analysis<\/strong>\n Identify who loves your product most:\n \u2022 Power Users: Which segment has highest engagement, retention, NPS?\n \u2022 Best-Fit ICP: Company size, industry, role, use case where you win\n \u2022 Poor-Fit Segments: Who churns fast or doesn't engage? Stop targeting them\n \u2022 Ideal Customer Profile (ICP) Definition:\n - Demographics\/Firmographics\n - Behavioral patterns\n - Psychographic traits (values, pain points, goals)\n \n <strong>C. Competitive Positioning<\/strong>\n \u2022 Win\/Loss Analysis: Why do customers choose you vs. competitors?\n - Wins: What differentiation resonates? (features, price, brand, UX)\n - Losses: Where do competitors beat you?\n \u2022 Switching Behavior: Are customers switching TO you from competitors? (strong signal)\n \u2022 Switching Friction: Would customers switch AWAY if competitor caught up? (weak moat)\n\n<strong>3. PMF SCORING FRAMEWORK<\/strong>\n Rate your product on each dimension (1-10 scale):\n \n \u2022 <strong>Customer Love<\/strong>: Sean Ellis score \u226540%, NPS \u226550\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Retention Strength<\/strong>: Cohorts flatten above 60% retention\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Organic Growth<\/strong>: 30%+ users from WOM\/referral, K-factor >0.5\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Unit Economics<\/strong>: LTV:CAC \u22653:1, payback \u226412 months\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Engagement Depth<\/strong>: DAU\/MAU \u226520%, high feature adoption\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n \u2022 <strong>Problem-Solution Fit<\/strong>: Customers rate pain 8+\/10, solution 10x better\n - Your Score: <span class=\"placeholder\">[SCORE]<\/span>\/10\n \n <strong>Overall PMF Score:<\/strong> Average of 6 dimensions = <span class=\"placeholder\">[TOTAL_SCORE]<\/span>\/10\n \n <strong>PMF Stage Assessment:<\/strong>\n \u2022 8-10: Strong PMF \u2014 Scale aggressively, invest in growth\n \u2022 6-8: Moderate PMF \u2014 Optimize product, improve retention, refine ICP\n \u2022 4-6: Weak PMF \u2014 Iterate core product, talk to more customers, consider pivot\n \u2022 0-4: No PMF \u2014 Major pivot needed or shut down\n\n<strong>4. GAP ANALYSIS & PRIORITIZATION<\/strong>\n For each dimension scoring below 8, identify:\n \n <strong>A. Root Cause Analysis<\/strong>\n \u2022 Why is this dimension underperforming?\n \u2022 What customer feedback or data supports this?\n \u2022 What assumptions were wrong?\n \n <strong>B. Improvement Hypotheses<\/strong>\n \u2022 Hypothesis 1: \"If we [CHANGE], then [METRIC] will improve because [REASON]\"\n \u2022 Hypothesis 2: [...]\n \u2022 Hypothesis 3: [...]\n \n <strong>C. Prioritization Matrix<\/strong>\n For each hypothesis, score:\n \u2022 Impact: How much will this move the PMF needle? (1-10)\n \u2022 Confidence: How sure are we this will work? (1-10)\n \u2022 Effort: How much work required? (1-10, lower = easier)\n \u2022 Priority Score: (Impact \u00d7 Confidence) \/ Effort\n \n Sort by Priority Score \u2014 tackle highest-scoring initiatives first\n\n<strong>5. PMF IMPROVEMENT ROADMAP<\/strong>\n \n <strong>Immediate Actions (Next 30 Days)<\/strong>\n \u2022 Customer Research: Interview 10-15 users, especially churned users\n \u2022 Metric Instrumentation: Ensure you're tracking all key PMF metrics\n \u2022 Quick Wins: Top 3 highest-priority, lowest-effort improvements\n \n <strong>Short-Term (30-90 Days)<\/strong>\n \u2022 Product Iterations: Implement top 5 prioritized hypotheses\n \u2022 ICP Refinement: Double down on best-fit segments, de-prioritize poor fits\n \u2022 Onboarding Optimization: Improve activation rate (% of signups becoming active users)\n \n <strong>Medium-Term (3-6 Months)<\/strong>\n \u2022 Core Product Evolution: Major feature builds or pivots if needed\n \u2022 Retention Programs: Engagement loops, notification systems, community\n \u2022 Economic Optimization: Pricing changes, CAC reduction, LTV expansion\n \n <strong>Success Metrics (6-Month Targets)<\/strong>\n \u2022 Sean Ellis score: From <span class=\"placeholder\">[CURRENT]<\/span>% \u2192 <span class=\"placeholder\">[TARGET]<\/span>%\n \u2022 Retention: From <span class=\"placeholder\">[CURRENT]<\/span>% \u2192 <span class=\"placeholder\">[TARGET]<\/span>%\n \u2022 NPS: From <span class=\"placeholder\">[CURRENT]<\/span> \u2192 <span class=\"placeholder\">[TARGET]<\/span>\n \u2022 LTV:CAC: From <span class=\"placeholder\">[CURRENT]<\/span> \u2192 <span class=\"placeholder\">[TARGET]<\/span>\n\n<strong>6. PMF DECISION FRAMEWORK<\/strong>\n \n <strong>If Strong PMF (8-10 score):<\/strong>\n \u2022 Decision: SCALE \u2014 Pour fuel on the fire\n \u2022 Focus: Growth marketing, sales team scaling, infrastructure\n \u2022 Investment: Fundraising for growth, aggressive hiring\n \n <strong>If Moderate PMF (6-8 score):<\/strong>\n \u2022 Decision: OPTIMIZE \u2014 Improve before scaling\n \u2022 Focus: Product iteration, retention optimization, ICP refinement\n \u2022 Investment: Moderate, focus on efficiency over growth\n \n <strong>If Weak\/No PMF (<6 score):<\/strong>\n \u2022 Decision: PIVOT or ITERATE \u2014 Don't scale prematurely\n \u2022 Focus: Customer discovery, product experiments, hypothesis testing\n \u2022 Investment: Minimal, extend runway, avoid premature scaling\n \u2022 Pivot Triggers: If no improvement after 2-3 iteration cycles (6 months), consider major pivot or shutdown\n\n<strong>OUTPUT FORMAT:<\/strong>\n\u2022 Executive Summary: Overall PMF score, stage, key strengths\/gaps\n\u2022 Quantitative Dashboard: All 6 PMF metrics with scores and benchmarks\n\u2022 Qualitative Insights: Customer interview themes, positioning analysis\n\u2022 Gap Analysis: Root causes, hypotheses, prioritization matrix\n\u2022 90-Day Action Plan: Specific initiatives with owners, timelines, success metrics\n\u2022 Decision Recommendation: Scale \/ Optimize \/ Pivot with clear rationale\n\n<strong>TONE & STYLE:<\/strong>\n\u2022 Data-driven and evidence-based\n\u2022 Honest and unbiased (acknowledge weaknesses)\n\u2022 Action-oriented (focus on what to do next)\n\u2022 Strategic (connect metrics to business outcomes)<\/div>\n <\/div>\n\n <div class=\"tip-box\">\n <strong>Tip:<\/strong> Fill in the <span class=\"placeholder\">[orange placeholders]<\/span> with your product metrics and context before using this prompt.\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">The Logic (Why This Prompt Works)<\/h3>\n <\/div>\n \n <div class=\"logic-grid\">\n <div class=\"logic-item\">\n <h4>Sean Ellis 40% Test (Validated PMF Benchmark)<\/h4>\n <p>The prompt uses Sean Ellis's empirically validated PMF threshold: \u226540% of users answering \"very disappointed\" if product disappeared. This simple question correlates strongly with sustainable growth\u2014companies above 40% scale successfully; those below struggle. Ellis tested this across 100+ startups and found it's the most predictive single PMF metric, better than NPS or retention alone.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Quantitative + Qualitative Dual Assessment<\/h4>\n <p>The framework balances hard metrics (retention curves, LTV:CAC, NPS) with soft signals (customer interviews, problem intensity, must-have perception). Metrics can mislead\u2014high retention with low growth could mean small niche; customer interviews reveal if you're solving a hair-on-fire problem or nice-to-have. Instagram had both before scaling; Color (photo-sharing app) had growth but weak qualitative signals and failed.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Retention Curve Flattening (True North PMF Signal)<\/h4>\n <p>The prompt emphasizes retention curve shape over absolute percentages. A curve that flattens (plateaus above 0%) indicates a core group finding lasting value\u2014the foundation for growth. A curve decaying to 0% means everyone eventually leaves\u2014no PMF regardless of initial virality. Facebook's curves flattened at colleges before expanding; many viral apps (Yo, Clubhouse) showed decay curves and couldn't sustain.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>ICP Segmentation (Who Loves You Most?)<\/h4>\n <p>Rather than treating all customers equally, the prompt requires identifying which segments show strongest PMF signals (highest retention, NPS, engagement). Early PMF often exists in narrow segments\u2014expanding too broadly before deeply owning a niche dilutes focus. Slack found PMF with tech teams before expanding to non-tech; targeting everyone initially would have failed. This lens prevents \"average\" PMF that masks strong fit in one segment and poor fit elsewhere.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Unit Economics Constraint (Sustainable PMF)<\/h4>\n <p>The framework includes LTV:CAC \u22653:1 as a PMF requirement\u2014product-market fit without economic fit isn't sustainable. Many products achieve customer love but can't acquire customers profitably (CAC too high) or retain them long enough (LTV too low). WeWork had customer demand but unit economics never worked at scale. This economic lens prevents chasing PMF metrics while building unprofitable businesses.<\/p>\n <\/div>\n \n <div class=\"logic-item\">\n <h4>Stage-Specific Decision Framework (Scale\/Optimize\/Pivot)<\/h4>\n <p>The prompt maps PMF score (8-10 strong, 6-8 moderate, <6 weak) to clear strategic actions\u2014preventing premature scaling (the #1 startup killer per CB Insights). Strong PMF = scale aggressively; moderate = optimize before scaling; weak = iterate or pivot. This disciplined gate-keeping saved companies like Segment (pivoted 3 times before PMF) and prevented waste at companies that scaled too early (e.g., Quibi's $1.75B loss despite weak PMF signals).<\/p>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">Output Preview<\/h3>\n <\/div>\n \n <div class=\"preview-section\">\n <div class=\"preview-title\">Example Result:<\/div>\n <div class=\"preview-content\">\n <p><strong>PRODUCT: Team Communication SaaS (Slack-like product, 6 months post-launch)<\/strong><\/p>\n\n <h4 class=\"subsection-title\">EXECUTIVE SUMMARY<\/h4>\n <p><strong>Overall PMF Score: 6.2\/10<\/strong> \u2014 Moderate PMF with strong signals in specific segments but gaps in retention and economics<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>Assessment: OPTIMIZE<\/strong> \u2014 Don't scale yet. Focus on improving retention, narrowing ICP, and optimizing unit economics before growth investment.<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>Key Strengths:<\/strong><\/p>\n <ul>\n <li>Strong customer love in tech\/startup segment (Sean Ellis: 48% \"very disappointed\" among 0-50 employee companies)<\/li>\n <li>Viral growth within teams (K-factor: 0.7 \u2014 each user brings 0.7 new users)<\/li>\n <li>High engagement for power users (DAU\/MAU: 35% for tech teams)<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>Critical Gaps:<\/strong><\/p>\n <ul>\n <li>Poor retention in enterprise segment (Month 3 retention: 32% vs. 78% in startups)<\/li>\n <li>Weak unit economics (LTV:CAC = 1.8:1, need 3:1+)<\/li>\n <li>Low NPS in non-tech industries (NPS: 12 vs. 62 in tech)<\/li>\n <\/ul>\n\n <h4 class=\"subsection-title\">QUANTITATIVE PMF METRICS<\/h4>\n <p><strong>1. Sean Ellis Test:<\/strong> 37% overall \"very disappointed\" (Below 40% threshold)<\/p>\n <ul>\n <li>Tech startups (0-50 employees): 48% \u2713 Strong PMF<\/li>\n <li>Enterprise (500+ employees): 18% \u2717 No PMF<\/li>\n <li>Non-tech SMBs: 22% \u2717 Weak PMF<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Insight:<\/strong> Clear PMF in tech startup segment; diluted by poor fit elsewhere<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>2. Retention Curves:<\/strong> Mixed signals<\/p>\n <ul>\n <li>Tech startups: Month 1: 85% \u2192 Month 3: 78% \u2192 Month 6: 74% (Flattening \u2713)<\/li>\n <li>Enterprise: Month 1: 65% \u2192 Month 3: 32% \u2192 Month 6: 12% (Decaying \u2717)<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Insight:<\/strong> Retention curves flatten only in tech startup segment\u2014PMF exists there<\/p>\n\n <p style=\"margin-top: 1rem;\"><strong>3. Organic Growth:<\/strong><\/p>\n <ul>\n <li>Referral\/Organic: 62% of new users (Strong WOM)<\/li>\n <li>K-factor: 0.7 (Good, approaching viral threshold of 1.0)<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>4. NPS:<\/strong> 38 overall (Mediocre)<\/p>\n <ul>\n <li>Tech segment: 62 (Excellent)<\/li>\n <li>Non-tech: 12 (Poor)<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>5. Unit Economics:<\/strong> Weak<\/p>\n <ul>\n <li>CAC: $2,400 (blend of low-touch PLG + high-touch sales)<\/li>\n <li>LTV: $4,300 (24-month avg retention, $180\/month ARPU)<\/li>\n <li>LTV:CAC: 1.8:1 \u2717 (Need 3:1+)<\/li>\n <li>Payback: 14 months \u2717 (Target: <12 months)<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Issue:<\/strong> Acquiring wrong customers (enterprise) drives CAC up; they churn fast, killing LTV<\/p>\n\n <h4 class=\"subsection-title\">PMF SCORE BREAKDOWN<\/h4>\n <ul>\n <li><strong>Customer Love:<\/strong> 6\/10 (37% Sean Ellis score, dragged down by non-PMF segments)<\/li>\n <li><strong>Retention Strength:<\/strong> 7\/10 (Strong in tech, weak elsewhere)<\/li>\n <li><strong>Organic Growth:<\/strong> 8\/10 (62% organic, K=0.7)<\/li>\n <li><strong>Unit Economics:<\/strong> 4\/10 (LTV:CAC too low, payback too long)<\/li>\n <li><strong>Engagement Depth:<\/strong> 7\/10 (DAU\/MAU 35% in best-fit segment)<\/li>\n <li><strong>Problem-Solution Fit:<\/strong> 7\/10 (Tech users rate pain 9\/10, love solution)<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Overall:<\/strong> 6.2\/10 = Moderate PMF<\/p>\n\n <h4 class=\"subsection-title\">GAP ANALYSIS & PRIORITIZATION<\/h4>\n <p><strong>Gap #1: Unit Economics (4\/10 score)<\/strong><\/p>\n <p><strong>Root Cause:<\/strong> Targeting enterprise customers who don't have PMF\u2014high CAC (sales-heavy), low retention (wrong ICP), kills LTV:CAC ratio<\/p>\n <p><strong>Hypotheses:<\/strong><\/p>\n <ul>\n <li><strong>H1:<\/strong> If we stop targeting enterprise (500+ employees) and focus only on tech startups (0-200 employees), CAC will drop 40% (less sales effort) and LTV will increase 60% (better retention) \u2192 LTV:CAC improves from 1.8 to 4.2<\/li>\n <li><strong>H2:<\/strong> If we move enterprise to pure PLG (no sales assist), CAC drops but conversion rate may drop\u2014test with subset<\/li>\n <\/ul>\n <p style=\"margin-top: 1rem;\"><strong>Priority Score:<\/strong> H1 = (9 impact \u00d7 8 confidence) \/ 3 effort = 24 \u2713 High priority<\/p>\n\n <h4 class=\"subsection-title\">90-DAY ACTION PLAN<\/h4>\n <p><strong>Immediate (Days 1-30):<\/strong><\/p>\n <ul>\n <li>Pause all enterprise outbound sales\/marketing (save $40k\/month burn)<\/li>\n <li>Interview 15 churned enterprise users: Why did you leave? What were you hoping for?<\/li>\n <li>Re-instrument analytics to segment all metrics by company size\/industry<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>Short-Term (Days 31-90):<\/strong><\/p>\n <ul>\n <li>Launch \"Startups Only\" positioning\u2014rebrand marketing to tech\/startup segment<\/li>\n <li>Optimize onboarding for tech teams (developer integrations, GitHub\/Jira\/Figma)<\/li>\n <li>Build viral loop features (invite teammates, public community channels)<\/li>\n <li>Target: Increase tech startup Sean Ellis score from 48% \u2192 55%+<\/li>\n <\/ul>\n\n <p style=\"margin-top: 1rem;\"><strong>Success Metrics (90-day targets):<\/strong><\/p>\n <ul>\n <li>Sean Ellis (tech startups only): 48% \u2192 55%<\/li>\n <li>Overall LTV:CAC: 1.8 \u2192 3.5 (by focusing on high-retention segment)<\/li>\n <li>CAC: $2,400 \u2192 $1,600 (less enterprise sales waste)<\/li>\n <li>Month 3 retention: 78% \u2192 85% (optimize tech segment onboarding)<\/li>\n <\/ul>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">Chain Strategy (Advanced Workflow)<\/h3>\n <\/div>\n <p style=\"margin-bottom: 1.5rem;\">For best results, use this 3-step sequential prompting strategy:<\/p>\n \n <div class=\"chain-steps\">\n <div class=\"chain-step\">\n <div class=\"step-number\">1<\/div>\n <h4>Quantitative Metric Collection & Segmentation<\/h4>\n <p><strong>Goal:<\/strong> Gather all PMF metrics and segment by customer type<\/p>\n <p><strong>Prompt:<\/strong> \"Extract and calculate all PMF metrics from our data for <span class=\"placeholder\">[PRODUCT]<\/span>. Required metrics: (1) Sean Ellis score: Survey 100+ users with 'How would you feel if you could no longer use [PRODUCT]?' Calculate % answering 'Very disappointed'. Segment by: customer size, industry, use case, tenure. (2) Retention cohorts: For each monthly signup cohort (last 12 months), calculate Month 1, Month 3, Month 6 retention rates. Plot curves\u2014do they flatten or decay? (3) NPS: Calculate Net Promoter Score overall and by segment. (4) Organic growth: % of new signups from referral\/organic vs. paid channels. (5) Unit economics: CAC (total sales\/marketing spend \u00f7 new customers), LTV (ARPU \u00d7 avg customer lifetime), LTV:CAC ratio, payback period in months. (6) Engagement: DAU\/MAU ratio, feature adoption rates, session frequency. Organize output as: Overall scores + Segment breakdown table (show which segments have strong vs. weak PMF signals). Highlight: Best-fit segment (highest scores) vs. Worst-fit segment (lowest scores).\"<\/p>\n <p><strong>Expected Output:<\/strong> PMF metrics dashboard segmented by customer type, with identification of high-PMF and low-PMF segments.<\/p>\n <\/div>\n \n <div class=\"chain-step\">\n <div class=\"step-number\">2<\/div>\n <h4>Qualitative Customer Interview Synthesis<\/h4>\n <p><strong>Goal:<\/strong> Understand why metrics are what they are through customer voice<\/p>\n <p><strong>Prompt:<\/strong> \"Conduct or analyze 15-20 customer interviews to understand PMF qualitatively. Interview split: 10 power users (high engagement, long tenure), 5 churned users (canceled in last 90 days). Interview guide: (1) Problem intensity: 'On 1-10, how painful was [PROBLEM] before our product? What did you try before us? How much time\/money did it cost you?' (2) Solution value: 'How much better is our product vs. your previous solution? 2x? 5x? 10x? What would you do if we disappeared tomorrow?' (3) Must-have vs. nice-to-have: 'Could you do your job without this product? What would break?' (4) Willingness to pay: 'At what price is this too expensive? Too cheap (seems low quality)? Just right?' (5) Recommendation behavior: 'Have you told others about us? Who? Why or why not?' (6) For churned users: 'Why did you leave? What were we missing? What would bring you back?' Synthesize findings into: (a) Themes from power users (what drives love), (b) Themes from churned users (why we lose them), (c) Problem intensity score (avg 1-10), (d) Solution superiority (2x, 5x, 10x better?), (e) Must-have % (% saying they couldn't live without it). Compare themes across segments identified in Step 1.\"<\/p>\n <p><strong>Expected Output:<\/strong> Qualitative PMF report with customer quotes, thematic analysis, and alignment (or misalignment) with quantitative findings.<\/p>\n <\/div>\n \n <div class=\"chain-step\">\n <div class=\"step-number\">3<\/div>\n <h4>Gap Prioritization & Roadmap Development<\/h4>\n <p><strong>Goal:<\/strong> Translate insights into prioritized action plan<\/p>\n <p><strong>Prompt:<\/strong> \"Based on PMF metrics [INSERT STEP 1 DATA] and customer insights [INSERT STEP 2 THEMES], create a prioritized PMF improvement roadmap. Steps: (1) Identify gaps: Which PMF dimensions scored <8>\n <p><strong>Expected Output:<\/strong> Action-ready PMF improvement roadmap with prioritized initiatives, owners, timelines, success metrics, and decision gates.<\/p>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"section\">\n <div class=\"section-header\">\n <h3 class=\"section-title\">Human-in-the-Loop Refinement Tips<\/h3>\n <\/div>\n <p style=\"margin-bottom: 1.5rem;\">Enhance your results with these follow-up prompts:<\/p>\n \n <div class=\"hitl-grid\">\n <div class=\"hitl-item\">\n <h4>\ud83d\udcca Cohort Retention Deep Dive<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Analyze retention cohorts in detail for <span class=\"placeholder\">[PRODUCT]<\/span>. For each monthly cohort from the last 12 months: (1) Plot retention curve showing Week 1, Week 2, Week 4, Month 2, Month 3, Month 6, Month 12 retention. (2) Identify: At what point do curves start to flatten? What % retention do they plateau at? (3) Compare: Early cohorts (12 months ago) vs. recent cohorts (last 3 months)\u2014are recent cohorts retaining better (product improving) or worse (lower quality users)? (4) Segment analysis: Plot separate retention curves for high-value vs. low-value customers, different acquisition channels (organic vs. paid), different personas\/use cases. (5) Leading indicators: What Week 1 behaviors predict Month 6 retention? (activation events, feature usage, invites sent). Identify: What's the 'aha moment' that correlates with long-term retention? (6) Churn analysis: For users who churned, when did they churn (Day 1, Week 1, Month 1, Month 3+)? Why? (survey churned users). Create: Retention curve dashboard, churn reason taxonomy, activation metric recommendation (what predicts retention).\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83c\udfaf ICP Refinement & Segmentation<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Refine our Ideal Customer Profile (ICP) based on PMF signals. Analysis: (1) Segment all customers by: company size, industry, role\/title, use case, geography, acquisition channel. (2) For each segment, calculate: Sean Ellis score, retention rate, NPS, LTV, CAC, engagement (DAU\/MAU), expansion revenue, referral rate. (3) Identify 'PMF segments': Which 2-3 segments score highest across all metrics? These are your best-fit customers. (4) Identify 'no-PMF segments': Which segments score lowest? These are poor fits\u2014stop targeting them. (5) Create detailed ICP profile for your #1 PMF segment: Demographics\/firmographics (company size, industry, revenue, employee count, tech stack), Psychographics (pain points, goals, values, buying behavior), Behavioral signals (what do they do before buying? what triggers purchase?), Where to find them (channels, communities, events). (6) Recommendation: What % of marketing\/sales resources should focus on PMF segments vs. others? Should we explicitly de-position from no-PMF segments (e.g., 'We're not for enterprise')? Provide: ICP document, segment prioritization matrix, GTM strategy recommendation (channels to double down, channels to cut).\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udcac Customer Interview Script & Analysis<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Create a detailed customer interview script for PMF assessment of <span class=\"placeholder\">[PRODUCT]<\/span>. Interview structure (45-60 min): (1) Intro (5 min): Explain purpose (learning, not selling), ask permission to record. (2) Context setting (10 min): What's your role? What does a typical day look like? What are your biggest challenges? (3) Pre-product state (10 min): Before our product, how did you handle [PROBLEM]? What tools\/workarounds? How much time\/money did it cost? On 1-10, how painful was this problem? What triggered you to look for a solution? (4) Product discovery & adoption (5 min): How did you find us? What made you try us? What were you skeptical about? (5) Current usage (15 min): How do you use our product? Walk me through your workflow. What do you love? What frustrates you? If we disappeared, what would you do? Could you do your job without us? (6) Value perception (5 min): How much better are we vs. previous solution? (2x, 5x, 10x?) At what price would this be: too expensive? too cheap (suspicious)? just right? (7) Recommendation (5 min): Have you recommended us? To whom? Why or why not? (8) Future needs (5 min): What's missing? What would make this a 10\/10 product for you? After 15-20 interviews: Synthesize themes, calculate avg pain score, identify must-have %, map feature requests by frequency, extract powerful quotes for case studies\/marketing. Provide: Full interview script with follow-up questions, synthesis template for organizing findings.\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udcc8 Growth Lever Identification<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Identify the highest-leverage growth opportunities for <span class=\"placeholder\">[PRODUCT]<\/span> given current PMF state. Analysis: (1) If PMF score is 6-8 (moderate): Growth levers ranked by priority: (a) Improve retention (cohort optimization\u2014get more users to 'aha moment'), (b) Expand within existing customers (upsell\/cross-sell to increase LTV), (c) Double down on best-fit ICP (narrow targeting for better unit economics), (d) Referral program (leverage existing love in PMF segments), (e) Delay heavy growth marketing until PMF improves to 8+. (2) If PMF score is 8-10 (strong): Growth levers ranked: (a) Scale paid acquisition in PMF segments (CAC is justified by high LTV), (b) Sales team scaling (outbound in high-PMF verticals), (c) Strategic partnerships (distribution deals, integrations), (d) International expansion (replicate playbook in new geos), (e) Platform plays (APIs, marketplace, ecosystem). (3) For each lever: Estimated impact on revenue (12-month projection), Required investment, Key risks, Success metrics to track. (4) Sequencing: What order to pull levers? Which are prerequisites for others? (5) Anti-patterns: What should we NOT do given our PMF stage? (e.g., don't scale paid ads if LTV:CAC <3:1). Provide: Prioritized growth lever roadmap with investment requirements and projected ROI.\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udd04 PMF Monitoring Dashboard Design<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Design a real-time PMF monitoring dashboard for <span class=\"placeholder\">[PRODUCT]<\/span>. Dashboard sections: (1) PMF Health Score (center): Overall score 0-10 calculated from 6 dimensions, color-coded (red <6, yellow 6-8, green 8-10). (2) Core metrics panel: Sean Ellis score (% 'very disappointed'), Month 3 retention rate, NPS, LTV:CAC ratio, DAU\/MAU, Organic growth %. Each with: current value, 30-day trend (\u2191\u2193), benchmark comparison, segment breakdown on hover. (3) Cohort retention curves: Visual chart showing retention curves for last 6 monthly cohorts\u2014check if flattening or decaying. (4) Segment performance matrix: Table showing all customer segments with PMF scores\u2014highlight best-fit (green) and poor-fit (red) segments. (5) Leading indicators: Week 1 activation rate, time to 'aha moment', referral rate\u2014these predict future retention. (6) Alert triggers: Red flags when: Sean Ellis drops below 35%, Month 3 retention declines >5% MoM, LTV:CAC drops below 2:1. (7) Qualitative summary: Latest customer interview themes, NPS verbatim comments, feature requests ranked by frequency. Refresh frequency: Core metrics (daily), cohort analysis (weekly), qualitative synthesis (monthly). Provide: Dashboard wireframe\/mockup, data source requirements (what needs to be tracked), KPI definitions, recommended review cadence (who reviews what, how often).\"<\/p>\n <\/div>\n \n <div class=\"hitl-item\">\n <h4>\ud83d\udd00 Pivot Decision Framework<\/h4>\n <p><strong>Follow-up Prompt:<\/strong> \"Develop a structured pivot decision framework for <span class=\"placeholder\">[PRODUCT]<\/span> if PMF doesn't improve. Scenario: After 6 months of iteration, PMF score remains <6>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n <div class=\"stats-footer\">\n <div class=\"stat\">\n <div class=\"stat-value\">\u2b50 4.9<\/div>\n <div class=\"stat-label\">(823 ratings)<\/div>\n <\/div>\n <div class=\"stat\">\n <div class=\"stat-value\">\ud83d\udccb 15,234<\/div>\n <div class=\"stat-label\">copies<\/div>\n <\/div>\n <div class=\"stat\">\n <div class=\"stat-value\">\ud83d\udcac 672<\/div>\n <div class=\"stat-label\">reviews<\/div>\n <\/div>\n <\/div>\n <\/div>\n\n <script>\n function copyPrompt() {\n const promptText = document.getElementById('promptText').innerText;\n const button = document.querySelector('.copy-button');\n \n navigator.clipboard.writeText(promptText).then(() => {\n button.textContent = '\u2713 Copied!';\n button.classList.add('copied');\n \n setTimeout(() => {\n button.textContent = '\ud83d\udccb Copy Prompt';\n button.classList.remove('copied');\n }, 2000);\n });\n }\n <\/script>\n<\/body>\n<\/html>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Product-Market Fit Assessment – AiPro Institute\u2122 Prompt Library AiPro Institute\u2122 Prompt Library Product-Market Fit Assessment Measure and optimize the alignment between your product and customer needs with quantitative metrics and qualitative insights Intermediate 15-25 minutes ~650 tokens Assessment Framework Tool Compatibility Model Agnostic GPT-4o Claude 3.5 Gemini 2.5 The Prompt \ud83d\udccb Copy Prompt You are…<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[182],"tags":[],"class_list":["post-4781","post","type-post","status-publish","format-standard","hentry","category-market-strategy"],"acf":[],"_links":{"self":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts\/4781","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/comments?post=4781"}],"version-history":[{"count":4,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts\/4781\/revisions"}],"predecessor-version":[{"id":4819,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/posts\/4781\/revisions\/4819"}],"wp:attachment":[{"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/media?parent=4781"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/categories?post=4781"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/teen.aiproinstitute.com\/zh\/wp-json\/wp\/v2\/tags?post=4781"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}