Product-Market Fit Assessment

Product-Market Fit Assessment - AiPro Institute™ Prompt Library

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

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. PRODUCT CONTEXT: Product Name: [PRODUCT_NAME] Product Category: [CATEGORY] (e.g., SaaS, Mobile App, Hardware, Marketplace, DTC) Target Customer: [TARGET_ICP] (describe ideal customer profile) Core Value Proposition: [VALUE_PROP] (what problem you solve, how) Stage: [STAGE] (Pre-launch, Early traction 0-100 customers, Growth 100-1000, Scale 1000+) Current Metrics: [KEY_METRICS] (Users, revenue, retention, NPS, etc.) PRODUCT-MARKET FIT ASSESSMENT FRAMEWORK: 1. QUANTITATIVE PMF SIGNALS Measure these key indicators: A. The Sean Ellis Test (PMF Litmus Test) • Survey Question: "How would you feel if you could no longer use [PRODUCT]?" - Very disappointed - Somewhat disappointed - Not disappointed • PMF Threshold: ≥40% answering "Very disappointed" = strong PMF signal • Your Score: [X%] very disappointed • Interpretation: - 40%+: Strong PMF, focus on growth - 25-40%: Moderate PMF, optimize product - <25%: Weak PMF, pivot or iterate core offering B. Retention Cohort Analysis • Cohort Retention Curves: Do they flatten or decay to zero? - Flattening curve (plateau above 0%) = PMF signal (users stick around) - Decaying to 0% = No PMF (everyone churns eventually) • Month 1 → Month 3 → Month 6 Retention Rates • Current Retention: [RETENTION_DATA] • Benchmark: Best-in-class SaaS = 90% Month 1, 70% Month 3, 60% Month 6 C. Organic Growth Rate • Word-of-Mouth Growth: % of new users from referrals/organic vs. paid • Viral Coefficient: How many new users does each user bring? (K-factor) • Target: K > 1.0 = viral growth, K = 0.5-1.0 = strong WOM • Your K-factor: [K_VALUE] D. Net Promoter Score (NPS) • NPS Question: "On 0-10, how likely are you to recommend [PRODUCT] to a colleague/friend?" - Promoters (9-10): Subtract Detractors (0-6) = NPS • Your NPS: [NPS_SCORE] • Benchmark: NPS >50 = Excellent, 30-50 = Good, <30 = Poor E. Revenue & Unit Economics • LTV:CAC Ratio: Lifetime Value / Customer Acquisition Cost - Target: 3:1 or higher = sustainable economics - Your Ratio: [LTV_CAC_RATIO] • CAC Payback Period: Months to recover acquisition cost - Target: <12 months - Your Payback: [PAYBACK_MONTHS] months • Gross Margin: Revenue - COGS / Revenue - Target: 70%+ for SaaS, 40%+ for e-commerce - Your Margin: [GROSS_MARGIN]% F. Usage Intensity & Engagement • DAU/MAU Ratio: Daily Active Users / Monthly Active Users - Target: 20%+ = habit-forming product - Your DAU/MAU: [DAU_MAU]% • Feature Adoption: % of users using core features weekly • Session Frequency: How often do users return? (daily, weekly, monthly) • Time Spent: Average session duration / time in product 2. QUALITATIVE PMF SIGNALS A. Customer Interview Insights Conduct 15-20 customer interviews exploring: • Problem Recognition: Do they deeply feel the pain you're solving? - "On a scale of 1-10, how painful is [PROBLEM] for you?" (Target: 8+) - "What have you tried to solve this before?" (workarounds = real pain) • Solution Value: Does your product solve it significantly better? - "How much better is our product vs. your previous solution?" (Target: 10x better) - "What would you do if we shut down tomorrow?" (scramble to find alternative = PMF) • Willingness to Pay: Do they value it enough to pay? - "At what price would this be too expensive? Too cheap? About right?" (Van Westendorp pricing) - "Have you recommended us to others? Why or why not?" • Must-Have vs. Nice-to-Have: Is your product a vitamin (optional) or painkiller (essential)? - "Could you do your job/life without this product?" (No = must-have) B. Customer Segmentation Analysis Identify who loves your product most: • Power Users: Which segment has highest engagement, retention, NPS? • Best-Fit ICP: Company size, industry, role, use case where you win • Poor-Fit Segments: Who churns fast or doesn't engage? Stop targeting them • Ideal Customer Profile (ICP) Definition: - Demographics/Firmographics - Behavioral patterns - Psychographic traits (values, pain points, goals) C. Competitive Positioning • Win/Loss Analysis: Why do customers choose you vs. competitors? - Wins: What differentiation resonates? (features, price, brand, UX) - Losses: Where do competitors beat you? • Switching Behavior: Are customers switching TO you from competitors? (strong signal) • Switching Friction: Would customers switch AWAY if competitor caught up? (weak moat) 3. PMF SCORING FRAMEWORK Rate your product on each dimension (1-10 scale): • Customer Love: Sean Ellis score ≥40%, NPS ≥50 - Your Score: [SCORE]/10 • Retention Strength: Cohorts flatten above 60% retention - Your Score: [SCORE]/10 • Organic Growth: 30%+ users from WOM/referral, K-factor >0.5 - Your Score: [SCORE]/10 • Unit Economics: LTV:CAC ≥3:1, payback ≤12 months - Your Score: [SCORE]/10 • Engagement Depth: DAU/MAU ≥20%, high feature adoption - Your Score: [SCORE]/10 • Problem-Solution Fit: Customers rate pain 8+/10, solution 10x better - Your Score: [SCORE]/10 Overall PMF Score: Average of 6 dimensions = [TOTAL_SCORE]/10 PMF Stage Assessment: • 8-10: Strong PMF — Scale aggressively, invest in growth • 6-8: Moderate PMF — Optimize product, improve retention, refine ICP • 4-6: Weak PMF — Iterate core product, talk to more customers, consider pivot • 0-4: No PMF — Major pivot needed or shut down 4. GAP ANALYSIS & PRIORITIZATION For each dimension scoring below 8, identify: A. Root Cause Analysis • Why is this dimension underperforming? • What customer feedback or data supports this? • What assumptions were wrong? B. Improvement Hypotheses • Hypothesis 1: "If we [CHANGE], then [METRIC] will improve because [REASON]" • Hypothesis 2: [...] • Hypothesis 3: [...] C. Prioritization Matrix For each hypothesis, score: • Impact: How much will this move the PMF needle? (1-10) • Confidence: How sure are we this will work? (1-10) • Effort: How much work required? (1-10, lower = easier) • Priority Score: (Impact × Confidence) / Effort Sort by Priority Score — tackle highest-scoring initiatives first 5. PMF IMPROVEMENT ROADMAP Immediate Actions (Next 30 Days) • Customer Research: Interview 10-15 users, especially churned users • Metric Instrumentation: Ensure you're tracking all key PMF metrics • Quick Wins: Top 3 highest-priority, lowest-effort improvements Short-Term (30-90 Days) • Product Iterations: Implement top 5 prioritized hypotheses • ICP Refinement: Double down on best-fit segments, de-prioritize poor fits • Onboarding Optimization: Improve activation rate (% of signups becoming active users) Medium-Term (3-6 Months) • Core Product Evolution: Major feature builds or pivots if needed • Retention Programs: Engagement loops, notification systems, community • Economic Optimization: Pricing changes, CAC reduction, LTV expansion Success Metrics (6-Month Targets) • Sean Ellis score: From [CURRENT]% → [TARGET]% • Retention: From [CURRENT]% → [TARGET]% • NPS: From [CURRENT] → [TARGET] • LTV:CAC: From [CURRENT] → [TARGET] 6. PMF DECISION FRAMEWORK If Strong PMF (8-10 score): • Decision: SCALE — Pour fuel on the fire • Focus: Growth marketing, sales team scaling, infrastructure • Investment: Fundraising for growth, aggressive hiring If Moderate PMF (6-8 score): • Decision: OPTIMIZE — Improve before scaling • Focus: Product iteration, retention optimization, ICP refinement • Investment: Moderate, focus on efficiency over growth If Weak/No PMF (<6 score): • Decision: PIVOT or ITERATE — Don't scale prematurely • Focus: Customer discovery, product experiments, hypothesis testing • Investment: Minimal, extend runway, avoid premature scaling • Pivot Triggers: If no improvement after 2-3 iteration cycles (6 months), consider major pivot or shutdown OUTPUT FORMAT: • Executive Summary: Overall PMF score, stage, key strengths/gaps • Quantitative Dashboard: All 6 PMF metrics with scores and benchmarks • Qualitative Insights: Customer interview themes, positioning analysis • Gap Analysis: Root causes, hypotheses, prioritization matrix • 90-Day Action Plan: Specific initiatives with owners, timelines, success metrics • Decision Recommendation: Scale / Optimize / Pivot with clear rationale TONE & STYLE: • Data-driven and evidence-based • Honest and unbiased (acknowledge weaknesses) • Action-oriented (focus on what to do next) • Strategic (connect metrics to business outcomes)

Tip: Fill in the [orange placeholders] with your product metrics and context before using this prompt.

The Logic (Why This Prompt Works)

Sean Ellis 40% Test (Validated PMF Benchmark)

The prompt uses Sean Ellis's empirically validated PMF threshold: ≥40% of users answering "very disappointed" if product disappeared. This simple question correlates strongly with sustainable growth—companies 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.

Quantitative + Qualitative Dual Assessment

The framework balances hard metrics (retention curves, LTV:CAC, NPS) with soft signals (customer interviews, problem intensity, must-have perception). Metrics can mislead—high 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.

Retention Curve Flattening (True North PMF Signal)

The prompt emphasizes retention curve shape over absolute percentages. A curve that flattens (plateaus above 0%) indicates a core group finding lasting value—the foundation for growth. A curve decaying to 0% means everyone eventually leaves—no 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.

ICP Segmentation (Who Loves You Most?)

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—expanding 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.

Unit Economics Constraint (Sustainable PMF)

The framework includes LTV:CAC ≥3:1 as a PMF requirement—product-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.

Stage-Specific Decision Framework (Scale/Optimize/Pivot)

The prompt maps PMF score (8-10 strong, 6-8 moderate, <6 weak) to clear strategic actions—preventing 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).

Output Preview

Example Result:

PRODUCT: Team Communication SaaS (Slack-like product, 6 months post-launch)

EXECUTIVE SUMMARY

Overall PMF Score: 6.2/10 — Moderate PMF with strong signals in specific segments but gaps in retention and economics

Assessment: OPTIMIZE — Don't scale yet. Focus on improving retention, narrowing ICP, and optimizing unit economics before growth investment.

Key Strengths:

Strong customer love in tech/startup segment (Sean Ellis: 48% "very disappointed" among 0-50 employee companies)
Viral growth within teams (K-factor: 0.7 — each user brings 0.7 new users)
High engagement for power users (DAU/MAU: 35% for tech teams)

Critical Gaps:

Poor retention in enterprise segment (Month 3 retention: 32% vs. 78% in startups)
Weak unit economics (LTV:CAC = 1.8:1, need 3:1+)
Low NPS in non-tech industries (NPS: 12 vs. 62 in tech)

QUANTITATIVE PMF METRICS

1. Sean Ellis Test: 37% overall "very disappointed" (Below 40% threshold)

Tech startups (0-50 employees): 48% ✓ Strong PMF
Enterprise (500+ employees): 18% ✗ No PMF
Non-tech SMBs: 22% ✗ Weak PMF

Insight: Clear PMF in tech startup segment; diluted by poor fit elsewhere

2. Retention Curves: Mixed signals

Tech startups: Month 1: 85% → Month 3: 78% → Month 6: 74% (Flattening ✓)
Enterprise: Month 1: 65% → Month 3: 32% → Month 6: 12% (Decaying ✗)

Insight: Retention curves flatten only in tech startup segment—PMF exists there

3. Organic Growth:

Referral/Organic: 62% of new users (Strong WOM)
K-factor: 0.7 (Good, approaching viral threshold of 1.0)

4. NPS: 38 overall (Mediocre)

Tech segment: 62 (Excellent)
Non-tech: 12 (Poor)

5. Unit Economics: Weak

CAC: $2,400 (blend of low-touch PLG + high-touch sales)
LTV: $4,300 (24-month avg retention, $180/month ARPU)
LTV:CAC: 1.8:1 ✗ (Need 3:1+)
Payback: 14 months ✗ (Target: <12 months)

Issue: Acquiring wrong customers (enterprise) drives CAC up; they churn fast, killing LTV

PMF SCORE BREAKDOWN

Customer Love: 6/10 (37% Sean Ellis score, dragged down by non-PMF segments)
Retention Strength: 7/10 (Strong in tech, weak elsewhere)
Organic Growth: 8/10 (62% organic, K=0.7)
Unit Economics: 4/10 (LTV:CAC too low, payback too long)
Engagement Depth: 7/10 (DAU/MAU 35% in best-fit segment)
Problem-Solution Fit: 7/10 (Tech users rate pain 9/10, love solution)

Overall: 6.2/10 = Moderate PMF

GAP ANALYSIS & PRIORITIZATION

Gap #1: Unit Economics (4/10 score)

Root Cause: Targeting enterprise customers who don't have PMF—high CAC (sales-heavy), low retention (wrong ICP), kills LTV:CAC ratio

Hypotheses:

H1: 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) → LTV:CAC improves from 1.8 to 4.2
H2: If we move enterprise to pure PLG (no sales assist), CAC drops but conversion rate may drop—test with subset

Priority Score: H1 = (9 impact × 8 confidence) / 3 effort = 24 ✓ High priority

90-DAY ACTION PLAN

Immediate (Days 1-30):

Pause all enterprise outbound sales/marketing (save $40k/month burn)
Interview 15 churned enterprise users: Why did you leave? What were you hoping for?
Re-instrument analytics to segment all metrics by company size/industry

Short-Term (Days 31-90):

Launch "Startups Only" positioning—rebrand marketing to tech/startup segment
Optimize onboarding for tech teams (developer integrations, GitHub/Jira/Figma)
Build viral loop features (invite teammates, public community channels)
Target: Increase tech startup Sean Ellis score from 48% → 55%+

Success Metrics (90-day targets):

Sean Ellis (tech startups only): 48% → 55%
Overall LTV:CAC: 1.8 → 3.5 (by focusing on high-retention segment)
CAC: $2,400 → $1,600 (less enterprise sales waste)
Month 3 retention: 78% → 85% (optimize tech segment onboarding)

Chain Strategy (Advanced Workflow)

For best results, use this 3-step sequential prompting strategy:

Quantitative Metric Collection & Segmentation

Goal: Gather all PMF metrics and segment by customer type

Prompt: "Extract and calculate all PMF metrics from our data for [PRODUCT]. 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—do 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 ÷ new customers), LTV (ARPU × 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)."

Expected Output: PMF metrics dashboard segmented by customer type, with identification of high-PMF and low-PMF segments.

Qualitative Customer Interview Synthesis

Goal: Understand why metrics are what they are through customer voice

Prompt: "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."

Expected Output: Qualitative PMF report with customer quotes, thematic analysis, and alignment (or misalignment) with quantitative findings.

Gap Prioritization & Roadmap Development

Goal: Translate insights into prioritized action plan

Prompt: "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 <8/10? Why (root cause from data + interviews)? (2) Generate hypotheses: For each gap, create 3-5 'If we [CHANGE], then [METRIC] will improve because [REASON]' hypotheses. (3) Prioritize: Score each hypothesis on Impact (1-10: how much will this move PMF?), Confidence (1-10: how sure are we?), Effort (1-10: how much work? lower = easier). Calculate Priority Score = (Impact × Confidence) / Effort. Sort descending. (4) Build 90-day roadmap: Select top 5 highest-priority initiatives. For each: What exactly to do? Who owns it (DRI)? What's the timeline? What resources needed? What's the success metric? (5) Set targets: Define 90-day goals for each key PMF metric. (6) Decision logic: If PMF score reaches 8+ → scale; 6-8 → continue optimizing; <6 → consider pivot. Provide: (a) Prioritized hypothesis list with scores, (b) 90-day initiative plan with owners/timelines/metrics, (c) Clear decision framework for what happens after 90 days based on results."

Expected Output: Action-ready PMF improvement roadmap with prioritized initiatives, owners, timelines, success metrics, and decision gates.

Human-in-the-Loop Refinement Tips

Enhance your results with these follow-up prompts:

📊 Cohort Retention Deep Dive

Follow-up Prompt: "Analyze retention cohorts in detail for [PRODUCT]. 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)—are 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)."

🎯 ICP Refinement & Segmentation

Follow-up Prompt: "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—stop 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)."

💬 Customer Interview Script & Analysis

Follow-up Prompt: "Create a detailed customer interview script for PMF assessment of [PRODUCT]. 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."

📈 Growth Lever Identification

Follow-up Prompt: "Identify the highest-leverage growth opportunities for [PRODUCT] given current PMF state. Analysis: (1) If PMF score is 6-8 (moderate): Growth levers ranked by priority: (a) Improve retention (cohort optimization—get 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."

🔄 PMF Monitoring Dashboard Design

Follow-up Prompt: "Design a real-time PMF monitoring dashboard for [PRODUCT]. 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 (↑↓), benchmark comparison, segment breakdown on hover. (3) Cohort retention curves: Visual chart showing retention curves for last 6 monthly cohorts—check if flattening or decaying. (4) Segment performance matrix: Table showing all customer segments with PMF scores—highlight best-fit (green) and poor-fit (red) segments. (5) Leading indicators: Week 1 activation rate, time to 'aha moment', referral rate—these 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)."

🔀 Pivot Decision Framework

Follow-up Prompt: "Develop a structured pivot decision framework for [PRODUCT] if PMF doesn't improve. Scenario: After 6 months of iteration, PMF score remains <6/10. Framework: (1) Pivot triggers: What metrics/qualitative signals indicate pivot is needed vs. keep iterating? (e.g., Sean Ellis <25% after 3 iteration cycles, retention curves still decaying after onboarding improvements, customer interviews show fundamental product-problem mismatch). (2) Pivot options taxonomy: (a) Customer segment pivot (same product, different ICP), (b) Problem pivot (same customer, different pain point), (c) Solution pivot (same problem, different approach), (d) Business model pivot (same product, different monetization), (e) Platform pivot (reposition product as infrastructure/API vs. end-user app), (f) Zoom in/out pivot (narrow to one feature, or expand to full platform). (3) Evaluation criteria for each pivot option: Market size, Competition intensity, Team expertise fit, Asset reusability (how much can we keep?), Time/cost to validate. (4) Validation plan: For top 2 pivot options, design a 60-day experiment to test with minimal investment (landing page + ads, customer interviews, prototype). (5) Decision gates: After 60 days, if [X metric achieved] → full pivot; if [Y metric not achieved] → shut down or try next pivot option. (6) Shutdown criteria: When to stop pivoting? (runway <6 months, team morale collapse, repeated pivot failures). Provide: Structured decision tree, pivot option evaluation matrix, experimentation plan template."

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