# Startup Metrics > Startup metrics and frameworks for founders. Use when user wants to assess product-market fit, calculate unit economics, analyze churn, project runway, diagnose funnel conversion, identify growth curves, assess competitive moats, or check fundraise readiness. Triggers on "PMF", "product-market fit", "CAC", "LTV", "churn", "runway", "burn rate", "unit economics", "funnel", "hockey stick", "J-curve", "moat", or /startup commands. - Skill: `orbweva/startup-metrics` (Agent Skill) - Install (CLI): `npx skillmds@latest add orbweva/startup-metrics` - Raw SKILL.md: https://api.skillmd.com/api/skills/orbweva/startup-metrics/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: ORBWEVA (https://skillmd.com/u/orbweva) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/orbweva/startup-metrics --- # Startup Metrics Skill Turn startup jargon into actionable analysis using YOUR actual numbers. Not textbook definitions — real calculations, benchmarks, and verdicts. ## Commands | Command | What it does | |---------|-------------| | `/startup:pmf` | Product-Market Fit assessment (Sean Ellis test + retention curves) | | `/startup:unit` | Unit economics calculator (CAC, LTV, payback, margins) | | `/startup:churn` | Churn diagnosis (logo vs revenue, cohorts, recovery) | | `/startup:runway` | Burn rate + runway projection with scenarios | | `/startup:funnel` | Funnel math + bottleneck identification | | `/startup:growth` | Growth curve analysis (J-curve, hockey stick, linear, flat) | | `/startup:moat` | Competitive moat assessment (7 moat types) | | `/startup:ready` | Fundraise readiness scorecard | | `/startup:glossary` | Explain any startup term using your actual numbers | | `/startup:help` | Command reference | --- ## Stage 1: Product-Market Fit (`/startup:pmf`) **Goal:** Assess whether you've found PMF — with data, not gut feel. ### The Sean Ellis Test The gold standard: survey users with "How would you feel if you could no longer use [product]?" ``` SEAN ELLIS PMF TEST ===================== Responses collected: ___ "Very disappointed": ___% "Somewhat disappointed": ___% "Not disappointed": ___% PMF THRESHOLD: 40%+ "Very disappointed" = PMF achieved ``` ### Interpreting Results | Score | Verdict | Action | |-------|---------|--------| | 40%+ | PMF achieved | Scale acquisition. Focus on growth loops. | | 25-39% | Almost there | Segment — you likely have PMF for a subset. Find them. | | 10-24% | Not yet | Product needs work. Go back to `/dt:empathize`. | | <10% | Wrong market | Pivot the ICP or the product. Don't scale. | ### Segment Analysis When overall score is 25-39%, segment by: ``` SEGMENT ANALYSIS ================= Segment | "Very disappointed" | Sample size | PMF? ─────────────────────┼─────────────────────┼─────────────┼───── All users | ___% | ___ | Completed onboarding | ___% | ___ | Weekly active users | ___% | ___ | Power users (daily) | ___% | ___ | Paid users | ___% | ___ | Industry: ________ | ___% | ___ | Role: ________ | ___% | ___ | ``` **Key insight:** You often have PMF for a segment before you have it overall. Find the segment, double down, expand later. ### Retention Curves (Alternative PMF Signal) If you don't have survey data, retention curves tell the story: ``` RETENTION CURVE ANALYSIS ========================== Cohort: [Month/week] Metric: [DAU, WAU, or key action] Week 1: 100% Week 2: ___% Week 4: ___% Week 8: ___% Week 12: ___% CURVE SHAPE: Flattening (levels off at X%): PMF signal ✓ Declining to zero: No PMF ✗ Smiling (dips then rises): Re-engagement working, investigate why ``` A retention curve that flattens — even at 10% — means SOMEONE finds lasting value. Find out who and why. ### Leading Indicators (Pre-PMF) Before you have enough data for Sean Ellis or retention: ``` PRE-PMF SIGNALS ================ [ ] Organic word-of-mouth (users you didn't acquire) — STRONGEST signal [ ] Users coming back without prompts/emails [ ] Users hacking workarounds for missing features (they care enough to try) [ ] Support requests asking for MORE, not complaining about bugs [ ] Usage increasing week-over-week without marketing spend [ ] Users voluntarily paying (even a small amount) Signals present: ___/6 If 4+: likely approaching PMF. Quantify with Sean Ellis test. If 0-2: keep iterating on the core value proposition. ``` ### Output Deliver: Sean Ellis score + segment analysis + retention curve shape + verdict + specific next action. --- ## Stage 2: Unit Economics (`/startup:unit`) **Goal:** Know your numbers. CAC, LTV, margins, and whether your business model actually works. ### Core Metrics Ask the user for their numbers, then calculate: ``` UNIT ECONOMICS =============== ACQUISITION Marketing spend (monthly): $___ Sales spend (monthly): $___ New customers acquired (monthly): ___ ───────────────────────────────────────── CAC (Customer Acquisition Cost): $___ = (marketing + sales spend) / new customers LIFETIME VALUE ARPU (Avg Revenue Per User/month): $___ Gross margin: ___% Average customer lifespan (months): ___ ───────────────────────────────────────── LTV (Lifetime Value): $___ = ARPU × gross margin × lifespan RATIOS LTV:CAC ratio: ___x Payback period: ___ months = CAC / (ARPU × gross margin) Contribution margin per customer: $___ = LTV - CAC ``` ### Benchmarks ``` LTV:CAC BENCHMARKS ==================== Ratio | Verdict ───────────┼───────────────────────────────────────────── < 1x | Losing money on every customer. Stop spending. 1-2x | Barely breaking even. Fix retention or reduce CAC. 3x | Healthy. The benchmark VCs look for. 3-5x | Strong. Consider spending more on acquisition. > 5x | Possibly under-investing in growth. Scale up. PAYBACK PERIOD BENCHMARKS =========================== Period | Verdict ───────────┼───────────────────────────────────────────── < 6 months | Excellent. Cash-efficient growth possible. 6-12 months| Healthy. Standard for SaaS. 12-18 months| Concerning. Need funding to grow. > 18 months| Dangerous. Fix pricing or churn first. ``` ### When Numbers Are Missing If the user doesn't know exact numbers, help them estimate: ``` ESTIMATION SHORTCUTS ===================== Don't know CAC? → Total marketing spend last 3 months / total new customers last 3 months Don't know lifespan? → 1 / monthly churn rate (e.g., 5% churn = 20 month lifespan) Don't know gross margin? → SaaS: typically 70-85% → Marketplace: typically 15-30% (of GMV) → Services: typically 40-60% → E-commerce: typically 30-50% Don't know ARPU? → MRR / total paying customers ``` ### Output Deliver: Complete unit economics table + LTV:CAC ratio + payback period + benchmark comparison + verdict + recommendations. --- ## Stage 3: Churn Diagnosis (`/startup:churn`) **Goal:** Understand why customers leave and what to do about it. ### Churn Types ``` CHURN BREAKDOWN ================ LOGO CHURN (customer count) Customers at start of month: ___ Customers lost during month: ___ Logo churn rate: ___% = customers lost / customers at start REVENUE CHURN (MRR) MRR at start of month: $___ MRR lost from churned: $___ MRR gained from expansion: $___ Gross revenue churn: ___% = MRR lost / MRR at start Net revenue churn: ___% = (MRR lost - MRR expansion) / MRR at start NET NEGATIVE CHURN: ___ (yes/no) If expansion revenue > lost revenue, you have net negative churn. This is the holy grail — existing customers grow faster than they leave. ``` ### Churn Benchmarks ``` MONTHLY CHURN BENCHMARKS (SaaS) ================================== Segment | Good | Acceptable | Concerning ───────────────┼─────────┼────────────┼────────── SMB | < 3% | 3-5% | > 5% Mid-market | < 1.5% | 1.5-2.5% | > 2.5% Enterprise | < 0.5% | 0.5-1% | > 1% Consumer/B2C | < 5% | 5-8% | > 8% ANNUAL EQUIVALENTS (for context): 2% monthly = 21.5% annual 5% monthly = 46.0% annual 8% monthly = 63.2% annual ``` ### Churn Reason Taxonomy Categorize every churn into one of these buckets: ``` CHURN REASONS ============== PRODUCT (you can fix) [ ] Missing feature they expected [ ] Bug or reliability issue [ ] Onboarding failure (never reached aha moment) [ ] Performance too slow FIT (you might fix) [ ] Outgrew your product (moved to enterprise tool) [ ] Wrong ICP (should never have been a customer) [ ] Seasonal use case (only needed it temporarily) EXTERNAL (you can't fix) [ ] Company went out of business [ ] Budget cuts / downsizing [ ] Champion left the company [ ] Acquired / merged PRICE (you can test) [ ] Too expensive for perceived value [ ] Found a cheaper alternative [ ] Downgraded but stayed (not full churn) INVOLUNTARY (you must fix) [ ] Failed payment / card expired [ ] Dunning emails not set up [ ] Account deactivated by error ``` ### Cohort Analysis Framework ``` COHORT RETENTION TABLE ======================== Month 0 Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Jan '26: 100% ___% ___% ___% ___% ___% ___% Feb '26: 100% ___% ___% ___% ___% ___% Mar '26: 100% ___% ___% ___% ___% Apr '26: 100% ___% ___% ___% May '26: 100% ___% ___% LOOK FOR: - Are newer cohorts retaining better? (product improving) - Is there a cliff at Month X? (onboarding or aha moment issue) - Do cohorts flatten? (you have a retained core) ``` ### Recovery Playbook ``` CHURN RECOVERY ACTIONS ======================== Prevention (before they churn): [ ] Usage-drop alert (activity < 50% of average for 2+ weeks) [ ] Proactive outreach at risk signals [ ] Health score per account (usage × recency × breadth) [ ] QBRs for high-value accounts Intervention (during cancellation): [ ] Cancel flow with reason selection (feeds taxonomy above) [ ] Offer pause instead of cancel [ ] Offer downgrade instead of cancel [ ] Human outreach for high-LTV accounts Win-back (after they churn): [ ] 30-day win-back email (what's changed since they left) [ ] 90-day win-back (major feature announcement) [ ] Involuntary churn: auto-retry failed payments + dunning sequence ``` ### Output Deliver: Churn rates (logo + revenue) + benchmark comparison + reason taxonomy analysis + cohort table + recovery recommendations. --- ## Stage 4: Runway (`/startup:runway`) **Goal:** How many months until the money runs out? And what can you do about it? ### Runway Calculator ``` RUNWAY PROJECTION ================== Cash in bank: $___ Monthly revenue (MRR): $___ Monthly expenses: $___ Monthly burn rate: $___ = expenses - revenue (gross burn = expenses, net burn = expenses - revenue) RUNWAY: ___ months = cash / net burn rate DEFAULT ALIVE? ___ (Paul Graham's question: if you stop fundraising and maintain current growth rate, do you ever become profitable?) ``` ### Scenario Modeling ``` SCENARIOS ========== | Maintain | Cut 20% | Accelerate ────────────────────┼───────────┼───────────┼─────────── Monthly burn: | $___ | $___ | $___ Monthly revenue: | $___ | $___ | $___ Growth rate: | ___% | ___% | ___% Runway: | ___ mo | ___ mo | ___ mo Break-even in: | ___ mo | ___ mo | ___ mo Cash needed: | $___ | $___ | $___ RECOMMENDATION: [maintain / cut / accelerate] because [reason] ``` ### Burn Rate Benchmarks ``` BURN RATE CONTEXT ================== Stage | Typical monthly burn | Notes ───────────────────┼──────────────────────┼───────────── Pre-seed (1-3 ppl) | $10-30K | Mostly salaries Seed (3-8 ppl) | $50-150K | Team + tools + small marketing Series A (8-20 ppl)| $150-400K | Growth hiring + acquisition spend Series B+ | $400K-1M+ | Scaling everything MINIMUM SAFE RUNWAY: Pre-fundraise: 18+ months (need 6mo buffer for fundraising) Post-fundraise: 12+ months before next raise "Danger zone": < 6 months with no revenue path ``` ### Key Decision Framework ``` RUNWAY DECISION TREE ====================== > 18 months: Safe. Focus on growth and PMF. 12-18 months: Start planning next fundraise or path to profitability. 6-12 months: URGENT. Cut non-essential spend. Start fundraising NOW. < 6 months: EMERGENCY. Cut to bare minimum. Bridge round or profitability. If DEFAULT ALIVE = yes: → You have optionality. Fundraise from a position of strength. If DEFAULT ALIVE = no: → You MUST either raise, cut, or grow faster. No other options. ``` ### Output Deliver: Runway calculation + 3 scenarios + default alive assessment + specific recommendations. --- ## Stage 5: Funnel Math (`/startup:funnel`) **Goal:** Find where customers drop off and how to fix it. ### Funnel Framework ``` CONVERSION FUNNEL ================== Stage | Count | Conv. rate | Benchmark ────────────────┼──────────┼────────────┼────────── Visitors | ___ | | → Signups | ___ | ___% | 2-5% (SaaS) → Activated | ___ | ___% | 20-40% → Engaged | ___ | ___% | 30-60% → Paying | ___ | ___% | 2-10% (freemium), 15-30% (trial) → Retained | ___ | ___% | see churn benchmarks BIGGEST DROP-OFF: _________ → _________ (___% lost) ``` ### Benchmarks by Model ``` CONVERSION BENCHMARKS ====================== MODEL: Freemium Visitor → Signup: 2-5% Signup → Activated: 20-40% Free → Paid: 2-5% (Slack-like) 5-10% (mid-market) MODEL: Free Trial (no CC) Visitor → Signup: 5-15% Signup → Activated: 30-50% Trial → Paid: 15-25% MODEL: Free Trial (CC required) Visitor → Signup: 1-3% Signup → Activated: 50-70% Trial → Paid: 40-60% MODEL: Sales-Led Visitor → Lead: 1-3% Lead → SQL: 10-20% SQL → Opportunity: 30-50% Opportunity → Closed: 15-30% ``` ### Bottleneck Diagnosis ``` BOTTLENECK IDENTIFICATION ============================ For each stage transition, compare your rate to benchmark: Stage | Your rate | Benchmark | Gap | Priority ────────────────────┼───────────┼───────────┼────────┼───────── Visitor → Signup | ___% | ___% | ___% | Signup → Activated | ___% | ___% | ___% | Activated → Paying | ___% | ___% | ___% | Paying → Retained | ___% | ___% | ___% | LARGEST GAP = your #1 bottleneck. Fix priority: activation > retention > conversion > traffic. (No point driving traffic to a leaky funnel.) ``` ### Funnel Math Show the compounding impact of fixing each stage: ``` IMPACT MODELING ================ Current: 10,000 visitors → ___ signups → ___ active → ___ paying = $___/mo Scenario A: Fix signup rate (2% → 4%) 10,000 → ___ signups → ___ active → ___ paying = $___/mo (+___%) Scenario B: Fix activation (25% → 40%) 10,000 → ___ signups → ___ active → ___ paying = $___/mo (+___%) Scenario C: Fix conversion (5% → 8%) 10,000 → ___ signups → ___ active → ___ paying = $___/mo (+___%) HIGHEST IMPACT: Scenario ___ (+___%) ``` ### Output Deliver: Funnel table with conversion rates + benchmark comparison + bottleneck identification + impact modeling + fix recommendation. --- ## Stage 6: Growth Curve Analysis (`/startup:growth`) **Goal:** Identify what growth phase you're in and what to expect next. ### Growth Curve Types ``` GROWTH CURVES ============== J-CURVE (most startups) ╲ ╲___________╱ ╱ ╱ ╱ Phase 1: Dip (spending > earning, building, iterating) Phase 2: Inflection (PMF found, economics work) Phase 3: Acceleration (compounding growth) HOCKEY STICK ____________╱ ╱ Long flat → sudden acceleration Usually triggered by: viral moment, PR hit, platform change, or hitting critical mass in a network effect LINEAR ╱ ╱ ╱ Steady, predictable growth. Not bad — but won't attract VC funding. Common in services, agencies, and bootstrapped SaaS. S-CURVE ___________ ╱ ╱ ________╱ Slow start → rapid growth → plateau (market saturated) Healthy. Time to find the next S-curve (new market, new product). FLAT / DECLINING ──────────___ ╲ Stagnation. Either no PMF, market saturated, or execution problem. ``` ### Diagnose Your Curve ``` GROWTH DIAGNOSIS ================= Metric: [MRR / users / revenue] Time period: [last 6-12 months] Month 1: ___ Month 2: ___ (___% MoM) Month 3: ___ (___% MoM) Month 4: ___ (___% MoM) Month 5: ___ (___% MoM) Month 6: ___ (___% MoM) Average MoM growth: ___% Trend (accelerating/steady/decelerating): ___ CURVE TYPE: ___ PHASE: ___ ``` ### Growth Rate Benchmarks ``` MoM GROWTH RATE BENCHMARKS ============================= Rate | Grade | Context ───────────┼───────┼─────────────────────────────────── > 20% MoM | A+ | Top decile. YC-caliber growth. 15-20% MoM | A | Excellent. Series A-ready. 10-15% MoM | B | Good. Keep compounding. 5-10% MoM | C | Decent for bootstrapped. Slow for VC. < 5% MoM | D | Stalling. Diagnose why. Negative | F | Declining. Urgent action needed. CONTEXT: 10% MoM = 3.1x annual. 20% MoM = 8.9x annual. T2D3 FRAMEWORK (for VC-backed SaaS): Year 1-2: Triple revenue (3x) Year 3-5: Double revenue (2x) $1M ARR → $3M → $9M → $18M → $36M → $72M ``` ### Output Deliver: Growth data plotted + curve type + phase diagnosis + growth rate grade + what to expect next + recommended actions for current phase. --- ## Stage 7: Competitive Moat Assessment (`/startup:moat`) **Goal:** Assess how defensible your business is — and what to strengthen. ### Seven Moat Types Score each moat type (0-5) for your business: ``` MOAT ASSESSMENT ================ Moat Type | Score (0-5) | Evidence ───────────────────────┼─────────────┼──────────────────── Network Effects | /5 | [More users = more value?] Switching Costs | /5 | [Cost/pain of leaving?] Economies of Scale | /5 | [Cheaper at volume?] Brand / Trust | /5 | [Would users pay more for your name?] Data Advantage | /5 | [Data that improves with use?] Regulatory / Legal | /5 | [Licenses, patents, compliance?] Counter-positioning | /5 | [Incumbents can't copy without hurting themselves?] ──────────────────────────────────────────────────────────── TOTAL MOAT SCORE: ___/35 MOAT STRENGTH: 28-35: Fortress (extremely defensible) 21-27: Strong (multiple defensible layers) 14-20: Moderate (some defensibility, needs strengthening) 7-13: Weak (vulnerable to well-funded competitors) 0-6: No moat (competing on execution alone) ``` ### Moat Deepening Strategies ``` MOAT DEEPENING PLAYBOOK ========================= Network Effects (score < 3): → Add collaboration features (multi-user = sticky) → Build a marketplace or community layer → Create integrations that connect users Switching Costs (score < 3): → Store user data in proprietary formats → Build workflows that accumulate over time → Offer migration from competitors, never to competitors Data Advantage (score < 3): → Use product usage data to improve the product → Build features that get better with more customers → Create benchmarks / insights only possible with aggregate data Counter-positioning (score < 3): → Identify what incumbents CAN'T do without cannibalizing revenue → Build your product around that gap → Example: Notion vs Microsoft — bundled simplicity vs unbundled complexity ``` ### "Can a Big Company Copy This?" Test ``` COPY RISK ASSESSMENT ====================== If [biggest competitor] copied your product tomorrow: 1. How long to build? ___ months 2. Would their users adopt it? ___ 3. Would it hurt their core? ___ 4. Do they have your data? ___ 5. Do they have your brand? ___ If answers are: short, yes, no, yes, no → HIGH RISK, you need a moat If answers are: long, no, yes, no, yes → LOW RISK, you have defensibility ``` ### Output Deliver: Moat scorecard (7 types) + total score + weakest moat identified + specific deepening recommendations + copy risk assessment. --- ## Stage 8: Fundraise Readiness (`/startup:ready`) **Goal:** Score whether your startup is ready to raise — and what to fix first. ### Fundraise Readiness Scorecard ``` FUNDRAISE READINESS SCORECARD ================================ TRACTION (30 points) [ /10 ] Revenue growth rate (MoM or YoY) [ /10 ] User growth and engagement metrics [ /10 ] PMF evidence (Sean Ellis score, retention curves) UNIT ECONOMICS (20 points) [ /10 ] LTV:CAC ratio (≥ 3x for full marks) [ /5 ] Payback period (< 12 months) [ /5 ] Gross margin (> 70% for SaaS) MARKET (15 points) [ /5 ] TAM size and credibility of estimate [ /5 ] Growth of target market [ /5 ] Competitive positioning clarity TEAM (15 points) [ /5 ] Founder-market fit (relevant experience) [ /5 ] Technical co-founder or team capability [ /5 ] Key hires made or identified PRODUCT (10 points) [ /5 ] Product maturity (beyond MVP?) [ /5 ] Clear roadmap with customer-validated priorities NARRATIVE (10 points) [ /5 ] Can explain the business in 1 sentence [ /5 ] Compelling "why now" and "why us" story TOTAL: ___/100 ``` ### Score Interpretation | Score | Verdict | Action | |-------|---------|--------| | 80-100 | Ready to raise | Polish the deck, start outreach | | 60-79 | Almost ready | Fix the weakest category first | | 40-59 | Not yet | 2-3 months of focused work needed | | <40 | Too early | Build more, prove more, then reassess | ### What VCs Actually Look For (by Stage) ``` VC EXPECTATIONS BY STAGE ========================== PRE-SEED ($500K-2M) Team + vision + early signal PMF not required, but must show insight "Why will this be massive?" SEED ($2-5M) Early PMF signal + initial traction $5-50K MRR or strong usage metrics "Show me the retention curve" SERIES A ($5-15M) Clear PMF + repeatable growth $100K+ MRR, growing 15%+ MoM Unit economics trending positive "Show me the growth machine" SERIES B ($15-50M) Proven unit economics at scale $500K+ MRR, clear path to $10M ARR Sales/marketing engine working "Show me the path to market leadership" ``` ### Common Fundraise Killers ``` RED FLAGS VCS SPOT IMMEDIATELY ================================= [ ] High churn with no explanation [ ] CAC increasing while growth slows [ ] No clear ICP ("our market is everyone") [ ] Burn rate doesn't match stage [ ] Cap table problems (too many investors, bad terms) [ ] No technical co-founder for a tech product [ ] Competitor already raised 10x more and has traction [ ] "AI wrapper" with no proprietary data or model [ ] Revenue concentration (one customer = 50%+ of MRR) [ ] Founders can't articulate unit economics ``` ### Output Deliver: Readiness scorecard + total score + weakest areas + stage-appropriate expectations + red flag check + specific fixes before fundraising. --- ## Stage 9: Startup Glossary (`/startup:glossary`) **Goal:** Explain any startup term using the user's actual numbers — not textbook definitions. ### How to Use When the user asks about a term, don't just define it. Calculate it with their data if available, show what "good" looks like, and connect it to their situation. ### Core Terms ``` METRICS MRR — Monthly Recurring Revenue. Sum of all active subscriptions this month. ARR — Annual Recurring Revenue. MRR × 12. The number VCs use for SaaS valuations. ARPU — Average Revenue Per User. MRR / total paying customers. ACV — Annual Contract Value. Average annual deal size. NRR — Net Revenue Retention. Revenue from existing customers this year / last year. > 100% = expansion. > 120% = exceptional. GMV — Gross Merchandise Value. Total transaction volume (for marketplaces). ACQUISITION CAC — Customer Acquisition Cost. Total sales + marketing spend / new customers. Blended CAC — All-channel CAC (including organic). Usually lower than paid CAC. Paid CAC — Only paid channel spend / customers from paid channels. CPA — Cost Per Acquisition. Same as CAC in most contexts. CPL — Cost Per Lead. Marketing spend / leads generated. RETENTION LTV — Lifetime Value. ARPU × gross margin × lifespan. Churn — % of customers (logo) or revenue lost per period. Cohort — Group of customers who started at the same time. Used to compare retention. DAU/MAU — Daily/Monthly Active Users. DAU/MAU ratio measures engagement (> 20% = good). GROWTH MoM — Month-over-Month growth rate. CMGR — Compound Monthly Growth Rate. Smoothed average over a period. K-factor — Viral coefficient. Invites × conversion rate. > 1 = viral growth. T2D3 — Triple-triple-double-double-double. VC growth benchmark. FUNDRAISING TAM — Total Addressable Market. Revenue if you had 100% market share. SAM — Serviceable Addressable Market. TAM you can actually reach. SOM — Serviceable Obtainable Market. SAM you'll realistically capture. Burn rate — Monthly cash spent (gross = total, net = expenses minus revenue). Runway — Cash / net burn rate = months until out of money. Dilution — % of ownership given up per funding round. PRODUCT PMF — Product-Market Fit. Validated demand for your product in your market. PQL — Product-Qualified Lead. User whose product usage signals sales readiness. PQA — Product-Qualified Account. Same concept at company level (better for B2B). TTV — Time to Value. How quickly a new user reaches the aha moment. Aha moment — The action that makes a user "get it" and become likely to stay. ``` ### Output Deliver: Definition + calculation using their numbers (if available) + benchmark + what it means for their specific situation. --- ## Integration Notes - **Feeds from DT:** `/dt:empathize` identifies user pain → `/startup:pmf` validates they want a solution - **Feeds from GTM:** `/gtm:positioning` defines ICP → `/startup:unit` proves you can profitably serve them - **Feeds to GTM:** `/startup:funnel` finds bottlenecks → `/gtm:landing` fixes the conversion page - **Feeds to GTM:** `/startup:churn` identifies reasons → `/gtm:growth` builds retention loops - **Feeds to Pitch:** `/startup:ready` identifies gaps → fix them → then raise ## Principles 1. **Numbers over narratives.** "We're growing fast" means nothing. "18% MoM over 6 months" means everything. 2. **Segment before you despair.** Overall metrics hide pockets of PMF, healthy cohorts, and power users. 3. **Fix the leakiest bucket first.** Activation > retention > conversion > traffic. Always. 4. **Default alive is the question.** If you can't answer it, calculate it now with `/startup:runway`. 5. **Benchmarks are context, not targets.** A 2% churn rate is terrible for enterprise SaaS and amazing for consumer. 6. **Your moat is what you're building, not what you have.** Score it honestly. Strengthen the weakest point. 7. **Fundraise readiness is not fundraise timing.** Being ready doesn't mean you should raise. Being ready means you CAN when you choose to.