Startup Metrics

Startup Metrics Skill

Turn startup jargon into actionable analysis using YOUR actual numbers. Not textbook definitions — real calculations, benchmarks, and verdicts.

Commands

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

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

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.