| name | afrexai-startup-metrics-engine |
| model | default |
| version | 1.0.0 |
| description | Complete startup metrics command center — from raw data to investor-ready dashboards. Covers every stage (pre-seed to Series B+), every model (SaaS, marketplace, consumer, hardware), with diagnostic frameworks, benchmark databases, and board-ready reporting.
|
| tags | ["startup","metrics","saas","kpis","unit-economics","growth","fundraising","investor","dashboard","arr","mrr","churn","ltv","cac"] |
Startup Metrics Command Center
Your complete system for tracking, diagnosing, and communicating startup health — not just formulas, but the thinking behind what to measure, when, and what to do when numbers go wrong.
Phase 1: Metrics Architecture
Step 1 — Identify Your Model & Stage
Before tracking anything, classify yourself:
Business Model:
model_type:
saas:
sub_type:
pricing:
contract:
marketplace:
type:
unit:
consumer:
type:
engagement_model:
hardware_plus_software:
type:
Stage (determines what matters):
| Stage | ARR Range | North Star Focus | Board Cares About |
|---|
| Pre-seed | $0-$50K | Engagement + retention signal | Problem-solution fit evidence |
| Seed | $50K-$500K | Cohort retention + early revenue | Product-market fit signals |
| Series A | $500K-$3M | Growth efficiency + unit economics | LTV:CAC, NDR, growth rate |
| Series B | $3M-$15M | Scalability + operating leverage | Rule of 40, magic number, burn multiple |
| Growth | $15M+ | Capital efficiency + market share | Net margins, NRR, competitive moat |
Step 2 — Build Your Metric Stack
Layer 1: Health Vitals (track daily)
- Revenue: MRR, ARR, net new MRR
- Growth: MoM growth rate, WoW for early stage
- Retention: Logo churn rate, revenue churn rate
- Cash: Monthly burn, runway in months
Layer 2: Efficiency (track weekly)
- Unit economics: CAC, LTV, LTV:CAC ratio, payback months
- Sales: Pipeline coverage, win rate, sales cycle length
- Product: Activation rate, feature adoption, NPS/CSAT
- Team: Revenue per employee, quota attainment
Layer 3: Strategic (track monthly)
- NDR (Net Dollar Retention)
- Burn multiple
- Rule of 40 score
- Magic number
- Cohort analysis curves
Phase 2: The Complete Formula Reference
Revenue Metrics
MRR = Σ(active_subscriptions × monthly_price)
ARR = MRR × 12
Net New MRR = New MRR + Expansion MRR - Churned MRR - Contraction MRR
MRR Components:
new_mrr: First-time customer revenue this month
expansion_mrr: Upsell + cross-sell from existing customers
churned_mrr: Revenue lost from customers who left
contraction_mrr: Revenue lost from downgrades (customer stayed)
reactivation_mrr: Revenue from returning churned customers
MoM Growth = (MRR_current - MRR_previous) / MRR_previous
CMGR (Compound Monthly Growth Rate) = (MRR_end / MRR_start)^(1/months) - 1
Why CMGR > MoM: Monthly growth is noisy. CMGR smooths 6-12 month periods for real trend.
Unit Economics
CAC = Total_Sales_Marketing_Spend / New_Customers_Acquired
- Include: salaries, commissions, tools, ads, events, content costs
- Exclude: product/engineering, CS (post-sale)
- Time-lag adjustment: match spend to cohort it generated (typically 1-3 month lag)
Blended CAC vs Channel CAC:
blended_cac = total_spend / total_new_customers
channel_cac = channel_spend / channel_new_customers
# Always track both — blended hides channel problems
LTV = ARPU × Gross_Margin% × Average_Customer_Lifetime
# Or: LTV = ARPU × Gross_Margin% × (1 / Monthly_Churn_Rate)
# Cap at 5 years for conservative estimates
LTV:CAC Ratio — THE ratio:
> 5.0 → Under-investing in growth (spend more!)
3.0-5.0 → Excellent efficiency
1.5-3.0 → Healthy but watch payback period
1.0-1.5 → Marginal — fix churn or reduce CAC
< 1.0 → Burning cash per customer — STOP and fix
CAC Payback = CAC / (Monthly_ARPU × Gross_Margin%)
< 6 months → Elite (PLG companies)
6-12 months → Great
12-18 months → Acceptable for enterprise
> 18 months → Danger zone (unless >130% NDR)
Retention & Churn
Logo Churn Rate = Customers_Lost / Customers_Start_of_Period
Revenue Churn Rate = MRR_Lost / MRR_Start_of_Period
# Revenue churn > logo churn = losing big customers (very bad)
# Revenue churn < logo churn = losing small customers (less bad)
Net Dollar Retention (NDR) = (Starting_MRR + Expansion - Contraction - Churn) / Starting_MRR
> 130% → World-class (Snowflake, Twilio territory)
110-130% → Excellent
100-110% → Good
90-100% → Acceptable but concerning
< 90% → Leaky bucket — growth can't outrun churn
Gross Dollar Retention (GDR) = (Starting_MRR - Contraction - Churn) / Starting_MRR
# NDR without expansion — shows your floor
> 90% → Sticky product
80-90% → Normal for SMB
< 80% → Product or market problem
Growth Efficiency
Burn Multiple = Net_Burn / Net_New_ARR
< 1.0 → Amazing (rare at early stage)
1.0-1.5 → Great
1.5-2.0 → Good
2.0-3.0 → Mediocre
> 3.0 → Bad — inefficient growth
Rule of 40 = Revenue_Growth_Rate% + Profit_Margin%
> 40 → Healthy SaaS (IPO-ready)
# Example: 60% growth + -20% margin = 40 ✓
# Example: 20% growth + 20% margin = 40 ✓
Magic Number = Net_New_ARR_This_Quarter / Sales_Marketing_Spend_Last_Quarter
> 1.0 → Efficient, invest more in S&M
0.5-1.0 → OK, optimize before scaling
< 0.5 → Inefficient — fix before spending more
Hype Ratio = Valuation / ARR
# Reality check on fundraising expectations
# Median SaaS multiples: 6-12x ARR (varies by growth + retention)
Cash & Runway
Monthly Burn = Total_Monthly_Expenses - Total_Monthly_Revenue
Gross Burn = Total_Monthly_Expenses (ignoring revenue)
Net Burn = Gross_Burn - Revenue
Runway = Cash_Balance / Monthly_Net_Burn
> 18 months → Comfortable
12-18 months → Start planning next raise
6-12 months → Urgently fundraising
< 6 months → Default alive or dead calculation needed
Default Alive? = Can_Current_Growth_Rate_Make_Revenue > Expenses_Before_Cash_Runs_Out
# Paul Graham's test — if growing, project the intersection
Sales Efficiency
Sales Cycle Length = Avg_Days(First_Touch → Closed_Won)
Pipeline Coverage = Total_Pipeline_Value / Revenue_Target
# Need 3-4x for predictable revenue
Win Rate = Deals_Won / Total_Deals_in_Stage
By stage: SQL→Opp (30-40%), Opp→Proposal (50-60%), Proposal→Close (60-70%)
ACV (Annual Contract Value) = Total_Contract_Value / Contract_Years
ASP (Average Selling Price) = Total_Revenue / Deals_Closed
Quota Attainment = Actual_Bookings / Quota_Target
# Healthy org: 60-70% of reps hitting quota
Sales Efficiency = Net_New_ARR / Fully_Loaded_Sales_Cost
> 1.0 → Scalable
Phase 3: Diagnostic Framework — PULSE Method
When a metric is off, don't just report it — diagnose it.
P — Pattern Recognition
Questions:
- Is this a trend (3+ months) or a blip (1 month)?
- Is it seasonal or structural?
- Did it change gradually or suddenly?
- Which cohorts/segments are affected?
U — Upstream Tracing
Every metric has upstream drivers. Trace back:
Revenue declining? →
├── New MRR down? → Lead volume? → Conversion rate? → Channel performance?
├── Expansion down? → Upsell attempts? → Product adoption? → CSM activity?
└── Churn up? → Which segment? → Voluntary vs involuntary? → Reasons?
CAC increasing? →
├── Spend up? → Which channels? → CPM/CPC changes?
├── Volume same but cost up? → Market saturation? → Competition?
└── Conversion down? → Funnel stage? → Lead quality? → Sales process?
L — Leverage Point
Find the highest-impact intervention:
- Which single metric, if improved 10%, would cascade the most?
- What's the cheapest/fastest fix vs highest-impact fix?
- Score: Impact (1-5) × Feasibility (1-5) × Speed (1-5)
S — So-What Translation
Convert metric into business language:
- "Churn increased 2%" → "We'll lose $X00K ARR this year at this rate"
- "CAC payback is 18 months" → "Each new customer is cash-negative for 1.5 years"
- "NDR is 95%" → "Even with zero new sales, we shrink 5% annually"
E — Experiment Design
diagnostic_experiment:
hypothesis: "[Metric] is declining because [upstream cause]"
test: "[Specific action] for [time period]"
success_metric: "[Metric] improves by [X%] within [timeframe]"
sample: "[Segment/cohort to test on]"
kill_criteria: "Stop if [negative signal] within [days]"
Phase 4: Cohort Analysis — The Truth Machine
Aggregate metrics lie. Cohorts tell the truth.
Revenue Cohort Table
Track each monthly cohort's MRR over time:
Month 0 Month 1 Month 3 Month 6 Month 12
Jan '25 $50K $48K $45K $42K $38K
Feb '25 $55K $53K $50K $48K —
Mar '25 $60K $58K $57K $56K —
Apr '25 $45K $44K $43K — —
Reading this:
- Jan cohort retained 76% at month 12 → mediocre
- Mar cohort retained 93% at month 3 → improving! What changed?
- Apr cohort started smaller but retention looks good
Engagement Cohort (Non-Revenue Signal)
cohort_engagement:
week_1_activation:
week_4_habit:
month_3_retention:
Cohort Red Flags
🚩 Each new cohort retains worse → product-market fit eroding
🚩 Large cohorts churn more → scaling quality issues
🚩 Specific channel cohorts churn fast → bad-fit leads
🚩 Expansion only in old cohorts → pricing/packaging problem
Phase 5: Board & Investor Reporting
Monthly Investor Update Template
investor_update:
subject: "[Company] — [Month] Update: [One-line headline]"
highlights:
- "ARR: $X (+Y% MoM) — [context]"
- "Key win: [biggest achievement]"
- "Challenge: [biggest problem + what you're doing]"
metrics:
arr: {current: "", prior_month: "", delta: ""}
mrr: {current: "", growth_mom: ""}
customers: {total: "", new: "", churned: ""}
ndr: ""
burn_rate: ""
runway_months: ""
cash_balance: ""
wins: []
challenges: []
next_month_priorities: []
asks:
- intro: "Looking for intro to [person/company] for [reason]"
- advice: "Would love 15 min on [specific topic]"
- hiring: "Seeking [role] — know anyone?"
Board Deck Metric Slides
Slide 1: Business Health Dashboard
ARR: $___ MoM: ___% NDR: ___%
Customers: ___ New: ___ Churned: ___
Runway: ___ months Burn Multiple: ___
Traffic light: 🟢 On track | 🟡 Watch | 🔴 Action needed
Slide 2: Revenue Waterfall
Starting MRR: $___
+ New: $___
+ Expansion: $___
- Contraction: $___
- Churn: $___
= Ending MRR: $___
Slide 3: Unit Economics
CAC: $___ → LTV: $___ → LTV:CAC: ___x
Payback: ___ months
Blended vs top channel efficiency
Phase 6: Model-Specific Metrics
SaaS Additions
Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)
> 4.0 → Very healthy growth
2.0-4.0 → Good
1.0-2.0 → Sustainable but slow
< 1.0 → Shrinking
Logo-to-Revenue Retention Gap:
If logo retention 85% but revenue retention 95% → upsell compensates
If logo retention 85% and revenue retention 85% → no expansion = problem
Expansion Revenue % = Expansion MRR / Total New MRR
> 30% → Healthy at scale
# Best SaaS: expansion > new revenue (Twilio was 170% NDR)
Marketplace Additions
GMV (Gross Merchandise Value) = Total value of transactions on platform
Take Rate = Platform Revenue / GMV
5-15% → Typical for most marketplaces
15-30% → Managed/full-service marketplaces
Supply-side metrics:
supply_liquidity = listings_with_transaction / total_listings
time_to_first_match = avg_days_from_listing_to_sale
Demand-side metrics:
search_to_fill = completed_transactions / searches
repeat_purchase_rate = returning_buyers / total_buyers
Consumer/PLG Additions
DAU/MAU Ratio:
> 50% → Exceptional (messaging apps)
25-50% → Strong habit (social, productivity)
10-25% → Good (media, entertainment)
< 10% → Weak engagement
Viral Coefficient (K-factor) = Invites_per_User × Conversion_Rate
> 1.0 → Viral growth (each user brings >1 new user)
0.5-1.0 → Amplified growth
< 0.5 → Not viral — need paid acquisition
Free-to-Paid Conversion:
PLG benchmark: 2-5% of free users convert
Freemium benchmark: 1-3%
Enterprise self-serve: 5-15%
Time to Value = Time from signup to "aha moment"
# Reduce this aggressively — strongest lever for activation
Phase 7: Metric Manipulation Red Flags
Vanity vs Real Metrics
| Vanity (Avoid) | Real (Track) |
|---|
| Total signups | Activated users (completed key action) |
| Page views | Engaged sessions (>2 min or action taken) |
| "Pipeline" | Qualified pipeline (met ICP criteria) |
| Gross revenue | Net revenue (after refunds + credits) |
| Total customers | Active customers (logged in last 30d) |
| Downloads | WAU/MAU |
| "Partnerships" | Revenue from partnerships |
Common Manipulation Tactics to Watch
🚩 Counting annual contracts as MRR at signing (vs. monthly recognition)
🚩 Excluding "one-time" churns from churn rate
🚩 Using gross revenue instead of net
🚩 Measuring CAC without fully-loaded costs
🚩 Cherry-picking best cohort as "representative"
🚩 Counting reactivations as new customers
🚩 Using "committed ARR" (signed but not live)
🚩 Trailing-12-month NDR when recent cohorts are worse
Phase 8: Action Playbooks
When CAC Is Too High
1. Audit channel efficiency — kill bottom 20% channels
2. Improve activation rate (reduces wasted spend)
3. Increase conversion at each funnel stage (+10% each = compound effect)
4. Shift mix: more organic/PLG, less paid
5. Reduce sales cycle length (lower cost per deal)
6. Tighten ICP — stop selling to bad-fit customers
When Churn Is Too High
1. Segment: which customers churn? (Size, channel, use case)
2. Time: when do they churn? (Month 1-3 = onboarding, 6-12 = value, 12+ = competition)
3. Reason: exit survey + CS interviews (top 3 reasons)
4. Fix activation if month 1-3 churn
5. Fix value delivery if month 6-12 churn
6. Fix switching cost / competitive moat if 12+ churn
When Growth Stalls
1. Check: is TAM exhausted in current segment? → Expand to adjacent
2. Check: conversion rates declining? → Product or message fatigue
3. Check: CAC rising with flat volume? → Channel saturation
4. Check: expansion revenue flat? → Packaging/pricing problem
5. Check: sales cycle lengthening? → Market conditions or competition
When Raising Capital
Metrics investors care about BY STAGE:
Pre-seed: Engagement, retention curves, market size
Seed: MoM growth (15%+), retention cohorts, early unit economics
Series A: $1M+ ARR, 3x+ YoY growth, LTV:CAC > 3, NDR > 100%
Series B: $5M+ ARR, path to Rule of 40, burn multiple < 2, sales efficiency
Quick Commands
- "Set up metrics for [stage] [model] startup" → Full metric stack recommendation
- "Diagnose [metric]" → PULSE diagnostic framework
- "Build investor update for [month]" → Template with guidance
- "Cohort analysis on [data]" → Retention curve analysis
- "Compare us to benchmarks" → Gap analysis vs stage-appropriate benchmarks
- "What metrics for Series [A/B] raise?" → Investor-ready checklist
- "Calculate unit economics from [data]" → Full LTV, CAC, payback analysis
- "Red flag check" → Scan metrics for warning signs
- "Board deck metrics" → Generate slide-ready metric views
Edge Cases
Multi-Product Companies
Track metrics per product line AND blended. Watch for cross-subsidization where one product's margins mask another's losses.
Usage-Based Pricing
MRR is estimated, not contracted. Track committed vs consumed. Expansion is automatic (usage growth), so NDR is naturally higher — compare to usage-based peers, not seat-based.
Negative Churn via Price Increases
If NDR > 100% only because of price increases (not organic expansion), this is fragile. Separate price-driven vs usage-driven expansion.
Very Early Stage (Pre-Revenue)
Track leading indicators: activation rate, engagement frequency, NPS, waitlist growth, organic traffic, time-to-value. Revenue metrics come later — don't force them.
Seasonal Businesses
Use YoY comparisons, not MoM. Adjust cohort analysis for seasonal patterns. Build seasonal forecast models.
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