Ai Startup Metrics

install
source · Clone the upstream repo
git clone https://github.com/wpank/ai
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/wpank/ai "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/marketing/startup-metrics" ~/.claude/skills/wpank-ai-startup-metrics && rm -rf "$T"
manifest: skills/marketing/startup-metrics/SKILL.md
source content

Startup Metrics Framework

Track, calculate, and optimize key performance metrics for different startup business models from seed through Series B+.

Installation

OpenClaw / Moltbot / Clawbot

npx clawhub@latest install startup-metrics

NEVER Do

  • Focus on vanity metrics (total users without retention, page views without engagement, downloads without activation)
  • Track 50 metrics loosely instead of 5-7 core metrics intensely
  • Ignore unit economics at any stage — CAC and LTV matter even at seed
  • Present metrics without context (benchmark, target, or trend)
  • Optimize dashboard numbers instead of real business outcomes
  • Skip segmentation — always break down by customer segment, channel, and cohort

Data

The

data/metrics.csv
file contains 42 metrics with formulas, benchmarks by stage, interpretation guidance, and related metrics. Use it for lookups and cross-referencing.

How to Use This Skill

  1. Identify business model — SaaS, marketplace, consumer/mobile, or B2B
  2. Determine stage — Pre-seed, seed, Series A, Series B+
  3. Select 5-7 core metrics based on model and stage
  4. Apply formulas and benchmarks from the sections below
  5. Recommend tracking cadence and reporting format

Universal Metrics (All Models)

Revenue

MetricFormulaSeed TargetSeries A Target
MRRSum(active subs x monthly price)$10K-$50K$200K-$800K
ARRMRR x 12$120K-$600K$2M-$10M
MoM Growth(current - prior) / prior15-20%10-15%
YoY Growth(current year - prior year) / prior yearN/A3-5x

Unit Economics

MetricFormulaHealthy Benchmark
CACTotal S&M spend / new customersVaries by model
LTVARPU x gross margin% x (1/churn rate)LTV:CAC > 3.0
CAC PaybackCAC / (ARPU x gross margin%)< 12 months
Gross Margin(revenue - COGS) / revenue70-85% for SaaS

Cash Management

MetricFormulaTarget
Burn Ratemonthly expenses - monthly revenueTrack gross and net
Runwaycash balance / monthly burnAlways 12-18+ months
Burn Multiplenet burn / net new ARR< 2.0 (lower is better)

SaaS-Specific Metrics

Revenue Composition

Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR

Retention

MetricFormulaBest-in-Class
Net Dollar Retention (NDR)(start + expansion - contraction - churn) / start> 120%
Gross Retention(start - churn - contraction) / start> 90%
Logo Retention(end - new) / start> 95% monthly

Efficiency

MetricFormulaReady to Scale
Magic Numbernet new ARR (quarter) / S&M spend (prior quarter)> 0.75
Rule of 40revenue growth% + profit margin%> 40%
Quick Ratio(new + expansion MRR) / (churned + contraction MRR)> 4.0

Marketplace Metrics

MetricFormulaTarget
GMVsum of all transaction values20%+ MoM early-stage
Take Ratenet revenue / GMV10-20% (model-dependent)
Liquidity (Fill Rate)transactions / listings> 70%
Repeat Rateusers with 2+ txns / total transacting> 60%
Time to First Transactionmedian signup-to-transaction< 3 days

Consumer / Mobile Metrics

MetricFormulaBenchmark
DAU/MAU Ratiodaily active / monthly active> 20% good, > 50% exceptional
Day 30 Retention% users active 30d after signup> 25%
K-Factor (Virality)invites per user x conversion rate> 1.0 = viral
Session Durationtotal time / sessionsContext-dependent
NPS% promoters - % detractors> 50 excellent

B2B Sales Metrics

MetricFormulaTarget
Win Ratedeals won / total opportunities20-40%
Pipeline Coveragepipeline value / quota3-5x
ACVtotal contract value / contract yearsTrack trends
Sales Cycleavg days from opportunity to closeSMB: 30-60d, Enterprise: 120-270d

Pipeline Conversion Rates

StageTypical Rate
Lead → Opportunity10-20%
Opportunity → Demo50-70%
Demo → Proposal30-50%
Proposal → Close20-40%

Metrics by Stage

Pre-Seed (Product-Market Fit)

Focus: Active users growth, user retention (D7/D30), core engagement, qualitative feedback (NPS, interviews).

Ignore for now: Revenue, CAC, unit economics.

Seed ($500K-$2M ARR)

Focus: MRR growth (15-20% MoM), CAC and LTV baselines, gross retention (>85%), product engagement.

Start tracking: Sales efficiency, burn rate, runway.

Series A ($2M-$10M ARR)

Focus: ARR growth (3-5x YoY), unit economics (LTV:CAC >3, payback <18mo), NDR (>100%), burn multiple (<2.0), magic number (>0.5).

Mature tracking: Rule of 40, sales efficiency, pipeline coverage.

Series B+ ($10M+ ARR)

Focus: Rule of 40 (>40%), efficient growth, path to profitability, market leadership.

Tracking Best Practices

Reporting Cadence

FrequencyMetrics
DailyMRR, active users, signups, conversions
WeeklyGrowth rates, retention cohorts, sales pipeline
MonthlyFull metric suite, board reporting, investor updates
QuarterlyTrend analysis, benchmarking, strategy review

Dashboard Format

Current MRR: $250K (↑ 18% MoM)
ARR: $3.0M (↑ 280% YoY)
CAC: $1,200 | LTV: $4,800 | LTV:CAC = 4.0x
NDR: 112% | Logo Retention: 92%
Burn: $180K/mo | Runway: 18 months

Always include: current value, growth rate/trend, context (target or benchmark).

What VCs Want to See

RoundKey Metrics
SeedMRR growth rate, user retention, early unit economics, product engagement
Series AARR + growth, CAC payback <18mo, LTV:CAC >3.0, NDR >100%, burn multiple <2.0
Series B+Rule of 40 >40%, magic number, path to profitability, market leadership

Data Infrastructure

Requirements

  • Single source of truth (analytics platform)
  • Real-time or daily updates for core metrics
  • Automated calculations (no manual spreadsheets for recurring metrics)
  • Historical tracking for trend analysis and cohort comparisons

Recommended Tools

CategoryTools
Product analyticsMixpanel, Amplitude, PostHog
SaaS metricsChartMogul, Baremetrics, ProfitWell
BI dashboardsLooker, Metabase, Tableau
Cohort analysisBuilt-in analytics + spreadsheets for custom analysis

Common Mistakes

  1. Vanity metrics — Focus on actionable metrics tied to value, not totals without context
  2. Too many metrics — Track 5-7 core metrics intensely, not 50 loosely
  3. Ignoring unit economics — CAC and LTV matter even at seed stage
  4. Not segmenting — Break down by customer segment, channel, cohort
  5. Gaming metrics — Optimize for real business outcomes, not dashboard numbers

Metric Calculation Examples

LTV Calculation

ARPU: $200/month
Gross Margin: 80%
Monthly Churn: 3%

LTV = $200 × 0.80 × (1/0.03) = $5,333

Burn Multiple

Net Burn: $150K/month
Net New ARR (quarter): $300K

Burn Multiple = ($150K × 3) / $300K = 1.5

Interpretation: Spending $1.50 to generate each $1 of new ARR — acceptable efficiency.

Magic Number

Net New ARR (Q2): $400K
S&M Spend (Q1): $500K

Magic Number = $400K / $500K = 0.80

Interpretation: Above 0.75 threshold — efficient, ready to scale S&M investment.

Quick Ratio

New MRR: $40K
Expansion MRR: $15K
Churned MRR: $8K
Contraction MRR: $4K

Quick Ratio = ($40K + $15K) / ($8K + $4K) = 4.58

Interpretation: Above 4.0 — healthy growth significantly outpacing churn.

Investor Metric Presentation

Present metrics with three components:

  1. Current value — the number itself
  2. Growth rate or trend — direction and velocity
  3. Context — benchmark, target, or peer comparison
Current MRR: $250K (↑ 18% MoM)
ARR: $3.0M (↑ 280% YoY)
CAC: $1,200 | LTV: $4,800 | LTV:CAC = 4.0x
NDR: 112% | Logo Retention: 92%
Burn: $180K/mo | Runway: 18 months
Burn Multiple: 1.8x | Magic Number: 0.65

Quick Start

To implement this framework:

  1. Identify business model — SaaS, marketplace, consumer, B2B
  2. Choose 5-7 core metrics — based on stage and model
  3. Establish tracking — set up analytics and dashboards
  4. Calculate unit economics — CAC, LTV, payback
  5. Set targets — use benchmarks from this skill
  6. Review regularly — weekly for core metrics, monthly for full suite
  7. Share with team — align on goals and progress
  8. Update investors — monthly or quarterly reporting

Data Reference

The

data/metrics.csv
file contains 42 metrics with:

  • Unique ID and category classification
  • Formulas for calculation
  • Benchmarks by stage (seed, Series A, Series B)
  • Interpretation guidance for each metric
  • Related metrics for cross-referencing and dependency tracking