Startup Metrics Framework
by wshobson
This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.
Skill Details
Repository Files
1 file in this skill directory
name: startup-metrics-framework description: This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics. version: 1.0.0
Startup Metrics Framework
Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A.
Overview
Track the right metrics at the right stage. Focus on unit economics, growth efficiency, and cash management metrics that matter for fundraising and operational excellence.
Universal Startup Metrics
Revenue Metrics
MRR (Monthly Recurring Revenue)
MRR = Σ (Active Subscriptions × Monthly Price)
ARR (Annual Recurring Revenue)
ARR = MRR × 12
Growth Rate
MoM Growth = (This Month MRR - Last Month MRR) / Last Month MRR
YoY Growth = (This Year ARR - Last Year ARR) / Last Year ARR
Target Benchmarks:
- Seed stage: 15-20% MoM growth
- Series A: 10-15% MoM growth, 3-5x YoY
- Series B+: 100%+ YoY (Rule of 40)
Unit Economics
CAC (Customer Acquisition Cost)
CAC = Total S&M Spend / New Customers Acquired
Include: Sales salaries, marketing spend, tools, overhead
LTV (Lifetime Value)
LTV = ARPU × Gross Margin% × (1 / Churn Rate)
Simplified:
LTV = ARPU × Average Customer Lifetime × Gross Margin%
LTV:CAC Ratio
LTV:CAC = LTV / CAC
Benchmarks:
- LTV:CAC > 3.0 = Healthy
- LTV:CAC 1.0-3.0 = Needs improvement
- LTV:CAC < 1.0 = Unsustainable
CAC Payback Period
CAC Payback = CAC / (ARPU × Gross Margin%)
Benchmarks:
- < 12 months = Excellent
- 12-18 months = Good
-
24 months = Concerning
Cash Efficiency Metrics
Burn Rate
Monthly Burn = Monthly Revenue - Monthly Expenses
Negative burn = losing money (typical early-stage)
Runway
Runway (months) = Cash Balance / Monthly Burn Rate
Target: Always maintain 12-18 months runway
Burn Multiple
Burn Multiple = Net Burn / Net New ARR
Benchmarks:
- < 1.0 = Exceptional efficiency
- 1.0-1.5 = Good
- 1.5-2.0 = Acceptable
-
2.0 = Inefficient
Lower is better (spending less to generate ARR)
SaaS Metrics
Revenue Composition
New MRR New customers × ARPU
Expansion MRR Upsells and cross-sells from existing customers
Contraction MRR Downgrades from existing customers
Churned MRR Lost customers
Net New MRR Formula:
Net New MRR = New MRR + Expansion MRR - Contraction MRR - Churned MRR
Retention Metrics
Logo Retention
Logo Retention = (Customers End - New Customers) / Customers Start
Dollar Retention (NDR - Net Dollar Retention)
NDR = (ARR Start + Expansion - Contraction - Churn) / ARR Start
Benchmarks:
- NDR > 120% = Best-in-class
- NDR 100-120% = Good
- NDR < 100% = Needs work
Gross Retention
Gross Retention = (ARR Start - Churn - Contraction) / ARR Start
Benchmarks:
-
90% = Excellent
- 85-90% = Good
- < 85% = Concerning
SaaS-Specific Metrics
Magic Number
Magic Number = Net New ARR (quarter) / S&M Spend (prior quarter)
Benchmarks:
-
0.75 = Efficient, ready to scale
- 0.5-0.75 = Moderate efficiency
- < 0.5 = Inefficient, don't scale yet
Rule of 40
Rule of 40 = Revenue Growth Rate% + Profit Margin%
Benchmarks:
-
40% = Excellent
- 20-40% = Acceptable
- < 20% = Needs improvement
Example: 50% growth + (10%) margin = 40% ✓
Quick Ratio
Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR)
Benchmarks:
-
4.0 = Healthy growth
- 2.0-4.0 = Moderate
- < 2.0 = Churn problem
Marketplace Metrics
GMV (Gross Merchandise Value)
Total Transaction Volume:
GMV = Σ (Transaction Value)
Growth Rate:
GMV Growth Rate = (Current Period GMV - Prior Period GMV) / Prior Period GMV
Target: 20%+ MoM early-stage
Take Rate
Take Rate = Net Revenue / GMV
Typical Ranges:
- Payment processors: 2-3%
- E-commerce marketplaces: 10-20%
- Service marketplaces: 15-25%
- High-value B2B: 5-15%
Marketplace Liquidity
Time to Transaction How long from listing to sale/match?
Fill Rate % of requests that result in transaction
Repeat Rate % of users who transact multiple times
Benchmarks:
- Fill rate > 80% = Strong liquidity
- Repeat rate > 60% = Strong retention
Marketplace Balance
Supply/Demand Ratio: Track relative growth of supply and demand sides.
Warning Signs:
- Too much supply: Low fill rates, frustrated suppliers
- Too much demand: Long wait times, frustrated customers
Goal: Balanced growth (1:1 ratio ideal, but varies by model)
Consumer/Mobile Metrics
Engagement Metrics
DAU (Daily Active Users) Unique users active each day
MAU (Monthly Active Users) Unique users active each month
DAU/MAU Ratio
DAU/MAU = DAU / MAU
Benchmarks:
-
50% = Exceptional (daily habit)
- 20-50% = Good
- < 20% = Weak engagement
Session Frequency Average sessions per user per day/week
Session Duration Average time spent per session
Retention Curves
Day 1 Retention: % users who return next day Day 7 Retention: % users active 7 days after signup Day 30 Retention: % users active 30 days after signup
Benchmarks (Day 30):
-
40% = Excellent
- 25-40% = Good
- < 25% = Weak
Retention Curve Shape:
- Flattening curve = good (users becoming habitual)
- Steep decline = poor product-market fit
Viral Coefficient (K-Factor)
K-Factor = Invites per User × Invite Conversion Rate
Example: 10 invites/user × 20% conversion = 2.0 K-factor
Benchmarks:
- K > 1.0 = Viral growth
- K = 0.5-1.0 = Strong referrals
- K < 0.5 = Weak virality
B2B Metrics
Sales Efficiency
Win Rate
Win Rate = Deals Won / Total Opportunities
Target: 20-30% for new sales team, 30-40% mature
Sales Cycle Length Average days from opportunity to close
Shorter is better:
- SMB: 30-60 days
- Mid-market: 60-120 days
- Enterprise: 120-270 days
Average Contract Value (ACV)
ACV = Total Contract Value / Contract Length (years)
Pipeline Metrics
Pipeline Coverage
Pipeline Coverage = Total Pipeline Value / Quota
Target: 3-5x coverage (3-5x pipeline needed to hit quota)
Conversion Rates by Stage:
- Lead → Opportunity: 10-20%
- Opportunity → Demo: 50-70%
- Demo → Proposal: 30-50%
- Proposal → Close: 20-40%
Metrics by Stage
Pre-Seed (Product-Market Fit)
Focus Metrics:
- Active users growth
- User retention (Day 7, Day 30)
- Core engagement (sessions, features used)
- Qualitative feedback (NPS, interviews)
Don't worry about:
- Revenue (may be zero)
- CAC (not optimizing yet)
- Unit economics
Seed ($500K-$2M ARR)
Focus Metrics:
- MRR growth rate (15-20% MoM)
- CAC and LTV (establish baseline)
- Gross retention (> 85%)
- Core product engagement
Start tracking:
- Sales efficiency
- Burn rate and runway
Series A ($2M-$10M ARR)
Focus Metrics:
- ARR growth (3-5x YoY)
- Unit economics (LTV:CAC > 3, payback < 18 months)
- Net dollar retention (> 100%)
- Burn multiple (< 2.0)
- Magic number (> 0.5)
Mature tracking:
- Rule of 40
- Sales efficiency
- Pipeline coverage
Metric Tracking Best Practices
Data Infrastructure
Requirements:
- Single source of truth (analytics platform)
- Real-time or daily updates
- Automated calculations
- Historical tracking
Tools:
- Mixpanel, Amplitude (product analytics)
- ChartMogul, Baremetrics (SaaS metrics)
- Looker, Tableau (BI dashboards)
Reporting Cadence
Daily:
- MRR, active users
- Sign-ups, conversions
Weekly:
- Growth rates
- Retention cohorts
- Sales pipeline
Monthly:
- Full metric suite
- Board reporting
- Investor updates
Quarterly:
- Trend analysis
- Benchmarking
- Strategy review
Common Mistakes
Mistake 1: Vanity Metrics Don't focus on:
- Total users (without retention)
- Page views (without engagement)
- Downloads (without activation)
Focus on actionable metrics tied to value.
Mistake 2: Too Many Metrics Track 5-7 core metrics intensely, not 50 loosely.
Mistake 3: Ignoring Unit Economics CAC and LTV are critical even at seed stage.
Mistake 4: Not Segmenting Break down metrics by customer segment, channel, cohort.
Mistake 5: Gaming Metrics Optimize for real business outcomes, not dashboard numbers.
Investor Metrics
What VCs Want to See
Seed Round:
- MRR growth rate
- User retention
- Early unit economics
- Product engagement
Series A:
- ARR and growth rate
- CAC payback < 18 months
- LTV:CAC > 3.0
- Net dollar retention > 100%
- Burn multiple < 2.0
Series B+:
- Rule of 40 > 40%
- Efficient growth (magic number)
- Path to profitability
- Market leadership metrics
Metric Presentation
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
Include:
- Current value
- Growth rate or trend
- Context (target, benchmark)
Additional Resources
Reference Files
references/metric-definitions.md- Complete definitions and formulas for 50+ metricsreferences/benchmarks-by-stage.md- Target ranges for each metric by company stagereferences/calculation-examples.md- Step-by-step calculation examples
Example Files
examples/saas-metrics-dashboard.md- Complete metrics suite for B2B SaaS companyexamples/marketplace-metrics.md- Marketplace-specific metrics with examplesexamples/investor-metrics-deck.md- How to present metrics for fundraising
Quick Start
To implement startup metrics framework:
- Identify business model - SaaS, marketplace, consumer, B2B
- Choose 5-7 core metrics - Based on stage and model
- Establish tracking - Set up analytics and dashboards
- Calculate unit economics - CAC, LTV, payback
- Set targets - Use benchmarks for goals
- Review regularly - Weekly for core metrics
- Share with team - Align on goals and progress
- Update investors - Monthly/quarterly reporting
For detailed definitions, benchmarks, and examples, see references/ and examples/.
Related Skills
Team Composition Analysis
This skill should be used when the user asks to "plan team structure", "determine hiring needs", "design org chart", "calculate compensation", "plan equity allocation", or requests organizational design and headcount planning for a startup.
Startup Financial Modeling
This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate", "estimate runway", "model cash flow", or requests 3-5 year financial planning for a startup.
Market Sizing Analysis
This skill should be used when the user asks to "calculate TAM", "determine SAM", "estimate SOM", "size the market", "calculate market opportunity", "what's the total addressable market", or requests market sizing analysis for a startup or business opportunity.
Anndata
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Geopandas
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between dat
Market Research Reports
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter's Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
Plotly
Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line charts, bar charts, heatmaps, 3D plots, geographic maps, statistical distributions, financial charts, and dashboards. Supports both quick visualizations (Plotly Express) and fine-grained customization (graph objects). Outputs interactive HTML or static images (PNG, PDF, SVG).
Excel Analysis
Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
Neurokit2
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Scientific Schematics
Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
