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Our Methodology

How we calculate metrics, derive benchmarks, and ensure data accuracy

Last updated: December 28, 2025

Data Sources

Our insights are derived from multiple reliable sources:

  • Aggregated Platform Data: Anonymous, aggregated metrics from companies using QuantLedger, representing a diverse cross-section of SaaS businesses
  • Industry Research: Published benchmarks from established sources including OpenView Partners, KeyBanc, and Stripe
  • Public Company Data: SEC filings and public financial statements from SaaS companies
  • Direct Research: Original research conducted through surveys, interviews, and analysis

Metric Calculations

All metrics are calculated using industry-standard formulas:

Monthly Recurring Revenue (MRR)

MRR = Sum of all active subscription values normalized to monthly

Excludes one-time charges, setup fees, and non-recurring revenue

Customer Lifetime Value (LTV)

LTV = (ARPU × Gross Margin) / Monthly Churn Rate

Uses gross margin LTV for accurate unit economics

Customer Acquisition Cost (CAC)

CAC = (Sales + Marketing Costs) / New Customers Acquired

Fully-loaded CAC including salaries, tools, and overhead

Net Revenue Retention (NRR)

NRR = (Starting MRR + Expansion - Contraction - Churn) / Starting MRR × 100

Measures revenue retention from existing customers over 12 months

Benchmark Methodology

Our benchmarks are segmented for accuracy and relevance:

  • By Company Stage: Seed, Series A, Series B+, and bootstrapped companies show different patterns
  • By Business Model: B2B, B2C, usage-based, and hybrid models are analyzed separately
  • By Industry Vertical: Fintech, DevTools, MarTech, and other verticals have distinct benchmarks
  • By ARR Range: <$1M, $1M-$10M, $10M-$50M, and $50M+ companies are grouped separately

All benchmarks represent medians unless otherwise specified. Percentile ranges (25th, 75th) are provided where data volume allows.

Data Quality & Privacy

We take data quality and privacy seriously:

  • Anonymization: All aggregated data is fully anonymized. No individual company data is ever shared or identifiable
  • Minimum Sample Sizes: Benchmarks require minimum sample sizes to ensure statistical validity
  • Outlier Treatment: Extreme outliers are reviewed and excluded when they would distort results
  • Data Validation: Automated checks identify and flag data quality issues before analysis

Update Frequency

Our content and data are regularly updated:

  • Real-time Metrics: Platform metrics update in real-time as new data is synced
  • Benchmark Updates: Industry benchmarks are refreshed quarterly
  • Article Reviews: Blog content is reviewed and updated at minimum annually, with last updated dates displayed on each article
  • Methodology Reviews: This methodology document is reviewed and updated as our processes evolve

Limitations & Caveats

We believe in transparency about what our data can and cannot tell you:

  • Benchmarks represent general trends and may not apply to every specific situation
  • Our platform data skews toward companies actively measuring metrics, which may differ from the broader market
  • Historical comparisons should account for market conditions and external factors
  • All projections and forecasts involve uncertainty and should not be treated as guarantees

When making business decisions, we recommend using our data as one input among many, including your own judgment, industry expertise, and professional advisors.

Questions About Our Methodology?

We welcome questions and feedback about our methodology. Contact us at data@quantledger.app