In episode #343 of SaaS Metrics School, Ben Murray demystifies SaaS revenue by breaking down the core revenue types that software, SaaS, and AI companies should be modeling on their P&L. Rather than focusing on labels, Ben explains why pricing models and revenue streams are the real drivers of financial clarity.

He walks through the most common revenue categories—subscriptions, variable usage-based revenue, professional services, managed services, hardware, and other emerging models—and shows how proper revenue segmentation becomes the foundation for accurate retention metrics, forecasting, unit economics, and due diligence readiness.

Resources Mentioned

• SaaS Metrics School framework: https://www.thesaascfo.com/scaling-with-confidence-the-ultimate-saas-metrics-playbook/
• Concepts covered in Ben’s SaaS Metrics course: https://www.thesaasacademy.com/the-saas-metrics-foundation
• MRR schedules & MRR waterfalls: https://www.thesaasacademy.com/offers/rJhZ6VdM/checkout

What You’ll Learn

• The core revenue categories every SaaS, software, and AI company should track
• How subscription and usage-based revenue differ financially
• Why overages must be separated from subscription revenue
• How revenue segmentation enables accurate MRR schedules and waterfalls
• Why retention should be calculated separately by revenue stream
• How revenue structure impacts forecasting accuracy
• How different revenue streams change CAC payback and LTV to CAC calculations
• Why clean revenue categorization simplifies due diligence

Why It Matters

• Revenue segmentation is the foundation of accurate SaaS metrics
• MRR schedules and retention calculations depend on clean revenue data
• Forecasts are more reliable when built from revenue waterfalls
• Mixed revenue streams require adjusted CAC payback calculations
• Clear revenue structure improves investor and acquirer confidence
• Proper setup reduces friction during fundraising and exits

In episode #342 of SaaS Metric School, Ben breaks down the Cost of ARR metric and explains why it’s one of the most practical and revealing go-to-market efficiency metrics for 2026 planning. He covers where the metric originated, how to calculate it correctly, and how to use it to sanity-check forecasts and budgets.

Ben walks through the three variations of Cost of ARR (blended, new, and expansion), explains why bookings data—not revenue—is required, and shows how benchmarking by ACV provides far more insight than aggregate benchmarks.

Resources Mentioned

• Benchmarkit.ai for SaaS metrics benchmarks
• Cost of ARR framework: https://www.thesaascfo.com/saas-cac-ratio/
• SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation

What You’ll Learn

• What the Cost of ARR metric is and why it matters for SaaS and AI companies
• The difference between blended, new, and expansion Cost of ARR
• Why Cost of ARR must be based on bookings, not revenue
• How improper CAC allocation distorts Cost of ARR results
• How to use Cost of ARR to validate 2026 forecasts and budgets
• Why benchmarking by ACV size is more accurate than company size
• What top-quartile Cost of ARR performance looks like across ACV ranges

Why It Matters

• Cost of ARR quickly exposes unrealistic bookings forecasts
• It connects sales and marketing spend directly to ARR outcomes
• The metric helps right-size go-to-market investment for 2026
• ACV-based benchmarks prevent misleading efficiency comparisons
• Tracking trends over time highlights improving or degrading efficiency
• Cost of ARR works across PLG, sales-led, SaaS, and AI models

In episode #341 of SaaS Metrics School, Ben Murray explains why revenue per FTE is a misleading metric for modern SaaS and AI companies and introduces the ROSE metric (Return on SaaS Employees) as a more accurate way to measure durable scaling.

Ben walks through how ROSE removes labor-cost bias, incorporates contractors and Agentic AI spend, and directly connects people investment to recurring revenue generation. He also shares practical benchmark ranges and explains how founders and finance teams should use ROSE when budgeting and forecasting for 2026.

Resources Mentioned

ROSE Metric Template: https://www.thesaascfo.com/saas-rose-metric/

ROSE Metric Bootcamp: https://www.thesaasacademy.com/offers/rJhZ6VdM

What You’ll Learn

• Why revenue per FTE breaks down in global and AI-driven teams
• How the ROSE metric improves capital allocation decisions
• What costs should be included in ROSE
• ROSE benchmark ranges and how they map to profitability and cash burn
• How to interpret ROSE differently based on growth stage and company goals
• How to forecast ROSE using trailing and forward-looking time periods

Why It Matters

• People and AI spend are the largest investments on a SaaS or AI P&L
• ROSE removes wage and geography bias from efficiency analysis
• The metric directly ties recurring revenue to capital deployed
• ROSE highlights whether headcount and AI investment are creating leverage
• Improving ROSE over time is critical for durable, profitable scaling
• Boards and investors care about efficiency trends, not just growth rates