In episode #347 of SaaS Metrics School, Ben Murray explores the lesser-discussed nuances behind ARR (Annual Recurring Revenue) disclosures. Building on the prior two episodes on ARR definitions and common disclosure mistakes, this discussion dives into the assumptions and gray areas that often underlie headline ARR numbers.

Drawing on extensive research across public tech company filings, Ben explains how assumptions about renewals, timing, and grace periods can materially affect how ARR is interpreted by boards, investors, and acquirers.

Resources Mentioned

• Blog post: In-depth analysis of ARR definitions and disclosure practices: https://www.thesaascfo.com/cfos-guide-to-disclosing-headline-arr-numbers/
• SaaS Metrics course: https://www.thesaasacademy.com/the-saas-metrics-foundation

What You’ll Learn

• Why most ARR definitions assume full renewal of existing contracts
• How ARR disclosures typically avoid assumptions around expansion, contraction, or churn
• Why ARR is almost always a point-in-time metric rather than a forecast
• Common disclaimers used to separate ARR from GAAP revenue and financial guidance
• How grace periods for contract renewals can materially affect reported ARR—and how some public companies quantify that risk

Why It Matters

• ARR assumptions directly influence how investors assess revenue durability
• Poorly explained ARR nuances can create confusion during due diligence
• Grace periods can inflate perceived recurring revenue if not disclosed properly
• Transparent ARR disclosures strengthen credibility with boards and potential buyers
• A defensible ARR definition supports better financial strategy and valuation discussions

In episode #346 of SaaS Metrics School, Ben Murray breaks down the most common mistakes SaaS and AI companies make when disclosing their ARR (Annual Recurring Revenue). Building on the prior episode about the five questions every ARR definition must answer, this discussion focuses on where ARR disclosures go wrong—and why unclear definitions can damage credibility with investors, boards, and acquirers.

Drawing from extensive research on public tech company filings and press releases, Ben explains how vague ARR definitions, hidden mechanics, and inconsistent methodologies create confusion and risk during fundraising, valuation discussions, and due diligence.

Resources Mentioned

• Prior episode: The 5 Questions Your ARR Definition Must Answer
• SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation
• Blog post on ARR: https://www.thesaascfo.com/cfos-guide-to-disclosing-headline-arr-number

What You’ll Learn

• Why a company’s pricing model does not always match its ARR model
• The importance of clearly defining which revenue streams are included in ARR
• Common issues with vague annualization periods (monthly vs. quarterly vs. trailing periods)
• How poor disclosure of usage-based or variable revenue creates misleading ARR numbers
• Why ARR definition changes and restatements require clear explanation and transparency

Why It Matters

• Clear ARR disclosure builds trust with investors, boards, and business leaders
• Poorly defined ARR can undermine company valuation and fundraising conversations
• Inconsistent ARR definitions make benchmarking and financial modeling unreliable
• Transparent ARR mechanics reduce follow-up questions during due diligence
• Strong financial strategy starts with defensible, repeatable revenue metrics

Defining ARR is getting harder—not easier—as SaaS, AI, usage-based pricing, and hybrid business models evolve. In episode #345 of SaaS Metrics School, Ben Murray breaks down the five critical questions every ARR definition must answer to hold up with Boards, investors, and during due diligence.

Drawing on extensive research into how public tech companies disclose ARR in press releases and SEC filings, Ben explains why ARR is not “dead” but why vague or inconsistent ARR definitions undermine credibility, comparability, and company valuation. This episode provides a practical framework to help SaaS leaders, CFOs, and founders clearly define ARR in a way that supports accurate metrics, financial modeling, and investor trust.

Resources Mentioned

• Blog post on ARR definitions and disclosure best practices: https://www.thesaascfo.com/cfos-guide-to-disclosing-headline-arr-numbers/
• Ben's SaaS Metrics training: https://www.thesaasacademy.com/the-saas-metrics-foundation

You’ll Learn

• The five questions every ARR definition must answer to be investor-ready
• Which revenue types belong in ARR—and which should be excluded
• The difference between revenue-based, contract-based, and hybrid ARR calculations
• How public SaaS and AI companies annualize subscription and usage-based revenue
• Common approaches for handling variable, consumption, and usage revenue in ARR
• Why vague ARR definitions create confusion in fundraising and due diligence

Why It Matters

• Clear ARR definitions improve credibility with investors and business leaders
• Poorly defined ARR can negatively impact company valuation
• Consistent ARR logic enables better KPI tracking and benchmarking
• Transparent ARR disclosures reduce friction during fundraising and M&A
• Accurate ARR supports stronger financial strategy and forecasting
• Well-defined revenue categories improve accounting and financial systems

In episode #344 of SaaS Metrics School, Ben Murray shares insights from his research into how public tech companies define and disclose ARR in press releases and SEC filings. By analyzing U.S. and global public companies, Ben identifies common ARR “buckets” and explains how different revenue models influence what gets included in ARR.

Rather than debating whether ARR is “dead,” this episode focuses on how companies are actually reporting ARR today—and what private SaaS and AI companies can learn from those disclosures.

Resources Mentioned

• Subscribe to Ben’s SaaS newsletter: https://mailchi.mp/df1db6bf8bca/the-saas-cfo-sign-up-landing-page
  Verint (example of detailed SaaS and AI ARR disclosures): https://www.thesaascfo.com/ai-arr-vs-saas-arr-how-to-define-and-calculate/

What You’ll Learn

• The most common ARR buckets used by public SaaS and tech companies
• How pure subscription revenue is typically defined in ARR
• How companies handle variable revenue such as usage, transactions, and overages
• When managed services revenue is included in ARR—and when it isn’t
• Why purely usage-based companies rarely report ARR
• How revenue models and pricing structures shape ARR definitions
• What ARR disclosures signal to investors and the public markets

Why It Matters

• ARR definitions directly impact how investors interpret growth
• Clear ARR buckets improve transparency and credibility
• Mixed revenue models require thoughtful ARR construction
• Public company disclosures set expectations for private companies
• Poor ARR definitions can confuse metrics, forecasting, and valuation
• Understanding ARR structure helps align finance, accounting, and reporting

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

In episode #340 of SaaS Metrics School, Ben breaks down what rising CFO confidence—now at a four-year high—means for SaaS and AI operators planning for the year ahead. Using insights from Deloitte’s latest CFO survey, Ben explains why optimism alone isn’t enough and why companies must pair confidence with strong financial systems, accurate forecasting, and reliable metrics.

The conversation centers on how leaders should prepare for potential market upturns while still balancing growth, efficiency, and risk, especially in a fast-moving AI-driven environment.

What You’ll Learn

• Key takeaways from Deloitte’s CFO confidence survey
• How CFO sentiment impacts budgeting, forecasting, and financial strategy
• Why cost management and productivity remain top priorities despite rising confidence
• The four critical SaaS finance data sources needed for reliable forecasting
• Why weak financial foundations limit decision-making and execution speed
• How proper revenue, bookings, and MRR data support long-term planning

Why It Matters

• Higher confidence increases pressure to make faster, higher-stakes decisions
• Accurate financial modeling depends on clean accounting and revenue data
• Reliable MRR and bookings data enable realistic growth and ARR forecasts
• Strong financial systems help leaders respond quickly to market shifts
• Investors and boards expect disciplined planning, not optimism-driven projections
• SaaS and AI companies without solid data foundations struggle to scale efficiently

Resources Mentioned

• Deloitte CFO Confidence Survey (via Ben’s newsletter): https://mailchi.mp/cd86087f90ac/cfo-confidence-at-highest-level-in-4-years
• SaaS Metrics Course at The SaaS Academy: https://www.thesaasacademy.com/the-saas-metrics-foundation