In episode #336, Ben Murray breaks down his top three go-to-market efficiency metrics that every SaaS and AI operator should master. He explains when each metric becomes meaningful, how they differ across go-to-market motions, why ACV-based benchmarking matters, and how these metrics become forward-looking tools through forecasting. Ben also highlights the importance of having fully burdened sales and marketing expenses in place so these efficiency metrics are accurate and defensible.

What You’ll Learn

• The three most important go-to-market efficiency metrics and why they matter
• How ACV—not ARR—should drive your benchmarking
• Why these metrics are proactive when used in forecasting, not just historical
• How revenue types (subscription vs. usage vs. platform/overage) influence metric design
• The foundational role of fully burdened sales and marketing expenses

Why It Matters

• Enables operators to measure the true efficiency of sales and marketing investments
• Provides clarity on the health and scalability of the go-to-market motion
• Helps leadership benchmark realistically against peers using ACV-based expectations
• Allows finance teams to forecast forward-looking efficiency, not just track history
• Ensures efficiency metrics remain accurate as product pricing and revenue models evolve
• Prevents major errors caused by incomplete or misallocated CAC inputs

Resources Mentioned

• Ben’s SaaS Metrics Framework (Pillar 5: Go-to-Market Efficiency): https://www.thesaasacademy.com/the-saas-metrics-foundation
• Ray Rike's benchmarking data at benchmarkit.ai
• Blog posts on modifying metrics for subscription + usage revenue models: https://www.thesaascfo.com/how-to-calculate-cac-payback-period-with-variable-revenue/

In episode #335, Ben answers a common operator question: Should Customer Success be included in the cost of customer acquisition (CAC)? He explains how Customer Success should be coded based on responsibilities, when it belongs in COGS vs. Sales, and when CS expenses should be included in expansion efficiency metrics.

What You’ll Learn

• Why CAC applies only to acquiring new customers.
• How Customer Success roles differ between adoption, retention, renewals, and expansion.
• When Customer Success expenses should be included in the cost of expansion ARR.
• How to allocate Sales, Marketing, and CS expenses between new and existing revenue.
• Why proper allocation is foundational for CAC payback, LTV to CAC, and Cost of ARR.

Why It Matters

• Prevents inflated or misleading CAC and go-to-market efficiency metrics.
• Ensures expansion ARR economics are calculated accurately.
• Helps leaders understand the true cost structure behind revenue growth.
• Supports cleaner financial models, better forecasting, and stronger investor discussions.
• Aligns internal teams (CS, Sales, Finance) on roles and financial impact.

Resources Mentioned

SaaS Metrics course: https://www.thesaasacademy.com/the-saas-metrics-foundation

In episode #334, Ben Murray breaks down how leading public SaaS and tech companies are reporting AI-driven value creation across their earnings calls. After analyzing more than 130 public tech earnings transcripts, Ben identifies five consistent themes in how incumbents communicate AI monetization, margin impact, revenue growth, and operational transformation to Wall Street.

These insights are critical for private SaaS and AI founders who want to understand how to position their own AI value story for Boards, investors, and future fundraising. As AI moves beyond the hype cycle, companies must clearly demonstrate monetization, adoption, and financial impact—not just vision and roadmap.

Why It Matters

Understanding how public companies frame AI value creation helps private founders avoid vague positioning and instead adopt investor-grade communication. These themes influence:

• Board reporting
• Fundraising narratives
• ARR and revenue forecasting
• Financial modeling
• Unit economics and cost structure decisions
• Long-term valuation strategy

As AI transitions from hype to monetization to full transformation, founders must adapt how they report AI’s contribution to performance and financial outcomes.

Resources Mentioned:

Reporting AI ARR: https://www.thesaascfo.com/ai-arr-vs-saas-arr-how-to-define-and-calculate/

SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation

In episode #333, Ben answers a foundational SaaS metrics question: Should expansion revenue be included in your Lifetime Value (LTV) calculation? Ben walks through the correct LTV formula and highlights how misalignment between LTV and CAC can distort your LTV:CAC ratio. He also covers when expansion should be included.

The episode provides a practical framework for SaaS founders, CFOs, and operators to ensure they calculate LTV accurately, compare it properly to CAC, and model unit economics using consistent, reliable inputs.

Key Topics Covered

• The correct LTV formula using average new-customer MRR × subscription gross margin
• Why the churn input should align with dollar-based metrics using 1 – Gross Revenue Retention (GRR)
• Why expansion revenue is deliberately excluded from LTV in most SaaS models
• How including expansion artificially inflates the LTV:CAC ratio
• The cost mismatch between acquiring new customers (CAC) and generating expansion revenue
• When PLG motions justify including limited, time-bound expansion revenue in LTV
• How organic upgrades differ from sales-assisted expansion
• How SaaS+ businesses must adjust their LTV formula to account for usage revenue
• The role of gross margin in determining true unit economics
• The importance of aligning metric definitions when evaluating customer profitability

Why This Matters

This episode is essential for:

• SaaS founders calculating LTV for budgeting, pricing, and forecasting
• CFOs, controllers, and FP&A leaders managing unit economics and CAC payback
• Finance teams modelling customer profitability and revenue expansion
• Operators working in PLG environments assessing organic expansion patterns
• Investors reviewing LTV:CAC ratios in diligence and portfolio monitoring
• Anyone building SaaS Plus (subscription + usage) revenue models

Resources Mentioned

Ben’s deep dive on SaaS+ LTV: https://www.thesaascfo.com/how-to-calculate-ltv-with-variable-revenue/

SaaS Metrics course: https://www.thesaasacademy.com/the-saas-metrics-foundation

In episode #332, Ben Murray explains why AI companies with high inference costs and lower gross profit margins must scale dramatically faster—up to 6x larger—to match the financial performance of a comparable SaaS business. Using simple financial modeling and the core principles of SaaS economics, Ben breaks down how AI margins, variable COGS, and TAM expansion interact to shape the financial trajectory of AI-native companies.

This episode builds on a recent blog post and downloadable Excel model, both linked in the show notes.

Key Topics Covered

• Why SaaS metrics still apply to AI companies, but with different economic inputs
• The impact of AI inference costs on gross margin and scalability
• Comparing a SaaS company at 80 percent gross margin vs. an AI company at 55 percent
• Why an AI company needs 6x the revenue to generate the same EBITDA
• How lower gross profit changes cash flow, EBITDA, and company valuation
• Why larger TAM and higher ACV potential in AI may offset lower margins
• How attacking labor budgets expands revenue opportunity for AI products
• The myth that SaaS metrics are “broken” for AI companies
• Understanding how COGS scale in SaaS vs. AI and why the math still works
• Evaluating OPEX profiles when modeling scale scenarios
• How to use the downloadable template to test scenarios for your own AI or SaaS business

Why This Matters

This episode is critical for:

• AI founders modeling their unit economics
• SaaS founders embedding AI and needing to understand margin changes
• CFOs, controllers, FP&A leaders, and finance teams navigating AI cost structures
• Investors assessing the scalability and valuation profile of AI companies
• Operators planning cash runway, revenue forecasts, and growth investment
• Understanding these financial dynamics early ensures you can forecast accurately, raise capital more effectively, and prepare for due diligence with confidence.

Resources Mentioned

Full blog post on AI vs. SaaS economics: https://www.thesaascfo.com/the-real-economics-of-saas-versus-ai-companies/

SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation

In episode #331, Ben breaks down the true financial and economic differences between a SaaS company and an AI company. Inspired by a tweet claiming that “SaaS metrics are broken” and that AI companies generate more absolute profit per customer, Ben puts the theory to the test using real financial modeling.

This episode walks through detailed revenue, gross margin, EBITDA, pricing power, TAM dynamics, and unit economics scenarios to determine whether AI companies actually outperform SaaS businesses.

What This Episode Covers

• Why investors are questioning traditional SaaS metrics when evaluating AI companies
• The importance of recurring revenue fundamentals, whether the company is SaaS or AI
• A side-by-side comparison of a $1M SaaS company versus a $1M AI company
• Gross margin profiles: 80 percent SaaS vs. 55 percent AI
• How EBITDA changes when OpEx is held constant
• The revenue scale required for an AI company to match SaaS gross profit
• The revenue scale required for an AI company to match SaaS EBITDA
• Why AI companies need a TAM that is 6x larger
• How pricing power tied to labor displacement can shift AI unit economics
• Modeling ARPA increases to see when AI gross profit matches SaaS
• Why the underlying P&L structure does not change, but the inputs do
• How founders should think about forecasting and financial strategy when building AI-native products

Why This Matters

• Founders embedding AI into SaaS products
• AI-native startups modeling their financial future
• CFOs and FP&A leaders forecasting revenue, cash, and margins
• Investors evaluating early-stage AI companies
• Operators building long-term company valuation strategies

Ben emphasizes that the P&L, revenue streams, cost structure, and core KPI’s still apply. What changes are the inputs—gross margin profile, pricing power, TAM, ACV, and scalability assumptions.

Resources Mentioned

• Full blog post with financial modeling examples: https://www.thesaascfo.com/the-real-economics-of-saas-versus-ai-companies
• SaaS metrics course: https://www.thesaasacademy.com/the-saas-metrics-foundation

In episode #330, Ben explains one of the most common and costly SaaS finance mistakes: failing to allocate CAC between new and existing customers. This oversight leads to misleading KPI’s, inaccurate CAC payback, flawed LTV to CAC ratios, and unreliable unit economics. Ben walks through exactly how to allocate CAC the right way, how to segment sales and marketing expenses, and why this matters for accurate revenue efficiency metrics and due diligence.

• Why fully burdened sales and marketing expenses are required for accurate CAC

• The danger of pushing all sales and marketing expenses into CAC without allocation

• How to allocate CAC between new customer acquisition and expansion

• How to segment sales teams (hunters vs. farmers) and avoid co-mingled headcount

• Allocating marketing spend based on acquisition channels

• Typical allocation benchmarks for sales (60-80% to new) and marketing (80-90% to new)

• Why accurate CAC is essential for CAC payback, LTV to CAC, and cost of ARR

• How the Cost of ARR provides a blended benchmark without requiring allocation

• Using allocation methods for businesses with multiple product lines or motions

• How to correctly calculate CAC using fully burdened sales and marketing expenses

• How to evaluate marketing economics and sales efficiency with proper allocation

• Why unallocated CAC leads to distorted financial strategy and misleading KPI’s

• How CAC allocation flows into CAC payback period, LTV to CAC, and ARR efficiency

• How to build a repeatable, defensible go-to-market metrics framework that withstands due diligence

• SaaS founders scaling beyond early customer acquisition

• CFOs, FP&A leaders, and finance teams who own KPI modeling

• Operators who need accurate CAC, CAC payback, and LTV calculations

• Investors or advisors assessing revenue efficiency and go-to-market economics

• SaaS Metrics Foundation course covering CAC, LTV, ARR, and unit economics: https://www.thesaasacademy.com/the-saas-metrics-foundation

• Coaching resources on building an accurate, SaaS-specific chart of accounts: https://www.thesaasacademy.com/saas-cfo-coaching

In episode #329, Ben Murray, The SaaS CFO, breaks down the growing debate around SaaS economics versus AI economics. A recent post claimed that “SaaS metrics are broken” and that traditional KPIs no longer apply to AI companies.

Ben challenges this idea and walks through why recurring revenue metrics still matter, how revenue models differ across SaaS and AI, and what CFOs need to understand about gross margin, unit economics, and total addressable market.

• Why claims that SaaS metrics are “broken” are inaccurate

• The difference between SaaS economics and AI economics

• Why recurring revenue metrics still apply to AI companies

• How subscription versus usage revenue impacts KPI calculation

• Gross margin expectations for SaaS vs. AI companies

• Whether AI companies truly generate more profit per customer

• The role of absolute profit versus per-customer economics

• How AI may expand TAM by targeting labor budgets, not just software budgets

• How Agentic AI affects financial modeling and cost structures

• Using ROSE (Return on Software Employees) to evaluate AI-driven ROI

• Why SaaS metrics still matter for both SaaS and AI companies

• How CFOs should evaluate margins, ARR, and revenue quality in AI models

• The difference between rate-based economics (ARPA, ACV) and volume-based economics (absolute profit)

• How to think about financial strategy when transitioning from a pure SaaS model to an AI-embedded product model

• How to assess realistic AI unit economics instead of relying on hype

• SaaS CFOs and finance leaders evaluating AI investments

• Founders embedding AI into their product and adjusting their financial models

• Operators who want a grounded understanding of real AI economics

• Investors assessing how AI shifts revenue models and margins

• Ben’s upcoming deep-dive blog post on SaaS vs. AI economics: TheSaaSCFO.com

• SaaS Metrics Foundation course for mastering KPI’s, ARR, MRR, and unit economics: https://www.thesaasacademy.com/the-saas-metrics-foundation

• ROSE metric framework for analyzing AI-driven productivity and financial systems: https://www.thesaascfo.com/saas-rose-metric/