Explore every episode of the podcast SaaS Metrics School

Only 9% of public software companies trade above 10x revenue — do you know which metrics separate them from the 68% stuck below 5x?

In episode #389, Ben Murray breaks down what public comps reveal about how premium SaaS valuations are actually created. You can't value a private SaaS business straight off public multiples, but the correlations between metrics and enterprise value tell you exactly what buyers reward — and if you're a founder or CFO eyeing an exit, these are the numbers that will decide whether you land the index median or the premium multiple.

• The real distribution of public software multiples — 68% below 5x revenue, 23% at 5–10x, and just 9% above 10x — and what it takes to reach the cream of the crop.
• How net revenue retention maps to valuation: NRR below 100% earns a 3.1x EV-to-revenue multiple, the index median sits at 5.7x, and companies above 120% NRR command 9.3x.
• Why retention sits at the top of the valuation pyramid and drives premium multiples in bear markets and bull markets alike.
• Why two companies hitting the same Rule of 40 get different valuations — and why 30% growth + 10% EBITDA beats 30% EBITDA + 10% growth.
• How to pressure-test your own exit readiness: if you can't confidently enter your metrics into a valuation calculator, your finance data foundation isn't ready for due diligence.

Tune in to see where your SaaS would land on the valuation curve — before a buyer runs the numbers for you.

• Ben's SaaS Exit Readiness blog post (four pillars + valuation framework): 
• Contact Ben for help on preparing for an exit: https://www.thesaascfo.com/contact/

Founders, if a buyer asked for your metrics tomorrow, could your data prove your financial performance or would holes in it kill the deal?

In episode #388, Ben Murray breaks down the metrics acquirers use to underwrite your SaaS business — and why so many exits fail before they start. Due diligence is about de-risking the acquisition, and buyers with deep bench strength will pour through your data looking for weaknesses. If you can't control the financial narrative with accurate, defensible metrics, they'll write it for you — and your valuation will pay the price.

• The four metric pillars buyers scrutinize in due diligence — retention quality, growth, capital efficiency, and margin architecture — and why retention is still king.
• The GRR threshold many private equity firms treat as a hard floor (hint: it's 90%) and why the gap between GRR and NRR matters for every recurring revenue stream.
• Why two companies with the same Rule of 40 score get very different valuations — and which composition earns the premium.
• The capital efficiency metrics on the exam — burn multiple, CAC payback, ARR per FTE — and how go-to-market efficiency changes how much capital a buyer must invest post-acquisition.
• Why AI-native margins are rotating back to 70–80% software expectations, and how a correctly structured SaaS P&L lets you explain your margin by revenue stream.
• The minimum runway Ben recommends for exit prep — 6 months of data foundation work — so you hand buyers numbers you're confident in.

Tune in before your next investor conversation — because if you're eyeing an exit in the next year or two, the data prep starts now.

• Ben's SaaS Exit Readiness blog post: https://www.thesaascfo.com/saas-exit-readiness-score/
• Contact Ben for exit-ready services: https://www.thesaascfo.com/contact/

Is your dev team's AI spend buried in the wrong part of your P&L — quietly distorting your gross margin and making your AI ROI impossible to measure?

In episode #387, Ben Murray tackles one of the most pressing accounting questions hitting SaaS CFOs and controllers right now: how to properly classify AI development costs across your chart of accounts. With dev teams racking up daily charges from multiple AI providers, getting these expense codes wrong doesn't just create accounting noise — it misrepresents your gross margin, obscures your true AI economics, and leaves you unable to answer board questions about AI ROI. The right GL structure makes all the difference.

• The single most important question to answer first: is the AI cost production-facing (customer-serving inference) or internal engineering use — because the answer determines whether it hits COGS or OpEx, and your gross margin depends on getting this right.
• A full breakdown of the five AI cost categories SaaS companies are facing today — production inference, dev/testing inference, model training and fine-tuning, GPU compute for production serving, and vector database infrastructure — and exactly where each one belongs on the P&L.
• Why model training and fine-tuning costs may need to be capitalized rather than expensed, just like other qualifying software development costs under existing accounting guidance.
• Why internal AI tool spend (Copilot, ChatGPT, Claude, etc.) needs its own dedicated GL account — separate from internal-use software — so you can actually measure efficiency gains and answer the AI ROI question when the board asks.
• Why this isn't optional housekeeping: as AI costs grow, the difference between properly coded and miscoded AI expenses will show up directly in your gross margin and in how investors read your unit economics.

Tune in to get the GL coding framework SaaS CFOs are building now — before AI expenses get too big to untangle.

• Ben's AI COGS blog post (detailed GL coding guidance): https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/

Is your ERP dashboard actually built on data that matters — or is it just a chart of accounts dressed up to look useful?

In episode #386, Ben Murray breaks down why traditional ERP dashboards are losing ground to AI-generated, prompt-built SaaS reporting and what that means for CFOs and finance leaders right now. If your team is still relying on static dashboards anchored to your general ledger, you're missing three out of four key SaaS data sources before you even start the analysis. The gap between what ERP dashboards can show and what modern AI-native metrics engines can produce is widening fast  and the CFOs who close that gap first will be the ones driving the board conversations.

• Why ERP dashboards are fundamentally limited to chart-of-accounts data — and the three additional SaaS data sources (HRIS, bookings, and customer/revenue data) that actually drive metrics like CAC payback, LTV to CAC, NRR, and Rule of 40.
• How Ben vibe-coded a full SaaS metrics dashboard in minutes using Claude — covering ARR trajectory, EBITDA margin, gross margin, revenue per FTE, and cash balance — and why a prompt-built report on a deterministic engine beats any canned dashboard.
• Why controlling the period of measurement matters: the example of setting CAC payback on a six-month sales cycle basis — something a standard ERP dashboard simply can't do.
• Where AI actually belongs in the FP&A process: not at the beginning, but at the end — writing board narratives, flagging dormant customers ripe for expansion via a RevIntel engine, and generating insights that traditional FP&A could never surface.
• Why agent-friendly APIs matter: how Saster's API grading tool surfaces whether your SaaS stack is actually exposing the data AI needs to take action — not just technically having an API.

Tune in to understand exactly where your ERP dashboard ends and where a closed-loop, AI-powered metrics engine takes over — before your next board meeting.

• Ben's LinkedIn post (vibe-coded SaaS metrics report): https://www.linkedin.com/posts/benrmurray_saas-activity-7498039803582689280-7Ckt?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAOOEO8Bf5aRLyU0jjrGXvPD2odJNDer6KU
• Ben's deterministic SaaS metrics engine: https://softwaremetrics.ai
• Ben's Five Pillar SaaS Metrics Framework: https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026

What if AI could produce a better board memo than any CFO — and you could trust every data point in it?

In episode #385, Ben Murray breaks down how he is using AI to generate complete 5-page financial board reports, and more importantly, why the outputs are reliable. This isn't AI hype — it's a working FP&A process Ben is running today inside his fractional CFO practice. If you're still manually assembling dashboards, PDFs, and narrative summaries before every board meeting, this episode reframes what's actually possible right now.

• Why a deterministic metrics engine — not AI — is the non-negotiable foundation that makes AI-written board reports trustworthy (and how Ben built one backed by 35 pages of documentation)
• The exact structure of a SaaS financial board report: executive overview, metrics vs. benchmarks, ARR growth, customer retention, GTM efficiency, financial capacity, data anomalies, and top priorities — all generated by AI
• How Ben identified 84 unique data points in a single AI-written board memo using ChatGPT — and why that number changes how you think about QA
• The MCP connection to SoftwareMetrics.ai that makes this a repeatable monthly process — and how Replit is now building it as a native app feature
• What's coming in October: Ben's SaaS Metric Sprint, a live walkthrough of building the data foundation, calculating metrics, and connecting MCP so you can run this for your own company

Tune in to see how Ben is turning month-end close into a board-ready narrative in minutes — and how you can build the same process for your SaaS company.

• Ben's app: https://softwaremetrics.ai/
• SaaS Metric Sprint (October 6th): https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026

Is your board meeting time being wasted on reporting instead of judgment and quality discussion?

In episode #384, Ben Murray addresses how AI can transform board meeting prep for SaaS finance leaders. If your board decks are packed with data but the important issues still get buried, and board members show up with wildly different levels of prep, you already know the problem.

Every hour spent reviewing numbers that should have been read beforehand is an hour not spent on the judgment calls that actually move the business forward.

• Understand the core frustration behind Jason Lemkin's "AI board member" post and why it resonates with CFOs building out their FP&A process
• See how AI can do a first analytical pass through your financials, metrics, and benchmarks before the meeting ever starts
• Learn how to use AI to set a focused board agenda: the 3 issues that matter, the 5 metrics that don't need discussion, and the questions still unanswered
• Get Ben's real-world example of using an AI agent to identify top 3 board priorities and pull follow-up ownership from meeting transcripts
• Discover why a deterministic data engine, not just a chatbot, is the real key to accurate AI-written financial narratives and agendas

Tune in to see exactly how Ben is putting Jason Lemkin's AI board member idea into practice, before your next board meeting rolls around.

• Jason Lemkin's SaaStr post on AI and board meetings: https://www.saastr.com/the-ai-board-member-why-yours-should-chair-the-next-meeting-for-real/?
• What I'm using: https://softwaremetrics.ai/
• CFO courses: https://www.thesaasacademy.com/

Can you actually trust the numbers when AI writes your board report?

In episode #383, Ben Murray breaks down how to vibe code finance dashboards that hold up to CFO standards. Every finance leader is being sold the same promise: that AI will do your analysis for you, but the hype skips the part that decides whether the output is usable. If you are putting AI-written numbers in front of your board or investors, the difference between a trusted report and an embarrassing one comes down to work most CFOs never do.

• Why the data foundation, not a magic prompt, decides whether your AI dashboards can be trusted
• How a deterministic metrics engine keeps AI away from your calculations while it writes the narrative on top
• The Cisco playbook for letting AI draft 80 to 90 percent of your board commentary before you finish it off
• How to turn an LLM-generated HTML dashboard into a live, refreshable report in about 5 minutes
• Which model, ChatGPT, Claude, or Gemini, actually produces the best-looking finance dashboards

Tune in to get the exact process CFOs are using to put AI-written reports in front of their boards with the numbers they can defend.

• Webinar: How I Vibe Code Finance Dashboards, plus templates: https://www.thesaasacademy.com/pl/2148817040
• SaaS Metrics Sprint, October cohort: https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026
• Tech CFO community: https://docs.google.com/forms/d/e/1FAIpQLSfP4uwvwEoc92Qc_rS8eu-9EzV6shivPbBhaNcPqsy5sNVNNg/viewform?usp=dialog

Seeing the millions-per-employee AI headlines and wondering where your SaaS company actually stands?

In episode #382, Ben Murray covers the latest ARR per FTE benchmarks from Ray Rike's Benchmarkit data. Social media is full of ARR per employee hype, but almost none of it tells you how the number was defined, whether contractors are counted, or how your company compares once you cut the data the way it actually matters. If you are benchmarking efficiency for a board deck, a raise, or a headcount plan, the aggregate number can quietly send you the wrong signal. This episode grounds the metric in real survey data so you know what good looks like for a company your size, in your region, with your pricing model.

• Know the headline numbers: bottom quartile at 127K, median at 193K, and top quartile at 279K of ARR per employee across the full SaaS population.
• See how pricing model changes everything, from usage-based leading at 291K down to subscription plus usage hybrids at 136K.
• Understand why efficiency can drop in the 50 to 100 million ARR band instead of climbing, and what that says about your next phase of growth.
• Compare the cuts that actually move the number: North America versus EMEA, and horizontal B2B versus vertical SaaS.
• Learn why aggregate benchmarks can be dangerous to your SaaS health, and why size and pricing bands beat the total-population average every time.

Tune in to see where your ARR per employee really stands before you use it in your next board deck or fundraise.

• Benchmarkit (Ray Rike) - benchmarkit.ai
• Ben Murray's blog post with the full data cuts: https://www.thesaascfo.com/arr-per-employee-benchmarks/

In episode #381, Ben Murray covers the latest 2026 tech CFO compensation benchmarks across base, bonus, equity, and severance. If you set finance comp or negotiate your own, guessing at the market rate is expensive in both directions. Underpay and you risk losing your best finance leader. Overpay and you burn cash you cannot spare. This episode gives you the median numbers and the revenue tier splits that decide what competitive actually looks like.

• Why the median CFO package of $285K base, $100K target bonus, and $1M equity is only a starting point, and why company revenue size changes the whole picture
• How median base pay climbs across revenue tiers, from roughly $240K under $10M up to $375K at $100M to $250M, a premium of about 50 percent
• The gap between target and realized bonus, with CFOs hitting about 87 percent attainment while VPs of Finance and FP&A land closer to 50 percent
• Why equity is where packages really split, at a $1M median for CFOs versus $200K at the VP of Finance level, a 5x difference
• Where severance protection shows up, at the CFO and VP of Finance level, and where it disappears at the director level

Tune in, then grab the full report and interactive benchmarks from the show notes before your next comp conversation or board meeting.

• Blog post and report: 2026 CFO compensation summary: https://www.thesaascfo.com/cfo-and-vp-finance-compensation-base-bonus-and-equity-benchmarks/
• Full 2026 CFO compensation report: https://www.benchmarkit.ai/2026-finance-executive-compensation

Is gross revenue retention under attack at your SaaS company? The latest benchmark data says the ground has shifted under everyone.

In episode #380, Ben Murray breaks down the latest SaaS gross revenue retention benchmarks from Ray Rike's Benchmarkit report, the same data set Ben uses to benchmark his own client base. GRR is one of the power three metrics, and it is hard to scale without it. Pricing models are changing; seat-based pricing is under pressure, and AI is reshaping how revenue holds. If your board still treats 88% median GRR as the baseline, you are benchmarking against last year's reality.

• Why median GRR fell from 88% to 84% year over year, and why the top quartile slipped from 95% to 91%
• Whether the 95% GRR "elite" rule of thumb still holds, backed by three years of top-quartile benchmark data
• Which pricing model retains revenue best, comparing pure subscription against usage-based and subscription-plus-usage
• Why vertical SaaS is outperforming horizontal SaaS on retention, with a 90% median GRR versus 84%
• How to benchmark GRR the right way by ACV segment instead of relying on dangerous aggregate numbers

Tune in to see where your gross revenue retention really stands, before your next board meeting or investor update.

• Benchmarkit SaaS Metrics Benchmark Report, Ray Rike: https://www.benchmarkit.ai/2026-saas-ai-native-metrics
• Ben's KPI app: https://softwaremetrics.ai/
• Ben's blog post on 2026 GRR benchmarks: https://www.thesaascfo.com/saas-grr-benchmark-2026/

AI ARR is easy to announce. Proving it is where most SaaS finance teams are about to get exposed.

In episode #379, Ben Murray tackles the new bar for AI financial transparency and what it means for your next budget season. The public markets have already moved the goalposts. Launching AI was the 2024 story. Reporting AI ARR was the 2025 story. Now investors and boards want to see AI margins, customer outcomes, and proof that AI revenue is actually dropping to the bottom line. That same pressure is heading straight for private SaaS, and your board will bring it to budget season whether you are ready or not.

• Understand why AI ARR by itself no longer satisfies boards or investors, and what they now demand to see in the numbers.
• Separate pure AI revenue, AI-influenced revenue, and AI upsell so your reporting survives scrutiny, using clean SKUs, product IDs, and chart of accounts.
• Know which AI costs belong in COGS, including inference, infrastructure, and observability, so you can show your real AI margins.
• Walk into budget season ready for the board questions on AI revenue, AI cost, and margin by revenue stream.
• Instrument heavy, medium, and light AI users so you can defend margins and LTV to CAC as usage scales.

Listen now and build the AI transparency your board will expect before budget season starts.

• Ben's blog posts on capturing AI costs in COGS: inference, infrastructure, and observability: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/
• Ben's training on AI metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas

Is your SaaS company stuck in the valuation doghouse while a handful of names trade at a massive premium?

In episode #378, Ben Murray breaks down Meritech's June 2026 public software comps report and the widening valuation gap across SaaS. The median revenue multiple has fallen 64% from its pre-ZIRP peak, and most public software now trades below 5X. If you are a SaaS founder or CFO, the multiple attached to your business depends on a short list of traits the market now rewards. This episode shows you which ones, and why the rules quietly changed.

• Why only 9 of roughly 100 public software companies trade above a 10X revenue multiple, while 77 sit below 5X
• How the Rule of 40 shifted under the surface, with revenue growth now 3.3x more correlated with the multiple than free cash flow margin
• Why two companies with the same Rule of 40 score can trade at 7.3x versus 3.7x, depending entirely on how they got there
• What the top 9 share in common: free cash flow margins above 20% and ARR growth above 20% at the same time
• How AI exposure now sorts the market, and why a weak AI ARR story lands horizontal SaaS in the doghouse

Tune in to see exactly what separates the premium names from the rest before you benchmark your own SaaS valuation.

• Meritech June 2026 Public Software Comps (Pulse Report): https://meritech.substack.com/p/meritech-software-pulse-12-june-2026
• Ben's academy: https://www.thesaasacademy.com/

Everyone says seat-based pricing is dead, but do you actually have an outcome you can charge for?

In episode #377, Ben Murray breaks down the 12 steps to building an outcome-based pricing plan, drawn from analyzing real, live outcome-based pricing pages and the fine print buried in their terms and conditions. Outcome pricing is complex to design and even harder for customers to understand: when are they charged, and where is the failure point at which they aren't? For SaaS founders and CFOs weighing a move to outcome- or agentic-AI pricing, getting the unit, success criteria, and spend controls right is the difference between a model customers trust and one that creates budget anxiety and billing disputes.

• How to decide whether you even have a billable outcome, and why a completed customer result is not the same as an activity.
• How to define the outcome unit and write success criteria twice, with real examples from Intercom's Fin, Help Scout's AI Answers, and Zendesk's 72-hour resolution window.
• Why failure forgiveness is a conversion tool, not just billing logic, and how measurement windows protect you from outcomes that unravel later.
• How to choose your commercial structure, anchor price to labor savings, revenue, or risk avoidance, and plan for the training lag before charges begin.
• Why spend controls and auditable billing events are non-negotiable, and how to know when outcome pricing is the wrong model entirely.

Tune in for the full framework, then grab the deep-dive blog post before you design your next AI pricing plan.

• Ben's blog post: 12 Steps to Creating an Outcome-Based Pricing Plan: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/

Is your 2027 software budget ready for the AI spend that's about to blow past every forecast you've built?

In episode #376, Ben Murray covers five takeaways for CFOs from the Pricing I/O AI Pricing Report, produced in partnership with Benchmarkit, which surveyed 296 software buyers in Q1 2026. With budget season around the corner and demand for tokens, agentic AI, and tools like ChatGPT and Claude climbing fast, the gap between what buyers want and where AI pricing is heading has never mattered more. If you own a software budget or sell AI software, these findings reshape how you should think about predictability, governance, and the guardrails buyers are actually asking for.

• Why buyers rank predictable total cost as a top-3 priority, far above low entry price, and why the seat-based pricing obituary may be premature for enterprise deals.
• What the 89% budget-overrun rate really signals: a forecasting problem on the buy side, not vendors changing the rules after signing.
• Why credit and token pricing is the single hardest model to evaluate, and what Salesforce's new agentic work units mean for your bill.
• The surprising finding that IT, not Finance, owns AI cost risk, and why department-level allocation of token spend is the fix.
• Why buyers want soft caps, alerts, and approval steps over hard cutoffs, and where hard caps get genuinely painful in outcome-based pricing.

Tune in to get the buyer-side data shaping AI pricing before you lock in your 2027 budget.

• Pricing I/O AI Pricing Report: https://www.benchmarkit.ai/widget/ai-pricing/cy-26?utm_source=TheSaaSCFO&utm_medium=Podcast&utm_campaign=TheSaaSCFO 
• Ben's blog post: 12 Steps to Creating Your Outcome-Based Pricing: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/

Do you actually know which of your AI customers are making you money and which are quietly destroying your gross margin?

In episode #375, Ben Murray breaks down the shape of AI usage and why the distribution curve of your customers determines whether your AI subscription product is profitable. This is why Anthropic and GitHub changed their pricing. Heavy users on a flat subscription can quietly turn a 40% gross margin into a negative one, and most finance teams are not tracking token usage by customer in enough detail to see it coming.

• The three AI usage distribution scenarios every SaaS CFO needs to model: normal, right skew, and left skew, and what each does to your gross margin
• Why a right-skewed distribution means your light users are subsidizing your heavy users, and how to spot when that subsidy stops working
• How a left-skewed distribution can leave 80% of customers unprofitable and drag overall gross margin into the negatives
• Why median, mean, and P90 token usage by customer are now core SaaS finance metrics, not just product analytics
• What finance needs from product and engineering — usage by customer, model mix, input and output token pricing — to run real pricing scenario analysis

Tune in before your next pricing review and find out where your AI margin is actually leaking.

• Ben's AI metrics course with the usage distribution template and free preview: https://www.thesaasacademy.com/ai-finance-metrics-saas
• AI readiness quiz: https://www.thesaasacademy.com/ai-finance-metrics-saas

The pricing model that built the SaaS industry is being replaced in real time. Is your finance team ready for what it does to your core metrics?

In episode #374, Ben Murray breaks down the four SaaS P&L metrics that break when per-seat pricing dies. Public tech leaders are already shifting fast. ServiceNow now drives 50% of net new business from non-seat-based pricing, Workday is reporting hundreds of millions in AI ARR, and GitHub is moving Copilot to usage-based billing. If you are a SaaS CFO or finance leader still modeling on a single blended gross margin, your benchmarks are about to stop working.

• Why the AI product gross margin sits around 52% and how a 30% revenue mix shift can compress your blended margin by 10 to 15 points
• How AI COGS scale directly with product usage, breaking the near-zero incremental cost assumption traditional SaaS finance was built on
• Why one blended LTV no longer works once you have heavy, medium, and light AI usage cohorts, and how to rebuild LTV to CAC by cohort
• How CAC payback period shifts when gross margin is no longer a single number across the customer base
• The new frameworks finance teams need to model hybrid subscription plus usage and outcome-based pricing before the board notices the margin compression

Tune in to get ahead of the pricing shift before your next forecast and board deck go out.

• Ben's blog post on the SaaS pricing revolution: https://www.thesaascfo.com/saas-per-seat-pricing/
• Ben's AI course for SaaS finance leaders: https://www.thesaasacademy.com/ai-finance-metrics-saas

Is per-seat pricing dying a slow death, and is your SaaS expense structure ready for its replacement?

In episode #373, Ben Murray breaks down the shift from per-seat subscriptions to usage and outcome-based pricing, and what it means for your finance org. Bloomberg projects subscription pricing falling from 60% to 30% of SaaS models over the next decade, while outcome-based pricing climbs from 10% to 60%. This is no longer a thesis on a slide. GitHub, Salesforce, Zendesk, Intercom, Figma, HubSpot, and others are already repricing, and public companies are reporting AI ARR in the hundreds of millions. If you cannot answer what your AI margins are when the board asks, you are already behind.

• See exactly how legacy SaaS leaders are repricing, from Zendesk charging per automated resolution to Salesforce billing per AI conversation plus flex credits, and what GitHub's June 1 move to token-based billing signals for the rest of the market.
• Understand why a single bucket of cloud hosting that blends traditional infrastructure with inference spend leaves you blind, and what instrumentation to put in place before budget season.
• Learn the questions your board will ask about AI margins, and how to answer whether low-usage customers are quietly subsidizing your heaviest users.
• Get the case for reconvening your pricing committee now to align product roadmap, AI features, and the expense framework that tracks them.
• Know which AI unit economics to track by revenue stream and by usage bucket so you can defend margin as your pricing model changes in real time.

Listen now and put the tracking framework in place before the AI margin questions land on your desk.

• Ben's blog post: https://www.thesaascfo.com/saas-per-seat-pricing/
• New course on AI unit economics and metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas

Are you a legacy SaaS company quietly hoping for a recovery that isn't coming?

In episode #372, Ben Murray breaks down two slides from Jason Lemkin's State of SaaS keynote at SaaStr Annual that every SaaS operator and CFO needs to confront. The four categories Lemkin laid out will tell you exactly where your company sits in the AI transition, and whether your ARR growth is real or borrowed time. If you're building, leading, or financing a SaaS business right now, this is the reality check that should reshape how you frame your strategy for the next board meeting.

• Understand the four SaaS+AI categories Jason Lemkin used to map every software company, and which one quietly signals the end of the road
• Learn why AI driving expansion revenue versus net new customer acquisition matters more than top-line ARR growth right now
• See which public SaaS companies are pulling off the AI-powered rocket ship growth and what they share
• Hear the "tired versus wired" narratives that separate operators stuck in 2024 talking points from those building what's next
• Get a clear lens for whether your AI features are real revenue drivers or just a story you're telling investors

Tune in to find out where your company actually sits before the next board meeting forces the question.

• SaaStr Annual / Jason Lemkin: https://saastrannual.com/
• Ben's new AI metrics course: https://www.thesaasacademy.com/ai-finance-metrics-saas

If you're shipping AI product lines, are you measuring the two metrics that actually tell you whether your AI is making money — or burning it?

In episode #371, Ben Murray covers two AI unit economics metrics every SaaS CFO and founder should be tracking today: the Inference Expense Ratio and the Work-to-Inference Ratio. Traditional SaaS metrics aren't enough anymore — and a year from now, when your board, investors, and potential acquirers start asking for AI margin and efficiency data, the companies that built the chart-of-accounts structure now will have clean answers. Everyone else will be scrambling.

• The Inference Expense Ratio (AI revenue ÷ inference cost) — and why you can start calculating this from your GL today if your chart of accounts is set up properly
• The healthy benchmarks: 10:1 for AI-infused products, 5:1 for AI-native, and why 3:1 is the warning zone where inference is silently eating your gross margin
• Why this metric only works if your chart of accounts cleanly separates AI revenue from non-AI revenue — and the SKU tagging that makes it possible
• The Work-to-Inference Ratio — how Salesforce's "agentic work units" concept lets you measure whether your AI is getting more efficient over time
• Why every AI product needs its own definition of a "work unit" — record updated, report generated, MCP called — and how the wrong definition will distort your margin trends
• The chart-of-accounts evolution every SaaS company needs right now: from SaaS-only structure to SaaS + AI, with new GL accounts for inference cost in DevOps COGS
• How the Inference Expense Ratio connects to Ben's ROSE metric — measuring revenue produced per dollar of employee, contractor, and agentic AI spend

Tune in to get the AI unit economics framework in place — before your board and investors start asking the questions you can't answer.

• Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas
• ROSE metric: https://www.thesaascfo.com/saas-rose-metric/

Are AI inference costs already eating into your gross margin — and you can't even see them on your P&L?

In episode #370, Ben Murray breaks down exactly what belongs in AI COGS for SaaS companies offering an AI-first or AI-infused product line. Inference bills are stacking up fast, infrastructure-layer spend is the surprise line item nobody priced in, and most finance teams haven't built the GL account structure to capture any of it cleanly. If you don't get the framework in place now, you'll be reporting AI gross margin you can't actually defend by next quarter — and your board will notice.

• The 5 cost categories every AI COGS framework needs — inference, model hosting/GPU infrastructure, the AI infrastructure layer, monitoring and observability, and AI-specific support
• Why AI inference costs deserve their own GL account — and shouldn't be buried inside your cloud hosting bill where they disappear
• The surprise cost line one industry report flagged as the #1 unexpected AI expense — hiding in data platform usage, networking, and egress
• How to structure your COGS cost centers so you can deliver clean margins by AI product line, not just lumped together at the company level
• Why token tracking by customer cohort (heavy / medium / light users) is now table stakes for any AI product sold as a subscription
• The deployed-engineer question: should AI support tickets sit with tech support or a specialized team — and how that decision rewires your margin model

Tune in to get the AI COGS framework in place before your gross margin lands on a board slide you can't defend.

• Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas
• Ben's blog post: What Should Be Included in AI COGS: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/
• SaaS Metrics Foundation course: https://www.thesaasacademy.com/the-saas-metrics-foundation

Did your AI bill just jump overnight — even though no one announced a price increase?

In episode #369, Ben Murray breaks down the hidden AI price hike that's quietly hitting SaaS P&Ls this month. Anthropic shipped a new tokenizer underneath Claude Opus 4.7 — same menu pricing as 4.6, but real enterprise workloads are showing 12-27% higher effective cost, with some prompts consuming up to 35% more tokens for identical output. Most finance teams won't catch this variance until the invoice lands. If you're running AI in production, paying for Claude Code, or modeling AI COGS into next year's plan, this is the cost dynamic you need on your radar before the next board meeting.

• Why "same per-token pricing" doesn't mean same cost — and how a new tokenizer can quietly inflate your token consumption by 35%
• The real-world math: how a $50K/month API spend can balloon to $67K with zero changes to the pricing page
• What Anthropic's doubled Claude Code per-developer estimate ($6 → $13/day) signals about the end of subsidized AI pricing
• Why the era of "AI is just going to keep getting cheaper" assumptions is breaking down — and what that means for forecasting and runway
• The exact metrics to monitor in your Anthropic console today to catch token volume spikes before they hit your GL
• How to use the Inference Efficiency Ratio (revenue ÷ token costs in COGS) to measure AI margin if you're embedding AI into your product
• Why finance teams now need to document internal-use AI models the same way they document internal-use software

Tune in before your next Anthropic invoice lands — and learn what to track now so AI variance doesn't become a board question.

• Dev.to article: https://dev.to/dev_tips/the-ai-price-hike-that-never-showed-up-on-the-pricing-page-your-bill-went-up-27-anyway-3mn5
• Put your AI framework in place: https://www.thesaasacademy.com/ai-finance-metrics-saas

Can you actually prove what your AI product is doing for customers — or are you still pointing at token counts and hoping the board nods along?

In episode #368, Ben Murray breaks down the four layers of AI measurement that every SaaS company needs to communicate internally and externally. Token usage is table stakes. The real question is whether you can move up the stack from consumption to work performed to verified outcomes to quantifiable P&L impact. Get this wrong, and your AI story falls apart in front of investors, customers, and your own finance team. Get it right, and you finally have ROI math a CFO will actually approve.

• Why AI inference belongs in COGS / DevOps — and what that means for the gross margin story behind your AI features and product lines
• How Salesforce's "agentic work units" framing on its latest earnings call signals where AI reporting is heading for the rest of SaaS
• Where true outcome-based pricing actually lives on the pricing page (HubSpot, Zendesk, and others) — and where Agentforce was really still usage-based in disguise
• How Layer 4 business impact replaces fuzzy ROI calculators with objective math
• What to show your board and investors at each layer so your AI value story holds up under scrutiny

Tune in before your next board meeting — your AI story needs more than token counts.

• Ben's blog post on AI measurement and AI work units: https://www.thesaascfo.com/the-four-layers-of-ai-measurement-a-cfos-framework/
• Ben's academy: https://www.thesaasacademy.com/

Salesforce just invented a new metric on their latest earnings call — not because they needed one, but because Wall Street didn't have the vocabulary to value what they built.

In episode #366, Ben Murray breaks down Salesforce's Q4 FY2026 earnings call — not the financials, but the narrative architecture: a new unit of measurement for AI value (the AWU), a framing strategy designed to neutralize the biggest fear enterprise buyers have about AI, and three customer testimonials brought live onto the call. This is the communication playbook every SaaS operator can steal when explaining AI to boards, investors, and customers — at a time when the old metrics (tokens, MAUs, queries) no longer tell the value story.

• Why Salesforce introduced the Agentic Work Unit (AWU) — and what 2.4 billion AWUs against 19 trillion tokens reveals about the limits of token-based AI metrics
• The AWU-to-token ratio as a customer health signal — and why this is the metric your AI-enabled SaaS dashboard is missing
• The "humans and agents working together" framing that lets you sell AI capabilities without triggering the "we're going to lay people off" deal-killer
• How Wyndham's 8,300-hotel deployment, SharkNinja's 250,000 holiday-season engagements, and Lemkin's SaaStr transformation prove ROI when slides can't
• How to expand your SaaS metrics dashboard from 5 pillars to 6 — and the AI-era KPIs (AWUs, AI-attributed ARR, input-to-output ratios, customer outcome metrics) that belong in the new pillar

Tune in before your next board meeting or AI sales pitch — and steal the vocabulary that's about to define the category.

• Salesforce Q4 FY2026 earnings call transcript
• Ben's 5-pillar SaaS metrics dashboard — and the upcoming 6-pillar AI-era expansion: https://www.thesaascfo.com/downloads/five-pillar-metrics-framework/

HubSpot's 50-cent bet may have just forced every SaaS founder to ask whether their current revenue model is still defensible.

In episode #365, Ben Murray breaks down HubSpot's April 2nd announcement — slashing its Breeze customer agent from $1 to 50¢ per resolved conversation, plus a shift on its prospecting agent to $1 per qualified lead — and what this risk transfer means for SaaS revenue, forecasting, and the metrics CFOs need to start tracking. With Salesforce Agent Force hitting $800M in Q4 run rate and over 60% of bookings coming from existing-customer expansion, the question is no longer whether AI is reshaping SaaS pricing, but how fast and how unevenly. Ben pulls in his SEC filings research and a sharp counterpoint from Salesforce's own earnings call to show why the "SaaS is dead" narrative is overplayed.

• The two HubSpot pricing changes that signal a true risk transfer — and the 65% resolution rate (90% for top performers) that makes the bet credible
• Why "75% of AI agent vendors have no systematic approach to pricing" should put your pricing committee on notice this quarter
• The forecasting and metrics shift CFOs need to make as outcome-based pricing erodes predictable usage-based revenue — and the new KPIs that replace the old ones
• How Salesforce Agent Force's $800M Q4 run rate and 60%+ expansion bookings prove the AI revenue thesis — while Robin Washington's earnings call comment complicates the seat-erosion story
• The pricing reality check Ben pulled from analyzing 100+ SEC filings — and what it means for whether your ICP actually fits outcome-based pricing

Listen before your next pricing committee meeting — and bring your CFO. The forecasting implications alone are worth the six minutes.

• Article from: https://thesaaslibrary.com/per-seat-pricing-dead-saas-2026/
• SaaStr post by Jason Lemkin: https://www.saastr.com/salesforce-now-has-3-pricing-models-for-agentforce-and-maybe-right-now-thats-the-way-to-do-it/
• Salesforce Q4 earnings call
• Ben's blog post: https://www.thesaascfo.com/your-ai-feature-is-quietly-destroying-your-gross-margin/

Is your AI SaaS company skating on thin ice because of exploding compute costs you're not tracking?

In episode #365, Ben Murray tackles one of the most pressing financial challenges facing AI-first SaaS companies: the structural margin compression caused by LLM inference costs. Traditional SaaS was built on near-zero marginal cost per customer — that era is over. If you're building on top of AI, every prompt, query, and agentic workflow is a hard COGS line that scales with revenue, and if you're not managing it, it will quietly destroy your unit economics.

• Why AI-first SaaS companies are running 50–60% gross margins (vs. 70–80% for legacy SaaS) — and what Bessemer data shows about AI supernovas with margins as low as 25%.
• How inference and compute costs differ fundamentally from traditional SaaS COGS — and why they won't scale down the way hosting costs did
• Why token costs vary wildly (from $1–2 per million to $30–180+ for frontier models) and how that variability makes feature-level economics a CFO priority
• 5 tactical ways to reduce LLM spend: model routing, prompt caching, context compaction, semantic caching, and batch processing
• How to set up your GL accounts and COGS tracking to allocate inference costs by feature — so you actually understand the economics of what you've built

Tune in before your next board meeting — because if you're not tracking AI inference costs at the feature level, you're flying blind on your most important unit economics.

Resources Mentioned

• The SaaS CFO: https://www.thesaascfo.com/
• Ray Rike — AI to ROI Newsletter: https://ai2roi.substack.com/
• Tomas Tunguz: https://tomtunguz.com/
• Fungies.io — 5 Ways to Save on LLM Costs: https://fungies.io

In episode #364, Ben Murray breaks down how SaaS finance teams should structure their chart of accounts to properly track inference costs, productivity AI, and agentic AI spend. As organizations shift from W-2 headcount to token costs and agentic software, your current expense coding may be out-of-date. If you can't see where the AI spend is going, you can't tie it to ROI — and you definitely can't make the case for going fully agentic.

• Why COGS is the right home for product inference costs (Claude, OpenAI, Gemini) — and why lumping them in with hosting is a mistake
• The three distinct AI spend buckets every SaaS CFO needs to track: direct COGS delivery costs, general productivity tools, and explicit labor substitution (agentic AI)
• Why agentic AI spend deserves its own GL account — and how that ties directly into your ROSE metric
• Where the tracking gets fuzzy: productivity tools vs. true labor displacement, and how to think about cause-and-effect as a CFO
• How AI spend reshapes the ROSE metric as orgs push toward $5M–$10M ARR per FTE targets
  Tune in to get the chart of accounts framework SaaS CFOs need before AI spend becomes too big to ignore — and too messy to measure.

Resources Mentioned

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

Is your SaaS company competing for funding in a market that's already decided AI wins? The Q1 2026 data is in — and the numbers are decisive.

If you're a SaaS founder thinking about your next raise — or a CFO modeling out valuation scenarios — understanding where investors are actually writing checks matters more than ever. In epsiode #363, Ben Murray covers:

• Which software categories dominated Q1 funding — AI infrastructure and vertical SaaS led at $4.6B and $4.5B respectively, and knowing why could sharpen your positioning
• Why enterprise pricing is the investor favorite — 59% of all capital flowed into enterprise-model companies, signaling exactly what target customer story VCs want to hear
• How Seed vs. Series A funding differs by category — Series A flipped toward vertical software and GRC, while Seed stayed heavy on AI infrastructure and DevOps
• What AI native vs. AI embedded actually means for classification — and why the distinction is shaping how investors evaluate your product
• Where to get the full Q1 2026 funding report — with searchable data across 552 rounds and $20B+ in tracked investment

Listen now to get the Q1 2026 funding breakdown — then download the full PDF report to see exactly where smart money is going before your next raise.

• Q1 2026 Funding Report PDF — available via Ben's newsletter:  https://mailchi.mp/thesaascfo.com/investors-sent-a-message-in-1q26-ai-or-bust

Are you feeding raw financial data straight into AI and wondering why the results are inconsistent — or worse, just wrong?

AI is only as good as the data architecture underneath it. For SaaS CFOs and operators running monthly FP&A cycles, that means the order of operations matters enormously. Skip the deterministic compute layer, and your AI narrates garbage. Get the structure right, and suddenly AI can do what no human ever could — synthesize five years of retention schedules and SaaS metrics in seconds.

In episode #362, I'll cover:

• Why separating the 'thinking layer' (math) from the 'talking layer' (AI analysis) is the foundational principle for reliable SaaS financial AI — and what breaks when you skip it
• The pre-compute-everything rule: why you should never ask AI to calculate cohort retention, ARR, or MRR — and what you should ask it to do instead
• Why context beats prompts: how structured data inputs dramatically outperform one-off prompt experiments in repeatable FP&A workflows
• How constraints on what AI can and can't touch produce better output than better prompting — and why your context window size is quietly sabotaging your analysis
• The right mental model for AI in SaaS finance: a super-smart narrator that reads 1,000 computed data points — not an engine that replaces your metrics framework

If you're building or buying any AI layer on top of your SaaS financials, listen to this before you ship anything — these five lessons will save you weeks of bad output.

Resources Mentioned

• SoftwareMetrics.ai — Ben's five-pillar SaaS metrics platform

Everyone's saying AI will kill SaaS — but is the SaaSpocalypse actually real, or just the latest wave of disruption that enterprise software has survived before?

If you're a SaaS founder or operator watching vibe-coded apps spin up overnight, the fear is real. But the narrative is missing something critical: enterprise software isn't just code, and the moats that protect your ARR aren't going away anytime soon. Understanding what actually protects your revenue — and what doesn't — is the difference between panic and a clear-headed strategy. Here's what will you'll learn in episode #361 with Ben Murray.

• Why enterprise software is far more than code — compliance infrastructure, security, governance, SLAs, and integrations take years to harden, and a weekend project won't replace that
• How your proprietary data moat is actually becoming more powerful in the AI era, not less — and why AI agents without that data context are starting from zero
• Why switching costs remain one of the strongest SaaS defensibility factors — and why even AI-native alternatives face massive operational barriers to displacement
• The real operational commitment behind SaaS that vibe-coded tools can't replicate: customer support, product development, distribution, and long-term value delivery
• Why internal vibe-coded tools face their own adoption ceiling — from data security concerns to IT compliance — so enterprise spend isn't fleeing as fast as the hype suggests

Tune in for the full bull case on SaaS survival — and get the frameworks from Ben's SaaSpocalypse blog post linked in the show notes.

• Ben's SaaSpocalypse Blog Post + Defensibility Frameworks: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

Is the “SaaSpocalypse” real—or just another wave of disruption you need to navigate?

If you’re building or scaling a SaaS company, the rapid rise of AI agents, lower barriers to entry, and shifting pricing models could directly impact your growth, revenue predictability, and competitive edge. Understanding these changes isn’t optional—it’s critical to staying relevant and defensible in an AI-driven market. Here's what you'll take away in episode #360 with Ben Murray.

• Understand how AI agents are reshaping the traditional SaaS interface and customer interaction

• Learn why barriers to entry are dropping fast—and what that means for competition

• Discover how evolving pricing models could impact your revenue and forecasting strategy

Tune in to uncover whether SaaS is truly at risk—and what you should do right now to stay ahead.

Resources:

• AI defensibility framework: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

Are finance teams implementing AI the wrong way?

In episode #359, Ben Murray argues that many CFOs and finance leaders are approaching AI backward—focusing too much on prompts and quick wins rather than building the foundational data infrastructure required for meaningful, repeatable insights.

Drawing from recent AI webinars and his experience building softwaremetrics.ai, Ben explains why SaaS metrics, retention, and cohort analysis should not rely on AI. Instead, these should be computed through structured, deterministic systems first—then enhanced with AI for deeper analysis and pattern recognition.

Resources Mentioned

• My new metrics engine: https://softwaremetrics.ai/
• My SaaSpocalypse post: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

What You’ll Learn

• Why prompt-driven AI workflows are not scalable in finance
• The difference between deterministic systems and AI-driven analysis
• Why you don’t need AI to calculate core SaaS metrics like retention or CAC payback
• The importance of structured data and clean data pipelines
• How AI should be layered on top of computed financial data—not raw inputs
• Why context windows and token usage matter when working with large datasets
• How AI can uncover insights (like expansion opportunities) that FP&A teams may miss

Why It Matters

• Prompt-based workflows create inconsistency and lack of auditability
• Without structured data, AI outputs are unreliable and not repeatable
• Finance teams risk “prompt fatigue” without building scalable systems
• Deterministic calculations ensure accuracy for critical SaaS metrics and reporting
• AI delivers the most value when used for analysis—not basic computation
• Efficient data handling reduces token costs and improves performance

What sparked the recent “SaaSpocalypse” conversation across social media, news outlets, and investor circles?

In episode #358 of SaaS Metrics School, Ben Murray explains how the debate around AI potentially disrupting SaaS began. Ben breaks down what actually started the conversation, the major concerns investors and operators are discussing, and why SaaS founders and CFOs should pay attention to the shift.

• Ben’s blog post: The SaaSpocalypse — Bull Case, Bear Case, and How to Assess SaaS Defensibility: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/

• What triggered the “SaaSpocalypse” narrative in early 2026

• Why AI coding tools are accelerating the build vs. buy decision for software

• How agentic workflows could pressure traditional SaaS products

• Why seat-based pricing models may face scrutiny in an AI-driven world

• How investors may rethink the durability of SaaS revenue and growth

• AI agents capable of executing workflows could reshape how software is delivered

• SaaS pricing models tied to seats may become less durable if AI reduces headcount needs

• The build vs. buy equation is shifting as AI coding tools make software easier to create

• Investors may begin reassessing SaaS valuations based on AI disruption risk

• SaaS operators must stay informed and proactive as AI reshapes the software landscape

Is AI killing SaaS? Ben argues the opposite.

In episode #357 of SaaS Metrics School, Ben Murray explains why AI isn’t replacing SaaS companies — it’s amplifying subject matter expertise. Drawing on his experience building SoftwareMetrics.ai with AI coding tools, he walks through how he would not be able to create a useful expert without domain knowledge. It doens't just apply to Ben.

Resources Mentioned

• Ben's new app at: https://softwaremetrics.ai/

What You’ll Learn

• Why AI is not replacing SaaS business models
• How subject matter expertise becomes more valuable in an AI-native world
• The importance of structured MRR schedules and clean invoice data
• How metadata (ACV, geography, vertical, company size) unlocks deeper retention insights
• The difference between dashboards and AI-powered revenue intelligence
• How AI can identify dormant expansion opportunities within your existing customer base

Why It Matters

• AI tools amplify expertise — they don’t replace it
• Clean financial and customer data becomes a strategic asset
• Revenue intelligence goes far beyond basic retention reporting
• SaaS operators who understand their metrics can leverage AI more effectively
• Industry-specific knowledge remains a competitive moat in a world of AI tooling

In episode #356, Ben shares the results from the FP&A category of his 7th Annual SaaS Tech Stack Survey, highlighting the top financial planning and analysis solutions used in software companies today.

With 37 FP&A solutions named in the survey, this remains one of the most competitive and fast-moving segments in the back-office tech stack. While spreadsheets still dominate usage—by a wide margin—dedicated FP&A platforms are gaining traction, especially as companies scale past $10M+ ARR and investor reporting requirements increase.

Ben also compares this year’s results to prior years and explains how FP&A tool adoption shifts by ARR size.

Resources Mentioned

• 7th Annual SaaS Tech Stack Survey: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/

What You’ll Learn

• The most widely used FP&A solutions in SaaS and AI companies
• Why spreadsheets still dominate financial modeling workflows
• Which platforms are gaining momentum (Drivetrain, Mosaic, Aleph, Pigment, Planful, and others)
• How FP&A adoption changes as companies scale beyond $10M ARR
• Why enterprise-grade tools like Workday appear in larger organizations
• How funding and competition are reshaping the FP&A software landscape

Why It Matters

• FP&A systems power your forecasting, budgeting, and board reporting
• Spreadsheet-based processes eventually break as complexity increases
• As ARR grows, investors expect more sophisticated financial modeling and analytics
• Selecting the right FP&A tool impacts forecasting accuracy, KPI visibility, and strategic planning
• Understanding market adoption trends helps founders and CFOs benchmark their financial systems

In episode #355, Ben breaks down the top invoicing solutions used by SaaS and AI companies based on his 7th Annual Tech Stack Survey.

With 57 different invoicing solutions named in the survey, this category shows far more fragmentation than core accounting. The top five solutions account for 55% of reported usage, but there’s still a long tail of specialized billing and revenue management platforms.

Ben walks through the most widely used tools and explains how invoicing increasingly overlaps with revenue management, subscription billing, and payment processing.

Resources Mentioned

• 7th Annual SaaS Tech Stack Survey: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/
• Metronome, sponsor of the invoicing category: https://metronome.com/

What You’ll Learn

• The top invoicing and billing solutions used in software companies
• Why QuickBooks and Stripe remain dominant in early and growth-stage SaaS
• Which newer platforms are gaining traction
• How fragmented the invoicing and billing landscape has become

Why It Matters

• Invoicing is a critical link between bookings, cash flow, revenue recognition, and ARR reporting
• Poor billing infrastructure can break your MRR schedules and retention calculations
• As pricing models evolve (subscription, usage, hybrid), your invoicing system must handle complexity
• Revenue management tools increasingly sit between CRM, payments, and your general ledger
• Clean invoicing data is essential for accurate financial modeling, KPI tracking, and due diligence

In episode #354, Ben shares the results from his 7th Annual SaaS Tech Stack Survey and reveals the top accounting solutions used by software, SaaS, and AI companies today.

With participation across 22 software categories, this year’s survey highlights both the consistent market leaders and the rise of newer, AI-first ERP platforms. While legacy players continue to dominate, new entrants are gaining meaningful traction.

Ben breaks down the “Power Six” accounting platforms and what their market concentration tells us about the current state of financial systems in tech companies.

Resources Mentioned

• 7th Annual SaaS Tech Stack Survey: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/
• Light, sponsor of the core accounting category: https://light.inc/

What You’ll Learn

• The top accounting and ERP systems used by SaaS and AI companies
• How the “Power Six” now dominate the accounting stack landscape
• Which newer AI-first ERP platforms are gaining traction
• How concentrated is the accounting software market among SaaS companies
• Why accounting system selection matters as companies scale ARR

Why It Matters

• Your accounting system is the foundation of your financial reporting, SaaS metrics, and KPI tracking
• Poor financial systems limit your ability to calculate ARR, revenue retention, and other recurring revenue metrics
• As revenue grows, moving from SMB accounting tools to more robust ERP and financial systems becomes critical
• Investors and auditors expect scalable accounting infrastructure as companies mature
• Understanding market trends helps founders and CFOs evaluate whether their current financial systems can support growth

Calculating SaaS metrics sounds straightforward—until you actually try to do it. In episode #353, Ben Murray breaks down why SaaS metrics are so difficult to calculate at scale, why spreadsheets eventually break, and what it really takes to produce CFO-grade metrics that stand up in the Boardroom and in due diligence.

Drawing on insights from the 7th Annual SaaS Tech Stack Survey, Ben explains why 58% of companies still rely on spreadsheets and highlights the growing mix of tools aimed at solving the SaaS metrics challenge.

At the core of the issue? SaaS metrics require clean, structured data from four distinct systems—and most companies don’t have that foundation in place.

Resources Mentioned

• 7th Annual SaaS Tech Stack Survey: https://mailchi.mp/thesaascfo.com/its-here-the-2026-saas-finance-ops-tech-stack-report
• Waitlist for Ben's SaaS Metrics app: https://docs.google.com/forms/d/e/1FAIpQLSeMMKm1N6g0PifGBNhFacivqA-lqePH9id93dCGKxNeBOWbFw/viewform?usp=dialog
• SaaS Metrics Foundation Course with App: https://www.thesaasacademy.com/the-saas-metrics-foundation

What You’ll Learn

• The four key SaaS finance data sources required to calculate accurate metrics
• Why SaaS metrics are difficult to automate (and why most companies struggle)
• Why spreadsheets are the default starting point—and why they don’t scale
• The most common tools companies use today to calculate SaaS metrics
• Why understanding the manual process is critical before implementing software
• What “CFO-grade SaaS metrics” actually means

Why It Matters

• Without structured financial data, your metrics won’t stand up to board or investor scrutiny
• Disconnected systems create inconsistencies that undermine trust in your numbers
• Spreadsheet-based processes break as transaction volume and complexity grow
• Accurate SaaS metrics require integrating financial, bookings, HR, and customer revenue data
• If your data foundation isn’t solid, automation tools won’t fix the problem

In episode #352 of SaaS Metrics School, Ben explains why SaaS and AI founders need to get control of their Stripe data early — before transaction volume and product complexity make it unmanageable. Drawing on years of fractional CFO experience, he explains how messy Stripe data can undermine revenue accuracy, MRR schedules, retention metrics, and due diligence readiness if the data flow isn’t clearly mapped from day one.

Resources Mentioned

• Ben’s 7th Annual Tech Stack Report: https://www.thesaascfo.com/surveys/finance-accounting-tech-stack-survey/

What You’ll Learn

• Why Stripe data becomes difficult to manage as transaction volume grows
• How Stripe feeds into revenue reporting, MRR schedules, and retention metrics
• What a “revenue by customer by month” (customer cube) actually requires
• How multiple product IDs and revenue types complicate Stripe reporting
• Why mapping payment, fee, and revenue flows early saves major cleanup later
• The role Stripe data plays in due diligence and investor scrutiny

Why It Matters

• Stripe is often the source of truth for self-serve and PLG revenue
• Poorly mapped Stripe data makes MRR waterfalls and retention metrics unreliable
• Due diligence requires defensible revenue-by-customer schedules
• Fixing Stripe data problems later is far more expensive and time-consuming
• Clean Stripe flows enable accurate forecasting and financial clarity as you scale

In episode #351 of SaaS Metrics School, Ben breaks down one of the most misunderstood areas of SaaS finance: the difference between bookings, invoices, and revenue. Using the SaaS revenue cycle as a framework, he explains how a signed contract flows through invoicing, revenue recognition, and ultimately cash collection — and why confusing these concepts leads to bad metrics, poor forecasting, and cash flow surprises.

Resources Mentioned

• Blog post: https://www.thesaascfo.com/bookings-vs-invoicing-vs-revenue/
• SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation

What You’ll Learn

• What a booking actually represents in a SaaS or PLG business
• How bookings differ between sales-led and self-service models
• Why invoices are not the same as revenue under accrual accounting
• How deferred revenue works and why revenue must be recognized over time
• The full SaaS revenue cycle: bookings → invoices → revenue → cash
• Why understanding this flow is critical for financial modeling, forecasting, and cash flow planning

Why It Matters

• Prevents overstating revenue or ARR in Board and investor reporting
• Improves accuracy in cash flow forecasting and runway planning
• Ensures go-to-market metrics like CAC payback and cost of ARR are built on the right data
• Reduces confusion between CRM data and accounting system source-of-truth
• Creates better alignment between finance, sales, and leadership teams

Justifying investment in customer success is far harder than justifying spend in sales and marketing. In episode #350, Ben walks through a practical framework for evaluating the ROI of customer success and retention programs by tying customer success investment directly to ARR, MRR, and revenue retention performance. Instead of relying on vague qualitative benefits, this episode outlines how finance and SaaS leaders can quantify retention improvements and translate them into real financial impact.

Resources Mentioned

Blog post on quantifying customer success and retention ROI: https://www.thesaascfo.com/quantifying-investments-in-customer-success-and-retention/

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

What You’ll Learn

• Where customer success should be classified on the SaaS P&L (COGS vs. Sales)
• Why customer success ROI is harder to quantify than CAC or go-to-market efficiency
• How to use MRR and ARR waterfalls as the foundation for retention analysis
• The difference between gross revenue retention and net revenue retention in ROI modeling
• How expansion, contraction, and churn act as independent levers in retention
• A scenario-based approach to estimating ARR impact from retention improvements

Why It Matters

• Helps justify customer success spend with real revenue and ARR impact
• Improves financial modeling and long-term financial strategy decisions
• Connects retention performance to unit economics and scalability
• Avoids over-investing in customer success without measurable outcomes
• Provides a clearer framework for board and investor discussions

Many SaaS teams try to use their CRM to report ARR and MRR, but this creates serious risks—especially in forecasting, retention analysis, and due diligence. In episode #349, Ben explains why your CRM is rarely the correct source of truth for recurring revenue and where ARR should actually come from to ensure financial accuracy and credibility with investors and acquirers.

Resources Mentioned

• How to Disclose ARR: https://www.thesaascfo.com/cfos-guide-to-disclosing-headline-arr-numbers/
• Ben's SaaS Metrics Course: https://www.thesaasacademy.com/the-saas-metrics-foundation

What You’ll Learn

• Why CRM-based ARR reporting is often inaccurate and easy to break
• The difference between bookings data and revenue-based ARR
• What qualifies as a true source of truth for ARR and MRR
• How invoicing, revenue recognition, and the general ledger fit together
• Why CRM-reported ARR frequently fails under due diligence scrutiny
• When (and only when) a CRM can be trusted for recurring revenue metrics

Why It Matters

• Prevents misleading ARR, MRR, and revenue metrics
• Ensures your financial systems can support investor and buyer diligence
• Reduces risk when calculating retention, CAC payback, and unit economics
• Improves confidence in Board reporting and long-term financial strategy

In episode #348 of SaaS Metrics School, Ben Murray responds to a thoughtful LinkedIn comment that challenged a common assumption: that a well-structured SaaS P&L tells the whole story. While a properly built chart of accounts and SaaS P&L are foundational, Ben explains where hidden risks can still exist beneath clean financial statements.

Using real-world examples from SaaS founders and finance teams, this episode explores how revenue commingling, misclassified expenses, role overlap, and customer concentration can quietly distort decision-making—despite an “immaculate” P&L.

Resources Mentioned

• LinkedIn SaaS P&L Post: https://www.linkedin.com/posts/benrmurray_saas-activity-7418308514533552128-l2eG/
• SaaS P&L Blog Post:
• SaaS Metrics Course: 

What You’ll Learn

• Why a clean SaaS P&L can still hide structural business risk
• How revenue commingling and miscoding undermine financial clarity
• When and how to reclass employee costs across departments
• Why materiality matters more than perfection in early-stage accounting
• How customer concentration risk often surfaces late in due diligence

Why It Matters

• A SaaS P&L is only as useful as the assumptions behind it
• Poor expense classification can distort margins and unit economics
• Misunderstanding departmental cost ownership leads to flawed decisions
• Customer concentration can materially impact valuation and investor confidence
• Strong financial systems require both structure and experienced oversight

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