SaaS Metrics School

Ben Murray

Ben Murray brings you actionable SaaS metrics lessons that he has learned through years of being in the SaaS CFO trenches. Whether you are new to SaaS or a SaaS veteran, learn the latest SaaS and AI metrics, finance, and accounting tactics that drive financial transparency and improved decision-making. Ben’s SaaS metrics blog consistently rates a 70+ NPS, and his templates have been downloaded over 100,000 times. There is always something to learn about SaaS and AI metrics.

  1. −3 H

    Why Feeding Raw Data to AI Is Killing Your FP&A Accuracy

    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 #360, 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

    6 min
  2. 22 MARS

    The SaaSpocalypse Is Overblown: 4 Reasons Your SaaS Company Isn't Dead Yet

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

    6 min
  3. 18 MARS

    CFOs We are Implementing AI Backwards

    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

    5 min
  4. 25 FEB.

    Top FP&A Solutions Used by Software Companies

    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

    4 min
  5. 20 FEB.

    Top Invoicing Solutions Used by Software Companies

    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

    3 min

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Om

Ben Murray brings you actionable SaaS metrics lessons that he has learned through years of being in the SaaS CFO trenches. Whether you are new to SaaS or a SaaS veteran, learn the latest SaaS and AI metrics, finance, and accounting tactics that drive financial transparency and improved decision-making. Ben’s SaaS metrics blog consistently rates a 70+ NPS, and his templates have been downloaded over 100,000 times. There is always something to learn about SaaS and AI metrics.

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