SaaS Metrics School

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 #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

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

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

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 #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  

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-pageVerint (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