Stop Doubting Your SaaS Numbers in Front of the Board — or a Buyer
Most finance leaders don’t have a formula problem — they have a data problem. When ARR doesn’t tie out and your margins move every month, you hesitate in the rooms where confidence matters most: board meetings, investor conversations, diligence calls, and exit discussions. In a 30-day core build — plus a review week and an AI metrics capstone — you’ll build the clean data foundation and board-ready, exit-ready metrics operating system that makes every number accurate, defensible, and repeatable. Using your own data, the SaaS Metrics Accelerator, SoftwareMetrics.ai, and the SoftwareMetrics.ai API/MCP connector for Claude and ChatGPT.
Applying takes about five minutes and doesn’t commit you to anything. Every application gets a review call.
Company Pass includes up to 2 participants, annual access to SoftwareMetrics.ai, and lifetime access to SaaS Metrics Foundation.
What changes
Most SaaS teams do not have a metrics formula problem. They have a data foundation problem.
The formulas are not the hard part. The hard part is getting the right data out of your GL, CRM, payroll system, invoice data, and subscription systems in a clean, repeatable format.
The usual pain points
The dashboard is not the hard part. The inputs are.
When I onboard new software clients, the hardest part is almost always the same: data gathering and normalization.
Once the four key SaaS finance data sources are structured correctly, the metrics become far easier to calculate, explain, benchmark, forecast, report, and defend during diligence. That is why this sprint focuses on the implementation work most courses skip.
Simple does not mean easy.
“Get the data right” sounds simple. But in practice, this is where SaaS teams get stuck. This sprint gives you the structure, templates, live walkthroughs, and implementation window to turn messy source data into a repeatable SaaS metrics process.
The four key SaaS finance data sources
Each live session in the first two weeks focuses on one source. Your job is to gather, clean, map, and normalize the inputs that power accurate SaaS metrics.
Financial Data
GL, chart of accounts, SaaS P&L mapping, revenue streams, department coding, COGS vs. OpEx, and the new GL structure needed for AI metrics.
Bookings Data
CRM or bookings report structure, new ARR, expansion ARR, services, downgrades, booking dates, revenue types, and GTM attribution.
People Data
Payroll, HRIS, contractors, wages, FTEs, department mapping, fully burdened cost structure, Rev/FTE, ROSE, and operating leverage analysis.
Customer / Revenue Data
Invoice data, subscription data, MRR schedules, MRR waterfall, customer counts, churn, expansion, contraction, retention, and ARR movement.
Gather the data. Normalize the inputs. Upload to SoftwareMetrics.ai. Generate the dashboard. Connect Claude and ChatGPT.
This sprint is built around a practical workflow: clean the four key data sources, use the SaaS Metrics Accelerator to understand and validate the metrics logic, then upload the normalized data into SoftwareMetrics.ai to generate the board- and exit-ready dashboard. With the SoftwareMetrics.ai API/MCP connector, you can connect Claude and ChatGPT to the same trusted metrics data layer.
1. Source Data
GL, CRM/bookings, people data, and customer/revenue data.
2. Accelerator Model
Use the model to structure, validate, and understand the calculations.
3. SoftwareMetrics.ai
Upload the data, generate the dashboard, and connect Claude or ChatGPT through the API/MCP connector.
The SaaS Metrics Accelerator helps you structure the inputs and validate the metrics.
The Accelerator is where the four data sources come together. It helps you understand the required inputs, trace the metrics logic, validate the calculations, and prepare your data for SoftwareMetrics.ai.
This is the implementation step that moves you from raw exports and disconnected spreadsheets to a repeatable monthly metrics process.
Metrics structured and validated
- Bookings and ARR movement
- ARPA and customer trends
- Headcount and revenue per FTE
- Gross margins and OpEx profile
- Rule of 40 and financial profile
- CAC, CAC payback, Cost of ARR, and Magic Number
- Retention, LTV, ROSE, and board- and exit-ready KPI outputs
Annual access to SoftwareMetrics.ai is included.
SoftwareMetrics.ai includes the pre-built board- and exit-ready dashboard. Once your four key SaaS finance data sources are compiled, cleaned, and normalized, you upload the data and the dashboard is generated for you.
SoftwareMetrics.ai now also includes API access and an MCP connector so you can connect Claude and ChatGPT to your trusted metrics data layer.
Your ongoing metrics workspace
- Upload and organize metrics inputs
- Generate a board- and exit-ready SaaS KPI dashboard
- Connect Claude and ChatGPT to your metrics data via API/MCP
- Track and benchmark SaaS performance
- Maintain the reporting process after the sprint
- Support a more repeatable monthly finance rhythm
Then we add the AI finance layer.
Once the SaaS data foundation is in place, we extend the system for AI-native reporting.
AI makes messy finance data more dangerous. If your GL, usage costs, customer data, and metrics definitions are ambiguous, LLM workflows will not fix the problem. They will amplify it. The goal is a deterministic data layer that Claude and ChatGPT can query through SoftwareMetrics.ai instead of guessing from messy spreadsheets.
AI metrics layer
- New GL structure for AI metrics reporting
- AI COGS and AI gross margin
- Inference and usage cost structure
- AI pricing and unit economics considerations
- SoftwareMetrics.ai API and MCP connector for Claude and ChatGPT
- Deterministic data layer usable by LLM workflows
- Board- and exit-ready AI finance reporting structure
What you should leave with
This is not a passive course. The goal is to leave with the structure and operating system needed to calculate, explain, and maintain your SaaS and AI metrics.
Data foundation
- Financial data structure
- Bookings data structure
- People data structure
- Customer/revenue data structure
- Data normalization checklist
- Deterministic data layer for AI workflows
- SoftwareMetrics.ai API/MCP connector setup for Claude and ChatGPT
Metrics outputs
- ARR movement framework
- Retention reporting framework
- Diligence-ready ARR, retention, margin, and GTM efficiency support
- GTM efficiency reporting
- Board-ready SaaS KPI dashboard in SoftwareMetrics.ai
- AI metrics GL structure
- SoftwareMetrics.ai workspace
- Claude and ChatGPT-ready metrics layer via API/MCP
Two sessions per week to build the foundation, then a data build window, review week, and AI capstone.
The first two weeks focus on one data source per session. Then you’ll have two weeks to work on your own data before we return for review, Q&A, and the AI metrics layer.
Session 1: Financial Data
GL, chart of accounts, SaaS P&L mapping, revenue streams, department coding, COGS vs. OpEx, and the first view of the AI GL structure.
Session 2: Bookings Data
CRM/opportunity setup, new vs. expansion ARR, services, downgrades, bookings report structure, and GTM efficiency inputs.
Session 3: People Data
Payroll, HRIS, contractors, wages, FTEs, department mapping, wage allocation, Rev/FTE, ROSE, and operating leverage inputs.
Session 4: Customer / Revenue Data
Invoice data, subscription data, MRR schedules, MRR waterfall, customer counts, ARR movement, churn, expansion, contraction, and retention reporting.
Two-Week Data Build Window
This is where the sprint becomes real. You’ll gather, clean, map, and normalize your four key data sources, identify gaps, and prepare your Accelerator and SoftwareMetrics.ai inputs.
Review, Q&A, and Accelerator Troubleshooting
We’ll review common data issues, troubleshoot source files, pressure-test the Accelerator model, and refine the board- and exit-ready SaaS KPI dashboard process.
AI Metrics, AI GL Structure, and LLM-Ready Data
We’ll add the AI finance layer: AI COGS, AI gross margin, inference and usage costs, new GL structure, and the deterministic data layer needed for Claude and ChatGPT workflows through the SoftwareMetrics.ai API/MCP connector.
All live sessions are recorded and available to your team for the duration of the sprint. [CONFIRM — delete this line if sessions are not recorded.]
Built for SaaS teams who own the metrics process.
Founders & CEOs
You need metrics you can trust before board meetings, year-end planning, fundraising, diligence, exit conversations, or strategic decisions.
CFOs & Finance Leaders
You need a cleaner data foundation, repeatable monthly metrics process, and board-ready reporting package.
Controllers, FP&A & RevOps
You need better alignment between CRM, billing, finance, payroll, customer success, and reporting systems.
This is not for someone who only wants to watch videos.
This sprint is best for companies ready to work with their own data.
Good fit
- Your team is responsible for SaaS metrics, FP&A, board reporting, or finance operations.
- You have real source data to work with.
- You want a repeatable monthly process, not a one-time dashboard.
- You are preparing for planning, board reporting, fundraising, diligence, an exit, or AI metrics conversations.
Not a good fit
- You are pre-revenue and do not have meaningful SaaS data yet.
- You only want a basic intro to SaaS metrics.
- You are looking for done-for-you dashboard consulting.
- You are not able to gather source data during the sprint.
Led by Ben Murray, The SaaS CFO
Ben Murray is the founder of The SaaS CFO and The SaaS Academy. He has taught SaaS finance and metrics to thousands of SaaS founders, CFOs, finance leaders, and operators.
Ben’s work focuses on helping SaaS companies understand, calculate, benchmark, and communicate the metrics that drive growth, efficiency, retention, valuation, cash flow, and now AI economics.
Why this sprint is different
The sprint is based on the same practical process used to onboard SaaS finance clients: identify the required data sources, normalize the inputs, load the model, validate the outputs, and create a repeatable process.
The goal is not more theory. The goal is a data foundation and metrics operating system your team can continue using.
“Confidence in your metrics makes a real difference in how investors view you.”
“Going through the due diligence of a Series A round is not for the faint of heart. When you’ve got confidence in your metrics and how you calculate them, that makes a real difference in how investors view you.”
Rob went through Ben’s SaaS metrics program four years ago, knowing very little about SaaS metrics at the time. He wrote unprompted, four years later, to say that the way the nuances of those metrics were covered — making sure his team measured exactly the right things — had been a significant contribution to their journey.
“I took over as CFO at a time when we needed some rigorous answers quickly about our finances. I knew the business really well. But Ben taught me exactly how to make our finances into a repeatable process that made us look great in front of investors.”
“I have since applied the learnings in 3 different software businesses, all with great success. I started teaching the content to my teams, which greatly helps to professionalize organizations and makes people more data driven.”
“As the CFO of a mid sized SaaS company I understand the importance and complexity in calculating the metrics of our business. Ben’s community is a constant source of answers to tricky questions going forward.”
Reviews from alumni of Ben’s SaaS metrics programs. The September Implementation Sprint is a new format built on the same process.
Diligence teams don’t test whether you know your ARR. They test whether you can defend it — the definition, the source, the nuance — while someone recalculates in front of you. That confidence isn’t memorized. It’s built.
Applying isn’t enrolling.
Every cohort is capped, and every seat is confirmed after a conversation — not before. The application exists so the sprint fits your stage, your data, and your goals.
Apply
About five minutes. Tell me your stage, your systems, and what you’re preparing for. No payment, no commitment.
Review call with Ben
We talk through your four data sources and whether the sprint is the right move right now. If it isn’t, I’ll tell you.
Seat confirmed
If it’s a fit, you choose your pass and confirm your seat. Payment plans available on every tier.
Choose your level of implementation support
A fractional CFO engagement to build this runs $5,000–$15,000+ per month. The Sprint productizes that same onboarding process — once, with the software included and your team trained to maintain it.
Application-first enrollment makes sure the program fits your stage, data availability, and goals. The September cohort is capped at [X] companies, and applications close September 2.
Company Pass
Where most teams start.
- 2 live participant seats
- All live implementation sessions
- Two-week data build window
- Review, Q&A & troubleshooting week
- SaaS Metrics Accelerator model
- Lifetime access to SaaS Metrics Foundation
- Pre-built board-ready dashboard in SoftwareMetrics.ai
- Annual SoftwareMetrics.ai access
- SoftwareMetrics.ai API/MCP connector access for Claude and ChatGPT
- AI metrics capstone — AI COGS, AI gross margin, AI GL structure
No payment today. Seat confirmed after your review call.
Implementation Pass
Done with you — your data, reviewed by Ben.
Everything in Company Pass, plus:
- A private 1:1 data review session with Ben on your four key data sources
- Dashboard sign-off — Ben reviews your board-ready output before your next meeting
- Priority Q&A throughout the build window
No payment today. Seat confirmed after your review call.
Executive Pass
Limited to 3 companies per cohort.
Everything in Implementation Pass, plus:
- Multiple private working sessions — Ben helps normalize your GL, bookings, people, and customer data directly
- Direct access to Ben throughout the sprint (priority email/Slack)
- Personal review of your AI COGS structure and AI economics dashboard
- Concierge onboarding for both your data sources and software setup
3 seats. Confirmed after your review call.
Questions before you apply?
What happens after I apply?
Applying is not enrolling. There is no payment and no commitment at the application stage. I read every application, and if it looks like a fit, we get on a call to talk through your data sources, your stage, and what you’re preparing for. If the sprint isn’t right for you, I’ll say so. Seats are confirmed after that conversation.
Do I need perfect data before joining?
No — and if your data were already clean, you wouldn’t need the sprint. It is designed to help you identify what you have, what is missing, and what needs to be cleaned. What you do need is access to real financial, bookings, people, and customer/revenue data, and the ability to work on it during the build window.
What if I can’t make a live session?
Sessions run Tuesdays and Thursdays and are recorded, so you and your second participant can catch up. That said, the live sessions are where the implementation questions get answered against your real data, so plan to attend what you can. [CONFIRM recording policy before publishing.]
Is this a course or consulting?
It sits between the two. You get structured teaching, templates, software access, and live implementation guidance. You do the data work, the software generates the dashboard, and the live sessions bring the judgment — but this is not a done-for-you consulting engagement.
Why focus so much on data?
Because data gathering and normalization are usually the hardest parts of building reliable SaaS metrics. Once the four key data sources are clean, the dashboard and metrics calculations become much more repeatable.
Who from my company should attend?
The Company Pass includes up to 2 participants because the implementation usually requires both finance ownership and source-data knowledge. Good combinations include CFO and controller, founder and finance lead, FP&A and controller, or finance and RevOps.
One person rarely owns all four data sources, so bringing the right second person can make the implementation much smoother.
What happens during the two-week data build window?
You will gather, clean, map, and normalize your own source data. When we return, we will review common issues, troubleshoot the Accelerator inputs, and refine the dashboard outputs.
Are payment plans available?
Yes. Every tier can be paid in three monthly installments. If budget timing is the only thing standing in the way, raise it on the review call.
How does SoftwareMetrics.ai fit in?
SoftwareMetrics.ai includes the pre-built board- and exit-ready dashboard. Once your four key SaaS finance data sources are compiled, cleaned, and normalized, you upload the data and the dashboard is generated for you. Annual access is included. SoftwareMetrics.ai also includes API access and an MCP connector so you can connect Claude and ChatGPT to your trusted metrics data layer.
How does the AI metrics course fit in?
The AI material is the capstone. After the SaaS data foundation is in place, we add the AI finance layer: AI COGS, AI gross margin, usage costs, new GL structure, and deterministic data design for Claude, ChatGPT, and other LLM workflows connected through SoftwareMetrics.ai.
How does this help with exit readiness?
Buyers, investors, and diligence teams will not just ask for your dashboard. They will ask for the source data behind ARR, retention, gross margin, CAC, payback, bookings, and customer movement.
The sprint helps you organize the four key SaaS finance data sources, normalize the inputs, and create a metrics layer that is easier to explain, defend, and support during fundraising or an exit process.
What’s the difference between the three passes?
All three include the live sessions, lifetime access to SaaS Metrics Foundation, the Accelerator model, the dashboard, annual SoftwareMetrics.ai access, and SoftwareMetrics.ai API/MCP connector access. The Implementation Pass adds a private 1:1 review of your data and a dashboard sign-off with Ben. The Executive Pass adds multiple private working sessions, a third seat, and direct access throughout the sprint — for teams that want the most hands-on implementation support.
Will you build the dashboard for me?
SoftwareMetrics.ai includes the pre-built board- and exit-ready dashboard. Your implementation work is compiling, cleaning, and normalizing the four key SaaS finance data sources. Once those inputs are ready, you upload the data into SoftwareMetrics.ai and the dashboard is generated for you.
The sprint is designed to help you get the data right so the dashboard is accurate, repeatable, and trustworthy. From there, the SoftwareMetrics.ai API/MCP connector gives Claude and ChatGPT access to a governed metrics layer instead of disconnected spreadsheets.
Walk into your next board meeting — or exit process — sure of every number.
Join the September SaaS Metrics Implementation Sprint and build a board-ready and exit-ready metrics operating system your finance team, leadership team, board, buyers, investors, Claude, and ChatGPT workflows can trust.
Apply for the September SprintApplications close September 2 · Cohort begins September 8 · No payment at the application stage
