SaaS Metrics Implementation Sprint | Go Live With Board-Ready Metrics
Applications close September 15 · Cohort begins October 6 · Capped at 25 companies
October 2026 Guided Implementation

Walk Into Your Next Board Meeting — or Diligence Call — Ready to Defend Every Number

Bring your real GL, bookings, payroll, and customer data. Over seven weeks, you will turn those source files into a reconciled SaaS metrics system, a live SoftwareMetrics.ai dashboard, a board-ready reporting package, and a monthly process your team can run without rebuilding everything from scratch.

This is not another SaaS metrics course. You build the system using your company’s actual data.

Applying takes about five minutes and does not commit you to anything. Every application gets a review call. The October cohort is capped at 25 companies.

Seven weeks from source files to go-live

7 weeksA focused implementation sprint, not an open-ended transformation project.
4 source systemsFinancial, bookings, people, and customer/revenue data.
1 operating systemLive metrics, board reporting, monthly ownership, and an AI-ready governed data layer.
Why this matters

Confidence in your metrics changes the conversation with investors.

“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 SteeleCFO, Iplicit · SaaS Metrics Foundation alum
The real problem

Your formulas are probably not the reason you doubt the numbers.

The hard part is getting the right data out of the GL, CRM, payroll system, invoice data, and subscription systems in a clean, repeatable format.

When the inputs are inconsistent, every downstream output becomes harder to explain: ARR movement, retention, gross margin, CAC, payback, operating leverage, board reporting, and diligence support.

The usual symptoms

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ARR movement does not tie out.
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The MRR waterfall depends on manual fixes every month.
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Bookings, customer, and people data use inconsistent definitions.
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The board package is rebuilt from disconnected spreadsheets.
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Diligence exposes source files and metric definitions that are difficult to defend.
Before

Monthly reporting is a reconstruction project.

  • Disconnected exports and spreadsheets
  • Manual fixes and undocumented assumptions
  • Metrics that change depending on who calculates them
  • Board questions that trigger another round of reconciliation
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After

Your team runs a governed monthly metrics process.

  • Four normalized source-data structures
  • Reconciled metrics and documented definitions
  • Live board-ready dashboard
  • Clear owners, checklist, and repeatable monthly workflow
What you build

Seven implementation artifacts your company owns at the end.

Every week ends with something tangible. You do not measure progress by videos watched. You measure it by what is live, reconciled, and usable.

Artifact 1

SaaS Financial Foundation

A mapped chart of accounts, SaaS P&L, revenue hierarchy, department structure, and gross-margin framework.

Built from: your GL and accounting data.

Artifact 2

Bookings & ARR Movement Engine

A consistent structure for new ARR, expansion, services, contraction, booking dates, and GTM attribution.

Built from: your CRM or bookings export.

Artifact 3

Workforce Economics Model

Employee and contractor mapping, FTEs, department costs, fully burdened costs, revenue per FTE, and ROSE inputs.

Built from: your payroll and HRIS data.

Artifact 4

Customer Revenue Cube + Reconciled MRR Waterfall

Customer IDs, invoice/subscription data, MRR movement, customer counts, churn, expansion, contraction, and retention inputs.

Built from: your billing, subscription, or invoice data.

Artifact 5

Live SaaS Metrics Dashboard

A working SoftwareMetrics.ai dashboard with reconciled outputs, benchmarks, and structured QA.

Built from: the four governed source-data structures.

Artifact 6

Board Metrics Pack

Decision-useful KPIs, definitions, known limitations, management narrative, and a monthly reporting checklist.

Built for: management, boards, investors, and diligence.

Artifact 7

AI-Ready Finance Reporting System

A governed finance data layer connected to Claude or ChatGPT through SoftwareMetrics.ai, plus a repeatable AI-enabled reporting workflow.

Built only after: the underlying data and metrics are trusted.

View Pricing & Apply

You built these from your company’s actual data — not a sample case study.

What “done” looks like

This is the system you are building toward.

Not theory. Not a sample spreadsheet you never use again. These are examples of the operating views and board outputs that become possible once the underlying finance data is structured and trusted.

MRR Waterfall showing beginning balance, new MRR, expansion, contraction, churn and ending MRR by month
The customer economics underneath the headline KPIs. A reconciled MRR waterfall makes new, expansion, contraction, and churn visible month by month.
SoftwareMetrics.ai Revenue Intelligence showing total ARR, dormant ARR, estimated upside ARR, ARR durability score and AI narrative
Then move from reporting to action. RevIntel surfaces dormant ARR, estimated upside, durability risk, and AI-generated revenue intelligence from the same customer data foundation.
AEGIS Board Pre-Read with plain-language board summary, flagged issues and evidence to inspect
From numbers to board conversation. Turn governed metrics into a concise pre-read that tells directors what changed, what matters, and what deserves attention.
Claude using live SoftwareMetrics data to create an executive dashboard from current company metrics
AI working from live finance data. Claude can query the SoftwareMetrics.ai connector and build analysis from current governed metrics instead of stale copied-and-pasted context.
Custom AEGIS operating dashboard built from SoftwareMetrics.ai data with financial profile, cost structure, revenue mix and KPI comparisons
Your data layer is not locked into one dashboard. Build a custom operating view for the way your company actually runs, then use SoftwareMetrics.ai as the governed metrics layer underneath it.
How the sprint works

Build. Submit. Validate. Move to the next milestone.

The sprint is run like an implementation project, not a lecture series.

Step 1

Build

Tuesday workshops establish the framework, required fields, definitions, and output for the week.

Step 2

Implement

Your team works inside its own source files between sessions and documents issues as they appear.

Step 3

Validate

Use office hours, QA checks, and the software to resolve mappings, tie-outs, and edge cases.

Step 4

Go live

Move from source-data readiness to dashboard, board reporting, and AI only after the foundation is trusted.

Plan on 3–5 hours per week outside the live sessions.

This is not passive training. You will work inside your company’s real GL, CRM, payroll, billing, and customer data between milestones.

If you want to learn the methodology, take SaaS Metrics Foundation. If you want the system implemented, join the Sprint.

Evidence, not course praise

What changes when the finance process becomes repeatable.

Investor readiness

“Ben taught me exactly how to make our finances into a repeatable process that made us look great in front of investors.”

Grant CavanaughChief Financial Officer
Repeatable across companies

“I have since applied the learnings in 3 different software businesses, all with great success.”

Sven BurgSaaS Metrics Foundation alum
Ongoing judgment

“Ben’s community is a constant source of answers to tricky questions going forward.”

Robert SenoffChief Financial Officer

Reviews from alumni of Ben’s SaaS metrics programs. The October Implementation Sprint is a new implementation format built on the same methodology.

Learn → Build → Run

Three products. Three different jobs.

The Sprint is not a premium version of a course. It is the implementation layer between learning the methodology and operating it every month.

Learn

SaaS Metrics Foundation

Understand the methodology, formulas, definitions, nuances, and benchmarks.

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Run

SoftwareMetrics.ai

Maintain, benchmark, report, and interrogate the metrics every month.

A governed data layer, not a closed dashboard

Once the data is trusted, build the finance interface you actually want.

The Sprint is not about locking your team into one reporting screen. SoftwareMetrics.ai becomes the governed metrics layer. From there, you can use AI to create custom dashboards, management views, board tools, or analysis workflows around the same trusted numbers.

Vibe-code the view. Keep the numbers governed.

For example, the custom operating dashboard shown here was created around live SoftwareMetrics.ai data. That means the presentation layer can change without rebuilding the underlying metric logic every time.

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Create a custom CFO or executive dashboard.
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Build a board or investor-specific reporting view.
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Ask Claude or ChatGPT questions against live governed metrics.
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Keep one source of metric logic underneath multiple interfaces.
The goal is not “AI that knows finance.” The goal is AI working from finance data you have already reconciled and can defend.
Custom AEGIS operating dashboard built on SoftwareMetrics.ai data
Example: a custom AEGIS operating dashboard built around the company’s own reporting priorities and powered by the same governed metrics layer.
Why AI is last

Everyone wants AI-ready finance. Almost nobody has AI-ready finance data.

Before you connect Claude or ChatGPT to finance, you need standardized definitions, governed source data, reconciled customer metrics, and trusted outputs.

That is why AI is Milestone 7, not Week 1. AI cannot repair an unreliable data foundation.

SoftwareMetrics.ai is used from the beginning

  • First upload in Week 1
  • Board-ready KPI dashboard
  • Benchmarking and structured QA
  • API/MCP connector for Claude and ChatGPT
  • Monthly reporting workflow
  • Board, planning, fundraising, and diligence support
The Go-Live Guarantee

Do the implementation work and I will stay with you until the system crosses the finish line.

Attend the core sessions, submit the four required source-data milestones, and complete the requested remediation work. If you still do not have a functioning, reconciled SaaS metrics dashboard and monthly reporting process by the end of the Sprint, you can join the next cohort at no additional charge.

This is an implementation guarantee, not a financial-results guarantee. It is designed to remove the risk of paying for another program and ending with unfinished work.

Seven-week implementation plan

One build sequence. No filler.

Core implementation workshops are held Tuesdays. Optional working office hours are held Thursdays. Each week has one required output.

Week 1
SaaS Financial Foundation
GL, SaaS P&L, revenue streams, COGS/OpEx, department mapping.
Output: SaaS Financial Foundation
Week 2
Bookings & ARR Movement
New vs. expansion ARR, services, contraction, booking dates, attribution.
Output: Bookings & ARR Movement Engine
Week 3
Workforce Economics
Payroll, contractors, FTEs, department mapping, burdened costs, ROSE.
Output: Workforce Economics Model
Week 4
Customer Revenue Cube
Invoice/subscription data, MRR schedules, waterfall, retention, ARR movement.
Output: Revenue Cube + MRR Waterfall
Week 5
Metrics Engine Goes Live
Load, reconcile, QA, and benchmark in SoftwareMetrics.ai.
Output: Live SaaS Metrics Dashboard
Week 6
Board Ready
Definitions, narrative, known limitations, monthly reporting checklist.
Output: Board Metrics Pack
Week 7
AI Ready + Go-Live Review
Connect governed metrics to Claude/ChatGPT and finalize the monthly operating process.
Output: AI-Ready Finance Reporting System
Who this is for

Built for the people who own the numbers and the process.

Founders & CEOs

You need metrics you can trust before board meetings, fundraising, diligence, exit discussions, or major operating decisions.

CFOs & Finance Leaders

You need a cleaner data foundation, repeatable monthly process, and a board-ready reporting package.

Controllers, FP&A & RevOps

You need alignment between CRM, billing, finance, payroll, customer success, and reporting systems.

Good fit

  • You have real SaaS source data to work with.
  • Your team owns metrics, FP&A, board reporting, or finance operations.
  • You want a repeatable process, not a one-time dashboard.
  • You can assign an implementation owner and commit 3–5 hours per week.

Not a good fit

  • You are pre-revenue without meaningful operating data.
  • You only want a basic introduction to SaaS metrics.
  • You want a fully done-for-you consulting engagement.
  • You cannot access or work with the source data during the sprint.
Instructor

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.

The sprint is based on the practical process used to onboard SaaS finance clients: identify the required sources, normalize the inputs, load the model, validate the output, and create a process the team can maintain.

What Ben brings to the room

  • Judgment on SaaS metric definitions and edge cases
  • Practical data normalization and reporting experience
  • Board, investor, diligence, and exit-readiness perspective
  • Templates, software, benchmarks, and implementation workflows
  • AI economics and AI-ready finance data design
How enrollment works

Applying is not enrolling.

Every seat is confirmed after a conversation. The application exists so the sprint fits your stage, your data, and your goals.

1

Apply

About five minutes. Tell Ben your stage, systems, team, and what you are preparing for. No payment and no commitment.

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2

Review call

Talk through the four data sources and whether the sprint is the right move now. If it is not, Ben will tell you.

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3

Confirm your seat

Choose the support level that fits your team. Payment plans are available on every tier.

Messy data is not a reason to wait. It is the reason the sprint exists. You need access to the real source systems and the willingness to work through them — not a perfect starting point.
Investment

Choose your level of implementation support

All three passes follow the same seven-week implementation sequence. The difference is how much private review, troubleshooting, and direct access your team wants while it builds.

Applications close September 15. The October cohort is capped at 25 companies.

Company Pass

The full implementation system for a hands-on finance team.

$4,995
Up to 2 participants
or 3 Ă— $1,665/mo
  • 7-week guided implementation sequence
  • 4 core source-data implementation workshops
  • 4 optional source-data office hours
  • Metrics go-live workshop + troubleshooting lab
  • Board-ready workshop + narrative clinic
  • AI capstone + private cohort Demo Day
  • Required milestone templates and checklists
  • SaaS Metrics Accelerator model
  • Lifetime access to SaaS Metrics Foundation
  • Annual SoftwareMetrics.ai access
  • API/MCP connector access for Claude and ChatGPT
  • Go-Live Guarantee
Apply

No payment today. Seat confirmed after your review call.

Executive Pass

Maximum access and concierge implementation support. Limited to 3 companies.

$14,995
Up to 3 participants
or 3 Ă— $4,998/mo

Everything in Implementation Pass, plus:

  • Multiple private working sessions
  • Direct priority access throughout the sprint
  • Concierge onboarding for data sources and software setup
  • Personal review of AI COGS structure and AI economics reporting
  • Executive-level board and diligence preparation support
Apply

3 company seats. Confirmed after your review call.

One board meeting spent defending numbers you are unsure of — or one fundraise or exit process slowed by messy diligence — costs more than the Sprint.
FAQ

Questions before you apply?

What happens after I apply?

Applying is not enrolling. There is no payment or commitment at the application stage. Ben reviews every application and schedules a conversation to discuss your systems, data, stage, and goals. Seats are confirmed after that call.

Do I need perfect data before joining?

No. The sprint is designed to help you identify what you have, what is missing, and what needs to be cleaned. You do need access to real financial, bookings, people, and customer/revenue data and an owner who can work on it.

How much work should we expect each week?

Plan on roughly 3–5 hours per week outside the live sessions. Your team will work inside its own source files, complete the required milestone output, and maintain an issue log for questions that require clarification or cross-functional follow-up.

Is this a course or consulting?

It sits between the two. You receive the methodology, templates, software, implementation labs, and live guidance. Your team does the data work. The software generates the dashboard. Ben supplies the framework, judgment, troubleshooting, and accountability.

Who from my company should attend?

Good combinations include CFO and controller, founder and finance lead, FP&A and controller, or finance and RevOps. One person rarely owns all four source systems, which is why the Company Pass includes two participants.

How does SoftwareMetrics.ai fit in?

You log in before the cohort and make the first upload in Week 1. As the data improves, you generate and validate the board-ready dashboard. Annual access, API access, and the MCP connector for Claude and ChatGPT are included.

Do I have to replace our internal spreadsheet models?

No. You receive Excel and Google Sheets Accelerator models for offline analysis, internal manipulation, and audit support, plus a SoftwareMetrics.ai workspace for automated reporting, benchmarking, and Claude/ChatGPT integrations. Your team can use either layer independently or together.

Why is AI the final milestone?

AI cannot repair an unreliable data foundation. Once the metrics are trusted, Claude and ChatGPT can help produce board updates, investor summaries, leadership reports, and diligence responses using the governed SoftwareMetrics.ai data layer.

What is the Go-Live Guarantee?

Attend the core sessions, submit the four required source-data milestones, and complete the requested remediation work. If you still do not have a functioning, reconciled SaaS metrics dashboard and monthly reporting process by the end of the Sprint, you may join the next cohort at no additional charge.

Will my confidential financial data be shared publicly?

No. Demo Day is a private cohort working session, not a public presentation. Each company controls what it shares and may present summarized, redacted, or sample outputs. You are never required to expose confidential customer-level or company-level financial data.

Are payment plans available?

Yes. Every tier can be paid in three monthly installments.

What is the difference between the three passes?

All three passes follow the same seven-week implementation sequence and include the software, templates, board-ready and AI milestones, and the Go-Live Guarantee. The Implementation Pass adds private source-data review, reconciliation review, dashboard sign-off, and priority troubleshooting. The Executive Pass adds multiple private working sessions, concierge onboarding, a third participant, executive board/diligence support, and direct priority access.

When the board asks where the number came from, you should be able to show them.

Build a board-ready, diligence-ready, and AI-ready metrics operating system your team can run every month.

The confidence is not memorized. It is built.

Apply for the October Sprint

Applications close September 15 · Cohort begins October 6 · Capped at 25 companies · No payment at the application stage