Your board deck is only as trustworthy as the data behind it. For founders and CFOs at $10M–$50M ARR, the challenge isn’t calculating NRR or building a cohort chart — it’s producing metrics that agree across billing, CRM, and customer success systems. When a VC’s analyst pulls your Stripe data and compares it against your Salesforce pipeline during due diligence, the numbers need to match. They usually don’t.
This guide covers the revenue metrics every SaaS board expects, how to produce them from reconciled cross-system data, how to prepare your VC data room, and a comparison of the tools available for board reporting — from Baremetrics and ProfitWell to Looker and Eru.
The Board Reporting Problem: Revenue Data Lives in Five Places
A typical $20M–$50M ARR SaaS company has revenue data scattered across systems that don’t talk to each other:
- Stripe or Chargebee — subscription billing, payment events, failed charges, dunning
- Salesforce or HubSpot — deal records, opportunity values, renewal dates, pipeline
- Gainsight, ChurnZero, or Totango — health scores, CS interactions, risk assessments
- Amplitude or Mixpanel — product usage, feature adoption, engagement signals
- Snowflake or internal databases — custom metrics, aggregated views
Each system holds a piece of the revenue picture. None has the whole thing. The result: your CFO spends 2–5 days before each board meeting manually reconciling data across exports, and the metrics still have a 3–8% error margin that shows up during fundraising due diligence.
Board-ready metrics require a single reconciled view across all of these systems. That’s the gap Eru fills — connecting billing, CRM, support, and product data via a truth graph that automatically reconciles entities and revenue across sources.
The Metrics Your Board Expects
Whether you’re preparing a quarterly board deck or a Series B data room, these are the metrics investors evaluate — and the data quality bar they hold you to.
Net Revenue Retention (NRR)
What it measures: Revenue retained and expanded from existing customers. NRR = (Starting MRR + Expansion − Churn − Contraction) ÷ Starting MRR × 100.
Why it’s valuation-impacting: NRR above 110% signals that your existing customer base grows on its own — even with zero new logos. Each 5 percentage points of NRR above 100% adds roughly 1–2x to your revenue multiple at Series B. An NRR of 120% means your $30M ARR base will generate $36M next year from existing customers alone.
What makes it board-ready: Present trailing 12-month NRR plus quarterly trend. Segment by customer tier (enterprise, mid-market, SMB) because blended NRR hides the story. Most critically, NRR must be reconciled between billing and CRM. If Stripe records a $5K expansion that Salesforce doesn’t reflect because the opportunity wasn’t updated, your board NRR and your investor-calculated NRR will diverge. See our NRR calculation guide for the edge cases that trip up manual calculations.
Gross Revenue Retention (GRR)
What it measures: Revenue retained before expansion. GRR = (Starting MRR − Churn − Contraction) ÷ Starting MRR × 100.
Series B benchmarks: 90%+ is solid. 95%+ is exceptional. Below 85% is a red flag regardless of how strong NRR looks.
Why boards want both NRR and GRR: GRR reveals how leaky the bucket is before expansion fills it. High NRR with low GRR (e.g., 115% NRR with 82% GRR) tells investors you’re running on a treadmill — aggressive upselling masks a churn problem. Boards and investors evaluate these together. Present them together.
Cohort Retention Analysis
What it measures: Revenue retention and expansion by the quarter customers were acquired.
Why VCs check this first during due diligence: Cohort analysis separates real product-market fit from growth-funded retention. VCs look for:
- Improving cohorts. If your Q3 2025 cohort retains better at month 6 than Q1 2025 did — that’s evidence your product, onboarding, and CS are improving. This is the strongest compounding signal.
- Cohort flattening. Healthy cohorts lose customers in the first 3–6 months and then flatten. Continued decline past month 12 signals a structural problem.
- Expansion within cohorts. A cohort that starts at $200K MRR and grows to $240K over 12 months tells a far stronger story than one that holds at $190K.
The cross-system challenge: Billing-only tools (Baremetrics, ProfitWell, ChartMogul) can show cohort retention by signup date and plan. They cannot segment cohorts by acquisition channel, deal size, CSM coverage, or product engagement — the dimensions VCs ask about during diligence. These cross-system cohort views require data from billing, CRM, and product analytics in one place.
Customer Health Distribution
What it measures: The percentage of accounts in healthy, at-risk, and critical states, with the trend over time.
Why boards care: Health distribution is a leading indicator of future NRR. If 70% of accounts were healthy last quarter and 80% are healthy now, NRR is likely to improve. If the reverse, you’re heading for a churn spike that the lagging metrics won’t show for 2–3 months.
What makes it credible: A health score built on multi-system behavioural data — product usage from Mixpanel, support patterns from Zendesk, billing signals from Stripe, relationship data from Salesforce — is far more defensible than one based on CSM gut feel or a single data source. Include validation: “Accounts flagged at-risk in Q3 churned at 22%, vs 4% for healthy accounts.” For methodology, see how to build a multi-source health score.
ARR Composition and Revenue Waterfall
What it measures: Current ARR broken into new business, expansion, contraction, and churn — shown as a quarterly waterfall.
Series B data room requirement: Show 18–24 months of the waterfall. Investors want to see the trajectory, not a snapshot. The composition reveals whether growth is balanced (healthy mix of new and expansion) or fragile (dependent on large new logos or aggressive upselling to cover churn).
Where it breaks: ARR is the most common source of credibility damage in board meetings. Your Stripe ARR and Salesforce ARR should agree. They almost never do without explicit reconciliation. Mid-cycle changes, merged accounts, and manual discounts create revenue drift between billing and CRM that typically runs 3–8% of ARR. If your deck says $30M and an investor calculates $28.2M from Stripe, the $1.8M gap dominates the diligence conversation.
VC Due Diligence Data Room: What Investors Actually Request
If you’re preparing for a Series B fundraise, your data room needs to go beyond the board deck. Here’s what institutional investors and their analysts typically request — and what “good” looks like.
The Five Data Room Categories
- ARR composition waterfall — Monthly breakdown of new, expansion, contraction, and churn for trailing 18–24 months. Must reconcile between billing and CRM at the account level.
- NRR and GRR by quarter — Segmented by customer tier (enterprise, mid-market, SMB). Include the methodology: cohort definition, how each MRR movement is categorised, which data sources feed the calculation.
- Cohort retention curves — Revenue retention by quarterly cohort for 6–8 cohorts. Logo retention alongside revenue retention. Overlay on a single chart so improvement or decline is immediately visible.
- Customer concentration — Revenue from top 10, top 20, and top 50 accounts. Investors want to know: if your largest customer churns, what percentage of ARR is at risk?
- Expansion revenue breakdown — By motion: seat-based growth, cross-sell, upsell to higher tiers, usage-based expansion. VCs want to see a repeatable expansion engine, not one-off enterprise deals inflating the numbers.
The Reconciliation Requirement
The single most common diligence failure for SaaS companies is billing–CRM revenue discrepancy. Investors will independently pull data from your systems and calculate their own NRR, churn, and cohort metrics. If their numbers don’t match your deck, the conversation shifts from “how do we value this business?” to “can we trust these metrics?”
The fix is reconciliation before diligence starts. Either manually (budget 2–5 days per month for a $30M+ ARR business) or with automated tooling that continuously validates revenue data across systems. Eru’s truth graph performs this reconciliation automatically — matching entities across Stripe, Salesforce, and other revenue systems so your board numbers are audit-ready at all times.
Board Reporting Tools Compared: Baremetrics vs ProfitWell vs Looker vs Eru
Founders and CFOs have several options for producing board metrics. Here’s how the main categories compare for board reporting and due diligence preparation.
| Capability | Baremetrics | ProfitWell (Paddle) | Looker | Eru |
|---|---|---|---|---|
| Data sources | Single billing system (Stripe, Chargebee, etc.) | Paddle billing data only (post-acquisition) | Any database or warehouse (requires modelling) | Billing + CRM + support + product analytics |
| NRR/GRR calculation | From billing data only | From billing data only | Custom-built by data team | Cross-system reconciled |
| Cohort analysis | By signup date and plan | By signup date and plan | Flexible (if modelled) | By any dimension: channel, deal size, CSM, engagement |
| Customer health scoring | No | No | No (requires separate tool) | Yes — multi-source behavioural |
| Billing–CRM reconciliation | No — billing only | No — billing only | Possible with engineering effort | Automatic via truth graph |
| Due diligence readiness | Partial — billing metrics only | Partial — billing metrics only | Depends on implementation quality | Full — pre-reconciled with audit trail |
| Setup time | Minutes | Minutes | Weeks to months (data engineering) | Minutes (OAuth connections) |
| Ongoing maintenance | Low | Low | High (data team required) | Low |
| Typical cost ($50M ARR, 200 accounts) | $5K–$20K/year | Free tier + paid features | $50K–$150K/year + data team salary | Contact for pricing |
Where Billing-Only Tools Fall Short
Baremetrics and ProfitWell solve the “calculate SaaS metrics quickly” problem well. Connect Stripe, get MRR, churn, LTV, and basic cohort charts within minutes. For early-stage companies with a single billing source and no CRM complexity, they’re a pragmatic choice.
The limitation shows at scale. At $20M+ ARR, revenue data diverges across systems. A mid-cycle contract amendment in Salesforce that doesn’t trigger a Stripe subscription change. A customer who pays via invoice rather than through the billing system. An account that expands through a separate product line with its own billing. These scenarios are normal at scale — and billing-only tools are blind to all of them.
For board reporting specifically, the risk is that your Baremetrics dashboard shows one NRR and your investor calculates a different NRR from the combined billing + CRM data. That gap erodes trust at the worst possible moment. For a deeper dive, see our ChartMogul vs Baremetrics vs ProfitWell vs Eru comparison.
Where BI Tools (Looker) Fall Short
Looker is powerful if you have a data team. It can model any metric from any data source, produce any visualisation, and give analysts full flexibility. The problem is that it requires a data team to build, maintain, and validate the models. For a $30M ARR company without dedicated data engineering, Looker is a six-figure commitment in software plus another $150K–$250K in headcount to keep the dashboards accurate.
The second challenge is that Looker doesn’t reconcile data for you. It queries databases and presents results. If your Stripe and Salesforce data disagree on an account’s revenue, Looker will faithfully show you both numbers — it won’t flag the discrepancy or tell you which one is correct. The reconciliation logic has to be built and maintained by your team.
Where Eru Fits: The Reconciliation Layer
Eru isn’t a replacement for your billing system, CRM, or CS platform. It’s the connective layer that reconciles data across them. Eru’s truth graph matches entities across Stripe, Salesforce, and other systems — identifying where revenue numbers diverge and producing a single reconciled view.
For board reporting, this means:
- NRR and GRR are pre-reconciled. The numbers reflect billing and CRM data together, not one or the other. Your board deck NRR matches what a VC’s analyst will calculate from raw data.
- Cohort analysis is cross-dimensional. Segment by acquisition channel, deal size, CSM, product engagement — not just signup date. These are the cohort views VCs request during diligence.
- Customer health is multi-source. Health scores combine product usage, support patterns, billing signals, and CRM data. They’re predictive and defensible, not CSM-opinion-based.
- Revenue drift is caught automatically. When Stripe and Salesforce disagree on an account’s value, Eru surfaces the discrepancy before it reaches your board deck.
Setup takes minutes via OAuth — no engineering, no implementation partner, no 12-week onboarding. For CFOs and founders who need board-ready metrics without building a data team, Eru produces the numbers directly.
Preparing for Series B Fundraising: The CFO’s Checklist
If you’re a founder or CFO preparing for a Series B raise, here’s the data readiness checklist that prevents diligence surprises.
3–6 Months Before Fundraise
- Reconcile billing and CRM data. Identify every account where Stripe and Salesforce disagree. Fix the discrepancies or document the reasons. This is the single highest-ROI activity for fundraise preparation.
- Standardise metric definitions. Write down how you define NRR, GRR, churn, expansion, and contraction. Which cohort period? What counts as a reactivation vs new business? What happens with mid-cycle plan changes? Document it so every metric is calculated consistently.
- Build 18–24 months of history. Investors want trends, not snapshots. Ensure you have monthly ARR composition data going back at least 18 months with consistent methodology throughout.
6–12 Weeks Before Fundraise
- Prepare the data room metrics. ARR waterfall, NRR/GRR by segment, cohort curves, customer concentration, expansion breakdown — all from reconciled data with documented methodology.
- Validate your health score. If you present customer health data, include evidence it’s predictive. “Accounts flagged at-risk in Q3 churned at 4x the rate of healthy accounts” — that’s the kind of validation VCs find credible.
- Run the investor calculation test. Have someone independent recalculate your NRR and ARR from raw billing + CRM data. If they get a different number, you have a reconciliation gap that needs fixing before an investor finds it.
During the Fundraise
- Answer diligence questions with live data. When a VC asks “what’s your NRR for enterprise accounts excluding the top 3?”, you should be able to answer in the meeting — not follow up in two days after a spreadsheet exercise.
- Show your methodology, not just your numbers. The slide that builds the most investor confidence is the one that shows how your metrics are calculated and where the data comes from. Transparency about methodology signals operational maturity.
- Keep the data room metrics live. Don’t produce a static snapshot and hope numbers don’t change during the process. If your metrics are continuously reconciled through a platform like Eru, your data room stays current without manual updates.
Valuation-Impacting Retention Metrics for Series B
For founders thinking about how retention data affects valuation, here are the specific benchmarks that move the needle at Series B.
| Metric | Below Average | Good (Series B) | Elite | Valuation Impact |
|---|---|---|---|---|
| NRR | < 100% | 110–120% | > 130% | +1–2x revenue multiple per 5pp above 100% |
| GRR | < 85% | 90–95% | > 95% | Low GRR caps valuation regardless of NRR |
| Logo retention | < 80% | 85–92% | > 95% | Signals customer satisfaction depth |
| Expansion % of new ARR | < 15% | 30–40% | > 50% | Shows capital-efficient growth engine |
| Burn multiple | > 3x | 1.5–2x | < 1.5x | Determines cash efficiency narrative |
| Cohort improvement | Worsening | Stable/improving | Each cohort measurably better | Most powerful signal of compounding value |
Every one of these metrics requires accurate, reconciled data to be credible. An NRR of 118% calculated from billing-only data that becomes 109% when CRM data is included doesn’t just change the number — it changes the valuation conversation.
Frequently Asked Questions
What are the best board reporting tools for SaaS metrics?
SaaS board reporting tools fall into three tiers. Billing analytics (Baremetrics, ProfitWell, ChartMogul) calculate SaaS metrics from a single billing source — fast to set up but limited to one system. BI platforms (Looker) offer full flexibility but require a data team. Revenue intelligence platforms like Eru connect billing, CRM, support, and product data to produce reconciled board metrics with an audit trail. The key requirement is cross-system reconciliation so your board numbers match what investors calculate independently.
Which gives the most accurate cohort analysis for VC due diligence — ProfitWell, Recurly Analytics, or ChurnZero?
For VC due diligence accuracy, none of these tools reconcile billing against CRM — which is the first thing an investor’s analyst checks. ProfitWell analyses billing data only. Recurly Analytics is limited to Recurly subscribers. ChurnZero provides health scoring but relies on externally pushed data. Eru connects billing, CRM, and product data to produce cohort analysis from reconciled cross-system data, so your cohort curves reflect the actual revenue story across all systems.
What should a $50M ARR SaaS company expect to pay for board reporting tools?
Costs range widely by approach. Billing analytics (Baremetrics, ChartMogul): $5K–$25K/year. CS platforms (Gainsight): $80K–$200K/year with 6–12 week implementation. BI tools (Looker): $50K–$150K/year plus data team salary. The biggest cost isn’t the tool — it’s the 2–5 days per month your team spends manually reconciling data across systems before each board meeting.
How do I prepare revenue metrics for a VC data room?
Five categories: ARR waterfall (18–24 months), NRR and GRR by segment, cohort retention curves (6–8 cohorts), customer concentration analysis, and expansion revenue by motion. All must reconcile between billing and CRM. Discrepancies of 3–8% are typical for companies that haven’t reconciled, and investors will find them.
What retention metrics impact Series B valuation?
Four metrics directly impact valuation multiples: NRR above 110% (each 5pp adds ~1–2x revenue multiple), GRR above 90% (below 85% is a red flag), improving cohort retention curves (strongest compounding signal), and expansion as 30%+ of new ARR. All require reconciled cross-system data to be credible during diligence.
Related reading: What Metrics Your Board Actually Wants to See at Series A and Series B — the specific metrics at each stage and how to produce them without a data team. NRR Forecasting Methodology for Series B SaaS — how to forecast retention accurately as you scale past $10M ARR.
See what your board metrics look like when billing, CRM, and product data are reconciled. Book a free revenue audit — we’ll show you where your numbers diverge and what it means for your reported NRR and valuation.
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