How to build a SaaS metrics dashboard from your billing data

Every SaaS company tracks MRR, churn, and expansion revenue. Few do it well.

Your billing system records every transaction. Stripe knows about every subscription created, every invoice paid, every upgrade and downgrade. Chargebee and Recurly do the same. The raw data is all there.

But open your Stripe dashboard and try to answer: "What's our net revenue retention by quarterly cohort?" or "Which pricing plan has the highest expansion rate?" You'll find subscription counts and gross volume. You won't find the metrics your board is asking about.

So you end up in spreadsheets. You download a CSV of invoices, build a formula to categorize each line as new, expansion, contraction, or churn, and manually compute an MRR waterfall. It works until pricing changes, prorations appear, or someone switches from annual to monthly mid-cycle. Then it breaks quietly, and nobody notices until the numbers don't add up at the board meeting.

There's a faster path, and it doesn't require a data warehouse or a dedicated finance team.

Why billing tools aren't analytics tools

Stripe, Chargebee, and Recurly are excellent at what they do: manage subscriptions, process payments, handle invoicing. Their built-in dashboards reflect that focus.

What you get from a billing dashboard:

• Gross revenue and transaction volume
• Refunds and disputes
• Active subscription counts
• Basic MRR (often just a sum of active subscription values)
• Payment failure rates

For payment operations, this is useful. You can see if revenue is flowing, catch spikes in failed payments, and track refund rates.

What you don't get:

• Net revenue retention over time
• Expansion vs. contraction breakdown
• Cohort-based churn analysis
• LTV by pricing plan or customer segment
• CAC payback period
• MRR waterfall (new, expansion, contraction, churn)

These are the metrics investors ask about, the numbers that tell you whether your business is actually healthy. And they require calculations that billing dashboards simply don't perform.

The gap gets worse when you need to combine billing data with other sources. LTV is useful on its own, but LTV by acquisition channel requires CRM or marketing data. Churn rate matters, but churn correlated with product usage tells you something actionable. Your billing system can't reach into those other systems.

Then there's the spreadsheet tax. Every month, someone:

• Downloads invoice data from Stripe or Chargebee
• Categorizes each line item as new, expansion, contraction, or churn
• Handles edge cases (prorated charges, credits applied, trial conversions)
• Calculates metrics manually and builds charts

This process is slow, error-prone, and fragile. It breaks the moment your pricing model changes or you introduce a new plan tier. Prorations are particularly painful: a mid-cycle upgrade generates a credit and a new charge, and if your formula doesn't handle both correctly, your MRR numbers are wrong.

Most teams tolerate this because they think the alternative is a data warehouse project. It isn't.

The SaaS metrics that matter (and where the data lives)

Before building a dashboard, it helps to know what you're measuring and where the inputs come from. This isn't a textbook definitions section. It's a practical map of what each metric needs.

MRR and ARR

• Source: Billing system
• The catch: Raw subscription amounts include trials that haven't converted, prorated charges, credits, and sometimes one-time fees mixed in with recurring revenue. A simple sum of active subscriptions overstates MRR for most companies.

Net revenue retention (NRR)

• Source: Billing system (time-series tracking across cohorts)
• The catch: Calculating NRR requires separating each cohort's revenue into expansion (upgrades, additional seats), contraction (downgrades), and churn (cancellations) over time. This isn't a number your billing tool surfaces.

Churn rate

• Source: Billing system, plus CRM to understand the "why"
• The catch: Two versions tell different stories. Logo churn counts customers lost. Revenue churn measures dollars lost. A company with 5% logo churn could have 2% revenue churn (small customers leaving) or 10% revenue churn (large customers leaving).

LTV and CAC payback

• Source: Billing (LTV) + CRM/ad platforms/accounting (CAC)
• The catch: LTV comes from billing data, but CAC requires marketing and sales spend data. Calculating LTV without segmenting by plan, cohort, or acquisition channel is a common mistake that hides important differences.

Expansion revenue

• Source: Billing system
• The catch: The data is often messy. An upgrade might show as a cancellation of the old plan and creation of a new one, rather than a clean "expansion" event. Mid-cycle changes generate prorations that need careful handling.

The pattern: most of these metrics start with billing data but need either careful transformation or data from a second source to be truly useful.

Three approaches to building a SaaS metrics dashboard

Approach A: Spreadsheets and billing exports

The most common starting point.

How it works: Export your Stripe or Chargebee data to CSV. Build an MRR waterfall in Google Sheets. Categorize each invoice line as new, expansion, contraction, or churned revenue. Chart it.

When it's fine:

• Pre-revenue or very early stage with a handful of customers
• You need a one-time snapshot for an investor update
• Your pricing is simple (one plan, monthly billing, no add-ons)

Where it breaks:

• The process is manual and needs to be repeated monthly
• Errors compound over time. One miscategorized invoice throws off your MRR trend
• Edge cases multiply as you grow. Annual plans, mid-cycle upgrades, credits, prorations, trials converting to paid, these all require special handling
• Nobody else can reproduce your spreadsheet. The logic lives in formulas that only the person who built them understands
• It doesn't scale past about 100 customers before the manual work becomes unsustainable

Spreadsheets are a reasonable starting point for very early-stage companies. But they're a temporary solution that most teams outgrow within a few months.

Approach B: Dedicated SaaS metrics tools

Tools like ChartMogul, Baremetrics, and ProfitWell (now Paddle) exist specifically for this problem. They plug directly into Stripe, Chargebee, or Recurly and auto-calculate the standard SaaS metrics.

What you get:

• MRR, ARR, and MRR waterfall calculated automatically
• Churn rate (logo and revenue)
• LTV and ARPU
• Net revenue retention
• Pre-built dashboards that update daily
• Cohort analysis out of the box

This is genuinely good for standard metrics. If your primary need is a clean MRR dashboard connected to Stripe, these tools deliver it with minimal setup. They handle the edge cases (prorations, trials, credits) that make spreadsheets painful.

Where they fall short:

• You're limited to the metrics they define. If their churn calculation doesn't match how your business thinks about churn, you can't easily change it
• They only see billing data. You can't combine it with CRM data to see churn by acquisition channel, or with product data to correlate usage with retention
• Customization is limited. Want a metric that doesn't fit their model? You're back to exporting data
• It's another subscription ($100-500+/month for most plans)
• If you later want to do custom analysis or bring in other data sources, you'll need a different tool anyway

For teams that just need standard SaaS metrics from their billing system, these tools are a solid choice.

Approach C: Connect billing data to an AI-native analytics platform

This is the approach we built Fabi around.

How it works: Connect your Stripe account, your Postgres or MySQL billing database, or payment data directly. Then ask questions in plain English:

"What's our net revenue retention by quarterly signup cohort?"

"Which pricing plan has the highest expansion rate?"

"Show me the MRR waterfall for the last 12 months, broken down by plan tier."

"What's the median time from trial start to first paid invoice?"

We generate the SQL behind every query, so you can see exactly how each metric is calculated. No black box. If you disagree with how something is categorized, you can adjust the logic or ask a follow-up question.

Why this approach is different:

• No metric limitations. You get the standard metrics (MRR, churn, NRR), but you're not limited to pre-defined calculations. You can ask questions the dedicated tools can't answer because you're not constrained by their data model.
• Cross-source queries. Connect your CRM alongside Stripe and ask: "What's our LTV by acquisition channel?" or "Do customers from outbound sales churn at a different rate than inbound?"
• Product + billing insights. Connect your product database and ask: "Is there a correlation between feature usage in the first 30 days and 12-month retention?"

These cross-source questions are where the actual strategic insights live. And they're questions that neither billing dashboards nor dedicated SaaS metrics tools can answer.

Being honest about the trade-offs: Dedicated SaaS metrics tools are faster for standard metrics. If all you need is an MRR dashboard from Stripe, ChartMogul will get you there with less setup. Fabi is the better choice when you need custom analysis, cross-source queries, or when you want one platform that handles billing analytics alongside everything else.

Step by step: building your first SaaS metrics dashboard

Step 1: Pick your core metrics.Start with three: MRR, churn rate, and net revenue retention. These give you a clear picture of revenue health. Add LTV, CAC payback, and expansion metrics later.

Step 2: Connect your billing data source.Whether it's a Stripe integration, a direct database connection, or an API, get your billing data into your analytics tool. With Fabi, this takes a few minutes: authenticate with Stripe or connect your billing database, and your data is available immediately.

Step 3: Ask your first question.Start simple. "What's our MRR for each of the last 12 months?" See if the numbers match your expectations. If something looks off, investigate. This validation step catches data quality issues early, before you build a dashboard on top of wrong numbers.

Step 4: Build out the dashboard.

• Add an MRR waterfall chart (new, expansion, contraction, churn)
• Add a churn trend line
• Build a net revenue retention view by cohort
• Set time ranges that match your reporting cadence (monthly for operating metrics, quarterly for board-level)

Step 5: Combine with other sources.This is where things get interesting:

• CRM for acquisition channel data ("Do content marketing customers retain better than paid ads?")
• Product database for usage metrics ("Which features correlate with lower churn?")
• Support tools for health signals ("Do customers who file tickets in month one churn faster?")

Step 6: Share with your team.A dashboard nobody looks at is a wasted dashboard. Share it with your co-founders, finance lead, and board. Set up a recurring review cadence. The best SaaS dashboards aren't just tracking metrics, they're driving conversations about what to do next.

Common mistakes when building SaaS dashboards

• Counting trials as MRR. Trials haven't converted yet. Including them inflates your MRR and makes your churn look worse when they don't convert. Fix: Separate trial subscriptions from paid subscriptions in your calculations.
• Not distinguishing logo churn from revenue churn. Losing 10 customers sounds bad. But if they were all on your cheapest plan and represented 2% of revenue, that's very different from losing 10 enterprise accounts. Fix: Track both, and know which one your audience cares about.
• Ignoring prorations and credits. A mid-cycle upgrade generates a prorated credit for the old plan and a prorated charge for the new one. If your MRR calculation just sums invoice amounts, prorations create false spikes and dips. Fix: Make sure your tooling handles prorations and credits correctly.
• Calculating LTV without segmenting. Your overall LTV is an average that hides important differences. Enterprise plan LTV is probably very different from starter plan LTV. Partnership-sourced customers may differ from cold outbound. Fix: Segment by plan, cohort, or acquisition channel before you calculate.
• Building a dashboard nobody updates. Pricing changes, new plans launch, metrics definitions evolve. If nobody owns the dashboard, it drifts out of sync with reality and people stop trusting it. Fix: Assign an owner and review it quarterly.

Your billing data has the answers. You just need the right tool to ask the questions.

Your billing system captures every transaction, every subscription change, every payment. The raw material for a complete SaaS metrics dashboard is already there, generated automatically every time a customer signs up, upgrades, or cancels.

The gap isn't data. It's the layer between raw transactions and the metrics that actually inform decisions. MRR waterfalls, cohort retention curves, expansion rates by segment. These are the views that tell you whether your business is healthy, where the risks are, and where the opportunities live.

You don't need a data warehouse to get there. You don't need a finance team building spreadsheets every month. Connect your billing data, start with the three metrics that matter most, and build from there.

Try Fabi free and build your first SaaS metrics dashboard today.