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Data analytics for SaaS companies

SaaS analytics for founders, CFOs and RevOps leaders: one reconciled metrics layer for ARR, retention and unit economics, with dashboards your board can trust.

See how we work
NRR fell because of contraction in the SMB planSaaS dashboard: ARR up 13%, NRR down 5 points; a bar chart shows the SMB plan lost 3.6 points of NRR from downgrades (demo data).SaaS metrics, Q3Demo dataNRR fell because of contraction in the SMB planARR$14.2M+13% vs Q2Net revenue retention104%-5 pts vs Q2Gross revenue retention91%-1 pt vs Q2NRR change by plan, Q2 to Q3pointsSMB plan-3.6Mid-market-1.0Enterprise-0.4Downgrades from Pro to Basic, not churnNRR fell because of contraction in the SMB planSaaS dashboard: ARR up 13%, NRR down 5 points; a bar chart shows the SMB plan lost 3.6 points of NRR from downgrades (demo data).SaaS metrics, Q3Demo dataNRR fell because of contraction in the SMB planARR+13% vs Q2$14.2MNet revenue retention-5 pts vs Q2104%Gross revenue retention-1 pt vs Q291%NRR change by plan, Q2 to Q3pointsSMB plan-3.6Downgrades from Pro to Basic, not churnMid-market-1.0Enterprise-0.4

SaaS analytics should tell you how much recurring revenue you have, how much you keep and what it costs to grow. Too often, the answer depends on which system you ask. Billing says one thing, the CRM another and the product database a third.

We build a tool-agnostic SaaS metrics layer that reconciles those sources, defines each SaaS KPI once and feeds every dashboard, board report and financial model. We are not a software vendor. We work with HubSpot, Salesforce, your billing and accounting systems and Power BI to give leadership one governed view of SaaS performance.

The goal is practical: decide which accounts to renew first, where to spend acquisition budget and how much runway you really have.

Challenges we solve

SaaS metrics disagree because billing, CRM and product data count customers differently

Metric disagreements in SaaS companies are rarely simple calculation errors. They come from the way each system records the customer, the contract and the moment revenue changes.

Sales reports bookings from the CRM. Finance reports invoices and recognized revenue from billing and accounting. Product teams report usage from the application database. Each view is correct on its own terms, which is why the disagreement is so hard to resolve in a meeting.

Same company, four different ARR figures

Reported ARR by source system, $M, end of June

Demo data
CRM bookings$12.9MProduct data$12.4MBilling$12.1MAccounting$11.8M$12.9M in CRM bookings: Includes contracts not yet billedCRM bookings$12.9MProduct data$12.4MBilling$12.1MAccounting$11.8M$12.9M in CRM bookings: Includes contractsnot yet billed

So what: A $1.1M gap between systems is a definitions problem, not a calculation error.

  1. 1

    One customer, one identity

    We match account IDs across billing, CRM and product systems, including parent and child accounts and resellers.

  2. 2

    Periods that line up

    We model bookings, invoices and recognized revenue separately, then reconcile each one to its source system.

  3. 3

    One rule for every metric

    We agree how upgrades, credits, pauses and multi-year deals affect ARR, churn and expansion, then document it.

  4. 4

    Fixes that stick

    Manual spreadsheet corrections move into the source system or the model, so the next report agrees.

  5. 5

    Churn risk visible to finance

    Product usage is joined to accounts and renewal dates, so finance and customer success see risk together.

  6. 6

    Currency and pricing handled once

    Multi-currency contracts and price changes are normalized in the model, not in each report.

The result is familiar: board decks with footnotes explaining why ARR differs from last month, churn rates that change depending on who ran the query, and CAC calculations nobody can reproduce. Each disagreement costs leadership time and weakens confidence in every other number.

Good SaaS analytics is not a new dashboard. It is a reconciled metrics layer with one identity per customer, one rule per metric and one source for every report. These are six root causes SaaS analytics has to resolve, and the fix for each. Each fix lasts because it lives in the model, not in a spreadsheet. Each card below names one root cause and the fix we apply to it. Together they produce one trusted set of SaaS KPIs.

What this costs leadership

In a month when the numbers disagree, finance can spend days reconciling instead of analyzing. Board time goes to footnotes. Worst of all, decisions on hiring, pricing and spend are made on figures nobody fully trusts. Fixing the root causes once, in the model, ends that cycle. The cards below show how.

Why a dashboard alone does not fix it

A new dashboard on top of the same disconnected sources only displays the disagreement more clearly. The work has to happen underneath: matching customers, aligning periods and writing down the rules. Once that is done, every dashboard, board pack and model inherits the same answers automatically. The result is one set of numbers that every team, investor and director can rely on. That trust is what lets leadership act quickly and with confidence.

What it is

SaaS analytics measures how much recurring revenue you have, keep and pay to grow

SaaS analytics is the measurement and analysis of a subscription business: recurring revenue, customer acquisition, retention, expansion and unit economics. It combines data from billing, CRM, accounting and product systems to explain how the business grows and where it loses value.

One ARR bridge shows what you have, keep and pay to growARR bridge: opening ARR $8.0M, new +$1.6M, expansion +$0.9M, contraction -$0.5M, churn -$0.6M, closing ARR $9.4M; net revenue retention 97.5% (demo data).One ARR bridge shows what you have, keep and pay to growDemo dataARR bridge, last 12 months, $ millions$6M$7M$8M$9M$10M$11M$8.0MOpeningARR+1.6New+0.9Expansion-0.5Contraction-0.6Churn$9.4MClosingARRPay to growKeep: net revenue retention 97.5%Axis starts at $6M. Have = opening and closing ARR; keep = expansion, contraction and churn; pay to grow = new ARR.One ARR bridge shows what you have, keep and pay to growARR bridge: opening ARR $8.0M, new +$1.6M, expansion +$0.9M, contraction -$0.5M, churn -$0.6M, closing ARR $9.4M; net revenue retention 97.5% (demo data).One ARR bridge shows what youhave, keep and pay to growDemo dataARR bridge, last 12 months, $ millions$6M$8M$10MOpening ARR$8.0MNew+1.6Expansion+0.9Contraction-0.5Churn-0.6Closing ARR$9.4MNet revenue retention 97.5%Axis starts at $6M. Pay to grow = new ARR; keep =expansion, contraction and churn.

Good SaaS data analytics rests on three things. First, a reconciled customer and subscription model that matches billing, CRM and accounting, so every customer, contract and change in recurring revenue is counted once. Second, documented definitions for each SaaS KPI, agreed between finance and RevOps and applied the same way in every report. Third, dashboards and models that give the board, finance and growth teams the views they need.

SaaS analytics differs from product analytics, which focuses on how users behave inside the application. Product usage is an important input, but the core questions are commercial: how much recurring revenue the company has, how efficiently it acquires more and how much it keeps.

The result is a company that spends less time arguing about numbers and more time acting on them.

For a founder, that means fewer surprises in board meetings. For a CFO, it means an ARR figure built to stand up to due diligence. For RevOps, it means one pipeline and one forecast that finance accepts. Trust comes first. It starts with definitions. Finance and RevOps agree them once. The rest follows from them.

SaaS KPIs and metrics
Benefits

Reliable SaaS analytics moves leadership from reconciling numbers to deciding

Reliable SaaS analytics benefits every leader in a subscription business, but each one uses it differently. The common thread is trust. When ARR, retention and unit economics are defined once and reconciled to source, the conversation moves from whose number is right to what to do about it.

Trust also saves time. Finance stops rebuilding the same ARR bridge each month. RevOps stops reconciling CRM exports with invoices. Founders stop preparing separate versions of the numbers for investors, the board and the team. That time goes back into decisions: which segments to grow, which accounts to save and how fast to hire.

Reconciled metrics return about 45 finance hours a monthPaired bars, hours per month before vs after: board pack preparation 20 vs 6, ARR bridge rebuild 16 vs 2, CRM vs invoice reconciliation 12 vs 1, investor questions 8 vs 2; 56 hours become 11 (demo data).Reconciled metrics return about 45 finance hours a monthDemo dataHours per month by recurring task, before and after the metrics layerBeforeWith one reconciled metrics layerBoard pack preparation20 h6 hARR bridge rebuild16 h2 hCRM vs invoice reconciliation12 h1 hInvestor questions8 h2 h0510152056 hours a month become 11: about 45 hours back for decisionsReconciled metrics return about 45 finance hours a monthPaired bars, hours per month before vs after: board pack preparation 20 vs 6, ARR bridge rebuild 16 vs 2, CRM vs invoice reconciliation 12 vs 1, investor questions 8 vs 2; 56 hours become 11 (demo data).Reconciled metrics returnabout 45 finance hours amonthDemo dataBeforeWith one reconciled metrics layerBoard pack preparation20 h6 hARR bridge rebuild16 h2 hCRM vs invoice reconciliation12 h1 hInvestor questions8 h2 h56 hours become 11 each month

The benefits below are the outcomes our metrics layer is designed to deliver. Each one names the role, the decision it supports and how the data makes it possible. Each outcome can be tracked on a dashboard.

The value is cumulative. Once the SaaS analytics layer exists, every new question is cheaper to answer, because the data is already reconciled and defined. Leadership can then spend board time on strategy rather than on reconciling the numbers.

The decisions it supports

Which accounts to renew first. Which channel to fund. Which plan to reprice. How fast to hire against ARR growth. When to raise the next round. Each of these depends on the same reconciled numbers, and each card below shows which leader gains what.

What changes in practice

Monthly reporting takes less time, because the numbers are already reconciled. Board preparation takes less time, because the pack draws on the same model. Questions from investors are answered from the same model that runs the business. And when a metric moves, leaders can quickly see why, by drilling from the headline figure to the segment, plan or account that caused it. That speed is the real return on SaaS analytics: decisions made while they still matter. Each benefit compounds as the metrics layer matures, because every new dashboard reuses the same governed definitions. The cards below show who gains what, and which recurring decision each benefit supports most directly. Together they turn SaaS analytics into a shared decision tool for the whole leadership team.

Fundraising answers ready

Founders see growth efficiency, retention and runway from the same data used to run the company, ready for investor questions.

An ARR bridge the CFO can defend

Reconciled ARR, consistent board reporting and a SaaS financial model tied to actuals, ready for due diligence.

Forecasts and commissions that agree

RevOps uses pipeline, bookings and expansion data that matches finance, so quotas and commissions are not disputed.

At-risk accounts flagged early

Usage signals linked to renewal outcomes show customer success which accounts to contact first each week.

Trends the board can trust

The same KPI definitions every quarter, with drill-down to segment and customer when directors ask why.

Hiring paced to the numbers

Leadership links headcount plans to ARR growth and burn, and sees the effect before committing.

KPIs we track

Six SaaS KPIs, defined once, explain growth and retention

These are the core SaaS KPIs we define and track in the SaaS analytics layer. They are the measures boards, investors and finance teams commonly ask for, and each one depends on reconciled data to be meaningful.

GoalARR growth
  1. New ARR
    • CAC payback
    • LTV:CAC
    • Burn multiple
  2. Expansion
    • Net revenue retention (NRR)
    • ARR bridge: expansion
  3. ContractionReview first
    • Gross revenue retention (GRR)
    • ARR bridge: contraction
  4. Churn
    • Logo churn
    • Revenue churn
Goal, drivers, metrics. The highlighted driver is the one to review first.

Know exactly how much recurring revenue you have

Demo

ARR and MRR: contracted recurring revenue, excluding one-time fees, implementation and services. ARR = MRR x 12. We report it with a monthly ARR bridge showing new, expansion, contraction and churned ARR, so every movement in the headline number has an explanation finance can defend in a board meeting or due diligence.

See whether existing customers grow on their own

Demo

Net revenue retention (NRR): (starting recurring revenue + expansion - contraction - churn) / starting recurring revenue, for each twelve-month cohort. It shows how much the installed base grows without new logos, and it is often the first SaaS KPI investors ask about when judging the quality of growth.

See exactly how much revenue you lose

Demo

Gross revenue retention (GRR): (starting recurring revenue - contraction - churn) / starting recurring revenue. It excludes expansion and is capped at 100 percent. GRR isolates revenue lost from existing customers, so strong upsell cannot hide a retention problem in a particular plan or segment.

Spot when you are losing many small customers

Demo

Logo churn: customers lost in a period divided by customers at the start. Tracked by segment and plan, it shows where onboarding or product fit is failing, even when the revenue impact looks small at first.

Spot when one large loss hurts more than many small ones

Demo

Revenue churn: recurring revenue lost from cancellations in a period divided by starting recurring revenue. Read alongside logo churn, it tells you whether the risk sits in a few large accounts or across the customer base, and which customer success motion fits.

Know how fast acquisition spend pays back

Demo

CAC payback: customer acquisition cost divided by (new MRR per customer x gross margin), expressed in months. CAC is total sales and marketing cost divided by new customers. Tracked by channel and segment, it shows where each acquisition dollar returns fastest and where budget should move next quarter.

Spend where customers are worth the most

Demo

LTV:CAC: customer lifetime value divided by acquisition cost. LTV = average recurring revenue per customer x gross margin / customer churn rate. Compared by channel and segment, it shows which customers create the most value for what they cost to win, and guides targeting and pricing.

Know how efficiently cash turns into growth

Demo

Burn multiple: net cash burn divided by net new ARR in the same period. It links the SaaS financial model to the metrics layer and shows how much cash each dollar of new ARR costs, which helps the CFO and board pace hiring and spend.

Why definitions come first

Exact formulas vary between companies. Questions such as whether to include usage-based revenue in ARR, how to treat multi-year contracts, or whether to measure retention by cohort or by period all change the result. Two teams can both be right and still report different numbers. We agree each definition with your finance and RevOps teams, document it in a shared KPI glossary and apply it identically in every dashboard, board report and financial model.

Edge cases we settle up front

Upgrades and downgrades mid-term. Credits and refunds. Paused subscriptions. Free trials that convert. Multi-year prepayments. Price increases at renewal. Currency changes. Reseller and indirect deals. Each edge case gets one written rule, so the same contract always produces the same ARR.

How the KPIs connect

The SaaS KPIs below form a KPI tree. ARR growth sits at the top. Beneath it are new ARR, expansion, contraction and churn. Beneath those sit the efficiency measures: CAC payback, LTV:CAC and the burn multiple. Reading the tree from top to bottom shows which lever moved the headline number, and therefore where leadership should act.

Cuts that turn a scorecard into a decision tool

Every KPI can be broken down by segment, plan, region, sales channel, cohort and account owner. Leadership sees not only the headline figure but where it comes from. That is what turns the KPI table into decisions: renew these accounts first, shift budget to this channel, fix contraction in this plan.

SaaS analytics across B2B SaaS pricing models

B2B SaaS companies often mix seat-based, tiered and usage-based pricing, and SaaS analytics has to handle each model without breaking the KPI series. Seat-based plans are counted on contracted seats. Usage-based revenue enters ARR only when it is committed, and is tracked separately when it varies. Monthly recurring revenue (MRR) is reported for monthly and annual contracts alike, so MRR trends and the ARR bridge tell the same story. Hybrid plans get a written rule for each component. That keeps data-driven decisions on pricing and packaging grounded in figures finance has already reconciled.

Targets, not borrowed benchmarks

We do not quote generic industry benchmarks as targets. Your board sets targets for your stage and model, and the dashboards track progress against them over time.

How each KPI is presented

Every KPI appears with its definition, its formula, its trend over the last eight quarters and the drivers beneath it. Charts carry insight titles, such as 'Expansion offset most of this quarter's churn', so readers know the takeaway before reading the axis. Definitions link to the shared glossary, so anyone can check how a number was calculated.

Reconciliation behind every number

ARR ties to contracted billing data. Recognized revenue ties to the general ledger. Customer counts tie to the CRM after deduplication. Each tie-out is checked on every refresh, and exceptions are flagged to finance before the numbers reach a dashboard. That is what makes the KPIs safe to show a board or an investor.

Using the KPIs to decide

The KPI set supports specific recurring decisions. Customer success uses GRR and churn by plan to decide which accounts to contact first. Marketing uses CAC payback by channel to decide where to move budget. The CFO uses NRR, burn multiple and runway to pace hiring. The board uses the full set to judge whether growth is efficient and durable.

Keeping definitions stable

When the business changes, for example with a new pricing model or a usage-based plan, definitions are updated through a controlled change: the glossary is revised, historical figures are restated where needed, and the change is noted on every affected dashboard. That keeps trends comparable over time and avoids silent breaks in the series that would confuse the board.

What the board sees

A one-page summary leads the board pack: ARR and its bridge, NRR and GRR, churn, CAC payback, burn multiple and runway, each with a short insight title and a comparison with target. Supporting pages follow, with segment and cohort detail for directors who want to drill in. Finance writes one paragraph of commentary per KPI, drawn from the same model, so the narrative and the numbers stay aligned.

What the operating teams see

Growth, finance and customer success teams see the same KPIs at the level of detail they act on: channels, plans, regions and individual accounts. Each team view keeps the board definitions, so a figure discussed on Monday in an operating review matches the figure the board sees at quarter end.

Where to start

A sound starting point is ARR, NRR, GRR and churn, because every other SaaS KPI depends on them being right. Once those four reconcile to billing and the ledger, CAC payback, LTV:CAC and the burn multiple can be added with confidence, using the same customer model and the same definitions.

churn analysis
Sample dashboards

SaaS analytics dashboards give the board, finance and growth teams one set of numbers

We show each SaaS dashboard on demo data, so you can review the structure and KPIs before connecting your own systems.

SaaS dashboards on one metrics layer

Demo data
ARR$14.2M▲ +13% QoQ
Net retention104%▼ -5 pts QoQ
Gross retention91%▼ -1 pt QoQ
Runway22 months▼ -2 months

ARR growth holding above 7% a quarter

ARR at quarter end, $M

$0M$5M$10M$15MQ4 LYQ1Q2Q3$14.2M in Q3: Strongest quarter of net new ARR$0M$5M$10M$15MQ4 LYQ1Q2Q3$14.2M in Q3: Strongest quarter of net newARR

So what: New ARR is carrying growth; fix SMB contraction before NRR falls further.

KPIQ1Q2Q3
ARR$11.7M$12.6M$14.2M
NRR108%109%104%
GRR91%92%91%
Burn multiple1.6x1.4x1.2x
View 1

Board meetings spent on decisions

Board dashboard: ARR, NRR, GRR, burn and runway with quarterly trends.

View 2

Every ARR movement explained

Finance dashboard: ARR bridge, recognized revenue, cohort retention and unit economics.

View 3

Budget moved to the best channels

Growth dashboard: pipeline, conversion, CAC payback by channel and expansion.

View 4

Customer success sees risk first

Retention dashboard: account health, usage trends and upcoming renewals, ranked by revenue at risk.

Each dashboard draws on the same SaaS analytics model, so figures match across audiences. The board, finance and growth teams see different levels of detail, but never different numbers. Every chart title states its insight, so readers know what to look at first. Each view answers one question: are we growing efficiently, and where should we act next? The board, finance and growth views are tabs of one gallery.

Use the gallery to decide which SaaS dashboard your board, finance team and growth team need first.

executive dashboardsBook a consultation
Services

SaaS analytics services build the metrics layer once and reuse it

Our SaaS analytics services can be delivered as a defined project or as ongoing support. The metrics layer comes first, because every other service depends on it. Dashboards and reporting are then added on top, each reusing the same governed definitions. Each card names the outcome, not just the service, and each service builds on the last.

One metrics layer feeds every SaaS analytics serviceBilling, CRM, accounting and product data flow into one metrics layer, which feeds dashboards, churn analysis, board reporting, ongoing support and analyst capacity.SourcesBuilt onceReused by every serviceBillingCRMAccountingProduct usageMetricslayerOne definitionper KPIBoard, finance and growth dashboardsChurn and retention by cohortInvestor and board reportingOngoing support from a data teamFractional analyst capacityEach new service reuses the same definitions, so it costs less than the lastOne metrics layer feeds every SaaS analytics serviceBilling, CRM, accounting and product data flow into one metrics layer, which feeds dashboards, churn analysis, board reporting, ongoing support and analyst capacity.SourcesBillingCRMAccountingProduct usageMetrics layerOne definition per KPIBuilt once, reused by every serviceBoard, finance and growth dashboardsChurn and retention by cohortInvestor and board reportingOngoing support from a data teamFractional analyst capacityEach new service reuses the samedefinitions, so it costs less than the last

One reconciled source of SaaS KPIs

Metrics layer: data model and definitions across billing, CRM, accounting and product.

Dashboards for every audience

Board, finance and growth dashboards in Power BI or your current tool.

Board packs without the scramble

Investor and board reporting from governed data.

A layer that grows with you

Ongoing support from a fractional data team.

churn analysisanalytics as a service
What you get

A SaaS financial model tied to actuals keeps the board pack and plan aligned

A SaaS financial model is only as reliable as the actuals behind it. Many models start from a spreadsheet export that is out of date the moment it is pasted in. We connect the model directly to the SaaS analytics layer, so forecasts start from reconciled ARR, retention and cost data and update as new actuals arrive.

The model covers new, expansion and churned ARR, headcount and operating costs, gross margin, cash burn and runway, with scenarios for growth, pricing and hiring plans. Assumptions are visible and adjustable by finance.

A model fed by actuals shows runway for each scenarioBefore and after: a stale spreadsheet export beside a financial model linked to the metrics layer, showing runway of 16, 22 and 24-plus months by scenario (demo data).Before: static spreadsheetAfter: model fed by actualsDemo dataARRNewExpansionChurnBurnRunwayExported Mar 314.214.915.30.90.80.90.40.50.4-0.3-0.4-0.50.60.60.7232221Out of dateLinked tometrics layerCash balance06121824MonthsPricing change: 24+ monthsBase plan: 22 monthsHiring plan: 16 monthsA model fed by actuals shows runway for each scenarioBefore and after: a stale spreadsheet export beside a financial model linked to the metrics layer, showing runway of 16, 22 and 24-plus months by scenario (demo data).Before: static spreadsheetDemo dataExported Mar 3ARR14.214.915.3New0.90.80.9Expansion0.40.50.4Churn-0.3-0.4-0.5Burn0.60.60.7Runway232221Out of dateLinked to metrics layerAfter: model fed by actualsCash balance06121824MonthsPricing change: 24+ monthsBase plan: 22 monthsHiring plan: 16 months

Board reporting uses the same definitions and the same data, so the monthly pack and the SaaS financial model stay consistent.

That consistency changes the board conversation. Instead of reconciling figures, directors can test decisions: what happens to runway if hiring slows, or to NRR if a pricing change reduces contraction. Finance answers from the model in the meeting, using the same data the board saw in the pack, without a follow-up request.

strategic planning advisory
Platforms we use

SaaS analytics runs on the CRM, billing and reporting platforms you already have

Our SaaS analytics work is tool-agnostic. We connect the systems you already use and recommend changes only when they solve a real problem, such as a missing customer identifier or a billing setup that cannot support the metrics you need.

CRM, billing, accounting and product data meet in one warehouseData flow from CRM, billing, accounting and product usage into a warehouse, then Power BI dashboards, then the renewal decision.CRMBillingAccountingProduct usageWarehousePower BIRenewaldecisionAt-risk accounts firstCRM, billing, accounting and product data meet in one warehouseData flow from CRM, billing, accounting and product usage into a warehouse, then Power BI dashboards, then the renewal decision.CRMBillingAccountingProduct usageWarehousePower BIRenewal decisionAt-risk accounts first
One account record across toolsCRM: HubSpot and Salesforce aligned with billing for pipeline, bookings, renewals and customer success data.
Revenue tied to the ledgerBilling and accounting: invoices, credits and revenue data from your billing system and NetSuite, QuickBooks or Zoho, reconciled to the general ledger.
Usage linked to revenueProduct usage events and GA4 data joined to accounts, so activation and engagement can be tied to renewals.
Alerts that act on dataWarehouse pipelines plus Zapier, Make or n8n for workflows such as at-risk account alerts and CRM hygiene.
Self-service for business usersPower BI for board, finance and growth dashboards, with row-level security for each team.
Operational data in reachAirtable and other operational tools connected where onboarding or implementation data lives.

The data a SaaS company needs is usually spread across five types of system. SaaS analytics brings them together in a cloud data warehouse or a governed Power BI model, depending on volume and complexity.

We document every connection, refresh schedule and transformation, so your team can maintain the stack after handover or extend it with our support. Nothing here requires a new platform.

The principle is simple: keep the systems that work, fix the ones that break your metrics, and connect everything to one model. That keeps costs down and avoids a disruptive migration. Where a system cannot provide what the metrics need, we explain the gap, the options to close it and the trade-offs, so you can decide with full information. Every connection is documented and tested.

What stays, what changes

The CRM, billing and accounting tools can usually stay. What changes is how they connect: one customer identity, one model and documented refreshes. That design is what ends metric disputes. Each platform connection is listed in the documentation with its owner, refresh schedule and data quality checks, so nothing depends on memory. Changes to any source are reviewed before they reach the model. Documentation is handed over in full.

HubSpotSalesforce
How we work

SaaS analytics reconciles first and visualizes last, in six steps

We build the SaaS metrics layer in six steps. The sequence matters. Definitions and reconciliation come before dashboards, because a dashboard built on unreconciled data simply moves the disagreement to a new screen.

Reconcile before you visualize: six steps

Select a stage, use Next or the arrow keys

Audit. Every disagreement explained. Review billing, CRM, accounting and product systems and document exactly where and why numbers disagree, before any build is proposed.

Step 1 of 6

Each step has a clear output that your finance and RevOps teams review before we move on. A focused first release, typically ARR, retention and the board dashboard, comes before the full metric set, so leadership sees value early while the remaining KPIs are added.

The audit and definition steps involve the most conversation. That is deliberate. Metric disputes are often disagreements about rules, not data, and they are cheaper to settle in a working session than in a board meeting. The audit is advisory work and acts as the gate: we propose the build only once definitions are signed off and the team is ready to use them; if not, we continue with advisory or training. Once rules are agreed and documented, integration and reconciliation become engineering tasks with a clear finish line.

After launch, the layer needs light maintenance as plans, prices and systems change. You can run it internally or keep us on to maintain it. Each step has a clear owner on your side. Nothing goes live until finance has reconciled it.

What you see at each step

A written audit, a signed-off glossary, a reconciled model, then dashboards.

Who is involved

On your side, a finance lead and a RevOps lead join the audit and definition sessions, and an engineer or system admin provides access to the source systems. On our side, a lead consultant runs the definition sessions, and the same delivery team builds the model and dashboards. Each step ends with a short written sign-off, so decisions are recorded and the project never loses track of what was agreed. That record also becomes the documentation your team uses after handover.

What the first release includes

The first release usually covers ARR and its bridge, net and gross revenue retention, churn and the board dashboard. It is designed so the next board meeting can run on reconciled numbers. Later releases add CAC payback, LTV:CAC, burn multiple, cohort views and the growth dashboard, each built on the same model. Each later release is smaller and faster, because the foundations are already reconciled and the definitions are already signed off.

data modernization projects

A SaaS metrics audit shows exactly why your numbers disagree

If your ARR, churn or CAC figures change depending on who runs the report, start with a SaaS metrics audit. We review how your billing, CRM, accounting and product data fit together, compare the definitions each team uses and identify the specific reasons your SaaS KPIs disagree. The audit is scoped in advance and involves a small number of working sessions with finance and RevOps.

You receive a written summary of the gaps, a proposed set of KPI definitions ready for your finance team to review, and a phased SaaS analytics plan for the metrics layer, the SaaS dashboard set and board reporting. The plan shows which systems need to connect, which data issues must be fixed at the source and what a first release would include.

If you prefer a broader view first, our readiness assessment covers systems, data quality, skills and governance across the business. Either way, you leave with a clear first step and a written plan your board can review. Book a consultation to start. The audit is useful on its own, with no obligation, and if a build would be premature the plan says so. We aim to reply within one business day.

AI and data readiness assessment
See how we work
Frequently asked questions

SaaS analytics: questions founders and CFOs ask before fixing their metrics

These are common questions founders, CFOs and RevOps leaders ask about SaaS analytics. They cover the difference from product analytics, tooling, revenue recognition, the SaaS financial model, board KPIs and timelines.

Each answer is written for a decision maker, not a data engineer, so you can share it with your leadership team. Each answer stands alone and reflects how we work with SaaS finance and RevOps teams. If your question is not here, raise it in a consultation.

No. Product analytics focuses on how users interact with the product. SaaS analytics covers the whole subscription business: recurring revenue, retention, acquisition cost and unit economics. Product usage is one input, but the core of SaaS analytics is reconciled financial and customer data that finance and the board can rely on.

Usually not. A SaaS company usually already has the data it needs in billing, CRM and accounting systems. The gap is reconciliation and consistent definitions. We build a metrics layer on your existing stack and use Power BI or your current reporting tool for the dashboards.

We model bookings, billings and recognized revenue separately and reconcile each to its source. ARR is based on contracted recurring value, while recognized revenue ties to the general ledger. Dashboards show both, clearly labeled, so finance and sales can see exactly why the figures differ.

Yes. We build driver-based SaaS financial models connected to actual ARR, retention and cost data from the SaaS analytics layer. The model supports budgeting, scenario planning, fundraising preparation and board discussions, and it updates automatically as new actuals arrive each month.

A typical board set covers ARR and growth, net and gross revenue retention, churn, CAC payback, gross margin, burn and runway. The right set depends on your stage, model and strategy. We agree the list with your leadership and keep the definitions consistent from quarter to quarter.

It depends on the number of systems and the state of the data. We start with an audit, then deliver a focused first release covering ARR, retention and the board dashboard before adding the rest. The audit sets out the phasing, so you know the sequence before committing to a build.