Interactive Tool
Revenue Data Diagnostic
Check every statement that's true for your organization. Your score reveals where you sit on the GTM Data Maturity Model.
0
Stage 1 of 25
A. Data Infrastructure
Where your data lives, how it flows, and whether your systems talk to each other.
0 / 5
All customer and account data is connected through shared keys or IDs across systems
Your CRM and accounting/ERP system are automatically integrated
There's a documented, agreed-upon source of truth for revenue
Data flows between systems automatically — no manual CSV exports or copy-paste
Data refreshes run automatically and complete in minutes, not hours
$30M Marketplace: Data pipelines were taking 24+ hours to run across 3,000+ databases. After rebuilding, same aggregation completed in under 1 hour at lower compute cost.
B. Metrics & Definitions
Whether your teams agree on what the numbers mean.
0 / 5
Revenue in your CRM matches revenue in your accounting system
Sales and Marketing report the same pipeline and conversion numbers
Key terms like customer, MRR, churn, and NRR all have shared definitions across all teams
Board deck metrics are generated from a single source, not assembled manually
Month-end revenue reconciliation is automated or trivial
$20M SaaS: Salesforce disagreed with Zuora disagreed with QuickBooks. After integrating all systems, a 2-day weekly Google Sheets process became a single live KPI dashboard.
C. Attribution & ROI
Whether you can trace revenue back to the activities that created it.
0 / 5
You have multi-touch attribution across marketing, sales, and partner channels
Each deal has clear, non-conflicting attribution to the source that created it
You can pull true LTV/CAC by channel same-day
Channel ROI is measured by downstream revenue, not just leads or bookings
Marketing spend is optimized on actual customer LTV, not publisher-reported ROAS
$400M Fintech: Attribution had been broken for over a year — four teams claiming the same deals. A consolidated model resolved it in weeks. $30M Marketplace: Redistributed $4M event budget using downstream conversion metrics.
D. Analysis & Segmentation
Whether you can cut data to find what's actually driving or killing growth.
0 / 5
You can segment your P&L by marketing channel, sales channel, and product line
Pricing decisions are informed by customer behavior and retention data
You can generate targeted upsell and expansion lists based on usage and segmentation
Product utilization data is connected to churn, retention, and expansion metrics
Sales reps can see downstream success metrics for their pipeline deals
$150M E-commerce: Unifying Shopify, analytics, and warehouse data revealed a 5% yield-per-visitor improvement and 10% profit lift from pricing optimization. $30M SaaS+Services: Connected NetSuite with HubSpot to see product attach rates and retention by line — then simplified pricing around what actually worked.
E. Forecasting & Automation
Whether your data predicts what's next — or just explains what already happened.
0 / 5
Reports are pre-built and always up to date, no weekly or monthly rebuilds
You can model "what happens to revenue if we change pricing, territory, or comp?"
Lead scoring is based on actual conversion data, not just rules or gut feel
You can identify at-risk customers before they tell you they're leaving
GTM planning is driven by bottom-up data models, not just top-down targets
$40M SaaS: Mapped product utilization against churn and retention to build a predictive model — proactive intervention based on actual usage signals instead of waiting for cancellation requests.
Your GTM Data Maturity
Stage 1
Spreadsheets & Tribal Knowledge
Start checking boxes above to see your maturity stage.
0 – 4
Stage 1: Spreadsheets & Tribal Knowledge
No source of truth. Decisions rely on manually assembled data that different people calculate differently.
5 – 9
Stage 2: Disconnected Systems
CRM, billing, and analytics tools exist — but they don't talk to each other. Reports tell different stories.
10 – 14
Stage 3: Canonicalized Data
Shared definitions and partial integration exist, but analysis is still manual and segmentation is limited.
15 – 19
Stage 4: Automated Analytics
Pre-built views, cohorts, and funnels always up to date. The gap is predictive capability and scenario modeling.
20 – 25
Stage 5: Predictive Intelligence
Forecast scenarios, leading indicators, and proactive GTM decisions driven by data. The target state.
Most companies we work with start at Stage 1–2 and reach Stage 3–4 within the first engagement. The unlock isn't a new tool — it's connecting what you already have.
Want to move up a stage?
Share your score and we'll send a relevant case study from a company at your stage — or book 15 minutes and we'll walk through exactly where the breaks are in your stack.