Marketing measurement & analytics
Know what the numbers actually mean.
A measurement layer that reconciles ad platforms, analytics and CRM revenue, so your team knows why the numbers disagree and which one to use for each decision.
Problems it solves
Why do your tools report different answers?
Each platform measures with its own rules and its own incentives. Without a layer that reconciles them against CRM outcomes, reporting turns into an argument about whose number is right.
Platforms disagree
Meta, Google Ads, GA4 and the CRM report different conversions for the same period.
Reporting disconnected from revenue
Dashboards count leads, not qualified leads, sales or revenue.
KPIs without definitions
The same metric name means different things to different teams.
Manual, late reporting
Numbers arrive after the decision they were meant to inform.
Deliverables
Measurement architecture
Which metrics exist, where they come from and which decisions they serve.
Cross-platform reconciliation
A documented explanation of why platforms, analytics and CRM differ.
CRM revenue connection
Qualified leads, pipeline and revenue joined to acquisition data.
Dashboards
Looker Studio or Power BI reporting built on reconciled data.
Analysis
SQL or Python analysis of funnels, cohorts and customer value where data permits.
Process
Map decisions
Start from the decisions reporting must support.
Define metrics
Write definitions, sources and owners for each metric.
Reconcile
Join platform, analytics and CRM data and explain the gaps.
Report
Build dashboards and automate the refresh.
Tools and systems
- GA4
- Looker Studio
- Power BI
- SQL
- Python
- PostgreSQL
- CRM platforms
Questions
Which number is the right one?
Usually none of them on its own. Each tool answers a slightly different question. The measurement layer documents what each number means and which one to use for each decision.
Do we need a data warehouse?
Not always. Smaller setups can reconcile data in a lighter way. A warehouse becomes useful when several sources, markets or long histories need to be joined reliably.
Can you work with our existing dashboards?
Yes. Existing reporting is reviewed first; it is rebuilt only where the underlying data or definitions cannot support it.