Don't Make Marketing Decisions on Data You Can't Trust
Bad analytics data does not fail loudly — it fails silently. You keep making decisions, running campaigns, and allocating budget based on numbers that are quietly wrong. We validate every layer of your tracking so you can act with confidence.
Signs Your Analytics Data Cannot Be Trusted
Numbers do not match
GA4, your CRM, and Ads Manager all show different conversion counts for the same period.
Tags firing when they should not
Events trigger on wrong pages, wrong actions, or multiple times for a single user action.
Missing data you cannot explain
Certain events, pages, or user segments simply do not appear in your reports.
No systematic testing process
Every code release could break tracking and you would not know until someone notices the numbers look wrong.
What Is Analytics QA & Data Validation?
Tag Debugging
Verify every tag fires correctly on the right trigger with the right parameters.
Cross-Platform Comparison
Compare data volumes across GA4, ad platforms, CRM, and your data warehouse.
Funnel Validation
Confirm every conversion funnel step is tracked accurately end-to-end.
Regression Testing
Build a framework that catches tracking breakage after every code release.
What's Included
Full Tag Audit Report
Documentation of every tag, its status, what it is doing correctly, and what needs to be fixed.
Debugging & Issue Resolution
All identified tracking issues fixed and validated — not just documented.
Cross-Platform Data Comparison
Side-by-side analysis of data volumes across GA4, Meta, Google Ads, CRM, and other platforms.
Funnel Validation Report
Step-by-step confirmation that every funnel stage is tracked accurately from first touch to conversion.
Data Discrepancy Explanations
Clear explanations for every number mismatch — what causes it, whether it is expected, and how to resolve it.
Regression Testing Checklist
A structured testing process your team runs after every release to catch tracking breakage immediately.
Absolute Trust, Not Just Audits
We dig deep to find silent tracking failures and build automated frameworks so you can trust your data again.
98%+ Accuracy Target
We do not stop until your data matches your source of truth.
Root Cause Analysis
We do not just report errors; we find exactly why they happen.
Cross-Platform Checks
Validating data consistency between GA4, CRM, and Ad platforms.
Automated Testing
Setting up regression tests to catch future tracking breaks.
Clear Reporting
Plain English explanations of what broke and how we fixed it.
Proactive QA
Catching issues before they impact your marketing spend.
How We Validate Your Analytics
Implementation Audit
We review every tag, trigger, and variable in your tag management system and compare against your measurement specification.
Live Debugging
We use browser developer tools, GTM Preview, and platform debuggers to watch events fire in real time and identify issues.
Cross-Platform Comparison
We compare data volumes and conversion counts across all connected platforms to identify discrepancies and their root causes.
Testing Framework
We deliver a regression testing checklist or automated test suite that your team can run after every code or tag release.
Implementation Audit
We review every tag, trigger, and variable in your tag management system and compare against your measurement specification.
Live Debugging
We use browser developer tools, GTM Preview, and platform debuggers to watch events fire in real time and identify issues.
Cross-Platform Comparison
We compare data volumes and conversion counts across all connected platforms to identify discrepancies and their root causes.
Testing Framework
We deliver a regression testing checklist or automated test suite that your team can run after every code or tag release.
Platforms We Validate
Frequently Asked Questions
Why does my GA4 data not match my CRM data?
GA4 and CRM data diverge for several reasons: GA4 counts sessions while CRM counts contacts; GA4 uses last-click attribution while your CRM may record the first touch; and GA4 may not track offline or phone conversions. Some discrepancy is expected, but large differences usually indicate a tracking configuration error that needs to be investigated.
How do I know if my tracking tags are actually firing?
You can check tag firing using GTM Preview mode, which shows all tags, triggers, and data layer events for each page interaction in real time. For individual pixels, browser developer tools and platform-specific debuggers (Meta Pixel Helper, GA4 DebugView) show exactly what is firing and what data is being sent.
What causes data discrepancies between analytics platforms?
Common causes include: different attribution models (last click vs data-driven), different session definitions, bot filtering settings, ad blocker impact varying by platform, time zone differences, and deduplication logic differences. We identify which cause applies in each case.
How often should we run analytics QA?
At minimum, run a QA check after every significant code release, major tag change, or platform update. We recommend a lightweight regression test after every deployment and a comprehensive full audit quarterly.
Can you fix issues you find during QA, or do you only report them?
We fix everything we find. Our QA engagement includes full remediation of identified issues, not just a list of problems to hand back to your team.
Make Sure Your Analytics Data Is Actually Correct
Book a free audit and find out exactly where your tracking is broken, why your numbers do not match, and how to fix it.