Reporting on broken tracking is not reporting. It is fiction with numbers attached. We verify the data first - then build the architecture that connects every click to revenue.
GA4 implementation, attribution modeling, tracking audits, and CRO infrastructure - built in the right order, on verified data.
Analytics infrastructure degrades silently. Campaigns end, tracking setups stay. Events change, Key Events do not. The dashboards keep showing numbers. The numbers stop being accurate. Nobody sees it until someone opens the containers.
Tags added for campaigns that ended two years ago. Conversion Actions that were never cleaned up. Key Events that fire on events the site stopped sending. Nobody sees it in a report - it only shows up when someone opens the container.
If your primary conversion action was imported from GA4 and that GA4 event is misconfigured, Smart Bidding is learning from noise. The campaign looks like it is running. The data looks stable. The signal does not exist.
They are not. A match rate of 50-70% means attribution is leaking somewhere between the site, GA4, and Ads. You are making spend decisions on half the data. That is not a B2B characteristic - it is a tracking problem.
It is fiction with numbers attached. Dashboards that look stable while the underlying data is wrong are worse than no dashboards - they create false confidence in decisions that should not be made.
GTM container review, GA4 Key Event validation, Ads conversion import verification, and Ads-to-GA4 match rate analysis. We verify the data before we build on it - every time, without exception.
Clean event taxonomy, correct conversion setup, and a measurement plan that maps to your business objectives - not just pageviews and sessions.
Multi-touch attribution setup, channel contribution analysis, and spend decision frameworks grounded in verified data.
GA4-connected dashboards that show revenue by channel, campaign, and keyword - not just traffic. Decision-making data in one view, in a format that gets used.
Recurring audit of dead events, match rate drift, and broken conversion imports. The system stays honest as the work compounds.
A/B test setup, hypothesis framework, and statistical significance tracking - so you know what is actually moving conversion rates.
GTM container review, GA4 Key Event validation, Ads conversion import check, match rate analysis. One to two days. We document what is there before we change anything.
We confirm which data is reliable and which is not. We do not build dashboards on data we have not verified. This step determines what can be trusted and what needs to be fixed first.
Prioritised fixes: dead events removed, conversion actions corrected, GTM container cleaned. We document every change with the reason and the expected outcome.
GA4 implementation aligned to business objectives. Attribution model selected and configured. Reporting structure designed around decisions that need to be made, not metrics that look impressive.
GA4-connected dashboards showing revenue by channel, campaign, and keyword. Built after the data is verified - not before.
Recurring audit of match rate drift, dead events, and broken imports. The system stays accurate as campaigns change and the site evolves.
GTM containers, Conversion Actions, and Key Events accumulate dead layers over time. Nobody cleans them unless someone is specifically responsible for it.
Attribution is leaking somewhere between the site, GA4, and Ads. You cannot see it in a report. You only see it when somebody opens the containers.
If you are building a reporting system on top of data, verify the data is accurate before you automate decisions from it. Not the other way around.
One to two days to verify what is actually being tracked, what is broken, and what decisions are being made on data that cannot be trusted. Then we fix the foundation and build on it.
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