Tracking Automation at Scale

Context

Understanding where clicks come from and what happens after someone lands is essential for optimising paid media. A tracking standard existed, but implementation sat with local markets and their agencies, and consistency kept breaking down. Across 25+ markets, hundreds of ad accounts and thousands of Google Ads campaigns, incorrect or missing tracking meant data loss, incorrect attribution, and a constant cycle of chasing markets to fix it.

Solution

I built a fully automated solution that removed the need for local teams to implement tracking by hand. Using the Google Ads API combined with predefined rules, the system generated and applied the correct tracking code automatically at campaign level, with channel, buying model and campaign name pulled directly from Google Ads.

It ran on an 8-hour cycle, picking up new or changed campaigns and updating tracking code without manual input, and without touching the underlying structure IT had already defined, which kept the rollout fast.

Results

100% tracking accuracy across all Google Ads campaigns and channels, with errors reduced to near zero. It freed up time for both local teams and the global team, who no longer had to chase implementation issues market by market.

My role

I led the project as paid search lead on the global team, extending it to cover other Google Ads formats as one complete solution. I set the strategic framework with my agency partner, who supported execution, and ran onboarding and training for local markets and their agencies.

My take

This is one of the clearest examples I have of automation done right, especially inside a global structure where you don't have direct authority over local teams. It worked because it removed real day-to-day friction for them, not because it was mandated. I built this before AI tools were widely used; automation is easier with AI today, but the planning and judgement behind it still matter more than the tool.