Michel B.

I fix your conversion tracking and attribution when you don't trust your data and therefore can't scale your Google Ads, Facebook Ads or TikTok Ads. I'm a conversion tracking & attribution expert with 17+ years of experience and an official Stape io server-side tracking partner.

With Tracking Architects, a collective of server-side tracking and attribution experts, I help ecommerce and lead generation companies trust their numbers in Meta and Google Ads, and decide where to spend.

My approach look typically like this:

1: Audit the ad account, pixel, events manager and tracking setup
2: Share the report based on impact- value, decide on the fixes
3: Fix the issue, build the server-side architecture if needed
4: Monitor and optimize, make sure better signals improves CPA's

What's usually broken

When I audit a new account, I almost always find the same three problems:

1. Signal quality: Meta and Google aren't receiving accurate purchase signals, so the algorithm can't find your best buyers

2: Attribution gaps: conversions are missed or double-counted, making platform data untrustworthy

3: No single source of truth. Shopify, Meta, and Google all tell a different story, and nobody knows which one to believe

Once those are fixed, most clients see 20-30% more tracked conversions on Meta and 17–26% uplift in Google Ads. Not because ad performance magically improved, because the data was wrong to begin with.

What accurate tracking actually unlocks

Clean data isn't the end goal, it's the starting point. Once your sources agree, you can start asking the questions that actually move the business:

• Which campaigns are profitable after COGS and shipping?
• Which customer segments have a CAC worth paying?
• Where should the next €10–50k in ad spend actually go?

That's the layer most brands never reach because they're still arguing about which number is right.
We help you get there.

Who I work with

Ecommerce, service and leadgen brands spending serious budget on Meta Ads and Google Ads, who need to trust their numbers before they scale. Especially if: