Modern marketing teams want full-funnel attribution, predictive insights, and AI-powered recommendations, but most organizations are unknowingly building these ambitions on messy, inconsistent, and incomplete data. In this session, we'll cut through that chaos and show you how to build a clean, AI-ready marketing data engine that works from impression to revenue.
We'll start by breaking down how to design a marketing campaign taxonomy that keeps your channels, objectives, funnel stages, and tactics organized. You'll see real examples of how a strong taxonomy simplifies reporting, improves optimization, and strengthens the outputs of models like MMM, which rely heavily on stable, well-structured inputs.
Next, we'll demystify UTM setup, the often-overlooked system that maintains mid-journey attribution. You'll walk away with a simple, durable UTM framework that prevents data loss, eliminates manual guesswork, and cleanly ties paid media, GA4, and CRM data together.
Then we'll shift to the heart of full-circle attribution: the CRM. You'll learn exactly which media and GA4 data fields should flow into your CRM to enable first-touch, last-touch, and multi-touch attribution, all in a way that is scalable, reliable, and AI-ready.
From there, we'll connect the dots across your MarTech stack by showing you how to standardize media, GA4, and CRM data into a single source of truth. This includes data structures, naming conventions, and governance steps that unlock accurate dashboards, stronger MMM models, and trustworthy AI insights.
Finally, we'll explore the power of connecting GA4 directly to BigQuery through Google Cloud, unlocking raw event-level data, user paths, custom dimensions, and advanced segmentation capabilities that GA4's UI can't provide.
If you want attribution that actually works, and AI models you can trust, this is your roadmap.
Modern marketing teams want full-funnel attribution, predictive insights, and AI-powered recommendations, but most organizations are unknowingly building these ambitions on messy, inconsistent, and incomplete data. In this session, we'll cut through that chaos and show you how to build a clean, AI-ready marketing data engine that works from impression to revenue.
We'll start by breaking down how to design a marketing campaign taxonomy that keeps your channels, objectives, funnel stages, and tactics organized. You'll see real examples of how a strong taxonomy simplifies reporting, improves optimization, and strengthens the outputs of models like MMM, which rely heavily on stable, well-structured inputs.
Next, we'll demystify UTM setup, the often-overlooked system that maintains mid-journey attribution. You'll walk away with a simple, durable UTM framework that prevents data loss, eliminates manual guesswork, and cleanly ties paid media, GA4, and CRM data together.
Then we'll shift to the heart of full-circle attribution: the CRM. You'll learn exactly which media and GA4 data fields should flow into your CRM to enable first-touch, last-touch, and multi-touch attribution, all in a way that is scalable, reliable, and AI-ready.
From there, we'll connect the dots across your MarTech stack by showing you how to standardize media, GA4, and CRM data into a single source of truth. This includes data structures, naming conventions, and governance steps that unlock accurate dashboards, stronger MMM models, and trustworthy AI insights.
Finally, we'll e ...
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