Attribution in the Warehouse

​Most companies have attribution reports. Far fewer have attribution reports anyone trusts.

The pattern is always the same: the data team builds or even models the data, marketing is supposed to use it, but marketing can't interrogate the numbers or build on top of them. So the numbers get challenged instead of used. Meetings relitigate figures instead of deciding, the data team drowns in one-off verification requests, and teams drift toward maintaining parallel versions of the truth.

In this session, Barbara Galiza and Timo Dechau will make the case that this is a foundation problem, not a reporting problem, and walk through the design and principles of an "intelligence layer": a trustworthy data foundation the data team builds and marketing runs on.

This webinar is hosted by Barbara Galiza and Timo Dechau and sponsored by RudderStack.

What to expect

We will cover:

​• Why attribution reports get ignored even when they're correct, and why adoption follows transparency, not accuracy
• The design and principles of an intelligence layer that both data and marketing share
• What the foundation is made of: identity resolution, attribution modeling in the warehouse, and audience building with activation back into the ad platforms
• How RudderStack supports each piece, including a look at Lookout, its new AI analytics instrumentation
• The division of labor in practice: the data team builds and owns the foundation, marketing runs attribution, segmentation, and activation on top of it
• Why data set up this way is ready for AI use cases, without making AI the pitch This is a principles-and-architecture session, no coding required. A warehouse foundation isn't a silver bullet: it takes real data engineering effort, and that effort is exactly what we'll show.

Who should attend

This session is for marketing leads, marketing ops, and growth teams, plus the data and analytics leads who support marketing.

​It will be especially useful if leadership has started questioning marketing's numbers, your data team is rebuilding or considering a warehouse-centric stack, reporting requests keep bouncing between teams, or there's an AI mandate and everyone quietly agrees the data underneath isn't ready.

​Some familiarity with what a data warehouse is and how different sources get ingested and used together will help you get the most out of it. Bring your questions: registration is capped at 200 so we have time to answer all of them in the 15-minute live Q&A.

​About Barbara and Timo

​Barbara leads marketing measurement strategy at Propel. Over the past 10 years she has helped brands like Microsoft, Mollie, and VEED.io answer the question every marketer dreads: what is actually driving results?

​Timo leads data engineering at Propel. He builds the tracking systems marketing teams wish they could trust, and has done it for companies like Amplitude, Shimano, Cricut, and Audible.