What is causal data, and how is it created?

In the video series, From Data to Decisioning, we explore the concepts of causation and correlation in marketing, and why causality data is the missing ingredient your team needs to drive meaningful growth with agentic AI.

Watch episode 2 below, and scroll to the bottom for a full list of episodes.

AI decisioning has massive potential for your growth. The only problem is, is that AI using the right kind of data? 

Remember that in marketing, most data is correlational. It tells you what will happen under the existing status quo. 

Today, I'm going to dive into what we call causality data. How can you create causality data? How can it be logged? And how can it speed up and make more efficient AI decisioning.

What is causal data?

First of all, causality data is the encoding of the effect of any intervention, the causal effect of any intervention. What would have happened to you as a person if you saw the ad versus what would have happened to you as a person who didn't see the ad? 

How is causal data created?

Now, of course, that's unachievable. You can't be in both states at the same time, but we can use a trick called randomization where I flip a coin and you are treated to that ad. Versus creating a digital twin who is looking exactly like you but not treated to the ad. That is the foundation of causality data. 

And you may think this takes a lot of time to build, a lot of time to develop. It's not actually true. Imagine we have processes that listen to every experiment that you've ever done and gonna continue to do in your organization and lock that data right, store it correctly. 

Now, causality data essentially tells you how to efficiently affect change. It is the missing link between agentic AI, AI decisioning, and making it all work for your growth targets.

Catch more episodes

Watch more From Data to Decisioning episodes:

• Episode 1: Overview of correlation vs. causation in marketing