Tobias "Tobi" Konitzer

Right.

Positions growth loop as a central hub of experimentation intelligence.

So I can lock that data and I can understand causal relationships and the effect, the causal effect of different interventions.

So that to me was the foundation.

I cannot build an autonomous decision system or decisioning system if I don't give the system a prior understanding.

as to here's a track record of encoded causal relationships or causal statements and the results.

So for that to happen, I needed experimentation.

I actually needed experimentation to sit in the product primitive that our customers use the most.

Now you have a different problem, which is you've got to go to the market and you've got to convince the market that you better use my experimentation service as opposed to the experimentation service that lives downstream.

So in the customer engagement platforms, in the marketing clouds.

And so we had to build features into the experimentation platform to make sure that this pull happens, that we can be the gravity, the gravity of intelligence for experimentation.

And that was two features.

One of them was...

Yeah, we sit on top of all your data so we can forecast exactly how can you run the most efficient experiment.

So our argument all of a sudden was, okay, you run experimentation in your marketing cloud and you usually say, you know what, I'm going to dedicate 50% of the traffic to the control group.

and 50% to the treatment group.

That's all you can do.

But imagine if you move experimentation closer to the data where we sit, where you have much more intelligence.

Now we can use machine learning or AI to forecast how to run the experiment most efficiently.

So imagine if there is a 10% gain in the experiment.