Cem Atik

Figuring out new keywords, just doing more A-B testing into campaigns, doing more experiments.

And that's the thing.

Just focusing more on scale instead of optimizing or just scaling.

Yeah, spending time in optimizing that you just can automate with AI is really simple because it's really simple.

Let us say you want to spend $100 each search term max before you just made a decision.

Then you say to a search term excluder, as example, a script that I built, by the way, hey, please check all keywords out that spend over $100.

If the return of investment is under one, cut it, point.

Simple stuff.

And that saves you a lot of time, especially when you are just having ad accounts spending 10 or 20K a day.

It's a massive release in work.

Ah, unfortunately, A-B testing is still a lot of manual work.

What we do is talking with AI to get different ideas, brainstorming, testing different angles.

But AI is a good module for doing data-driven decisions, right?

So that works great.

But to say like, hey, test this vertical as example, it's right now not working.

Maybe in like a couple of years, I'm pretty sure that we will be on this point.

But right now it is more for doing manual work that is really data-driven.

As I say, we reach point A in spend.

If it's profitable, let it run.

If not, cut it.