Every time I read an article about AI and advertising, I know the author will just scratch the surface. The article will be filled with buzzwords, numbers, and quotes about how great (or bad) AI is.

I understand different audiences might look for different answers. What I don’t agree is with the lack of research on the subject.

Putting my rant aside, and for those CTOs considering to add AI to the mix, I wanted to take a bit further one of the main expectations about AI: understanding the customer journey.

When we are talking about AI in advertising, we are referring to “Analytical AI,” that is, learning from past data to predict future outcomes and advise on decisions. We are not talking about Deep Blue vs. Kasparov.

If your AI-powered feature is telling you that there is a bottleneck in step #7 of the funnel, that is not AI, that is a simple analysis.

To understand your customer journey first you need to collect data. You need a reliable cross-device tracking system. Second, if you are running ads on multiple channels, you need to collect data from their Ads API and understand where the customers are coming from. To gather this channel-owned statistical data, you need another system.

Finally (and I emphasize finally here) you can introduce predictive analysis and Analytical AI.

This last piece is where we are now. Predictive analysis has been for a long time. But AI making decisions on their own based on simulatiosn, is where we are working on in the ad tech industry.

Once you’ve collected all the channel and users data, once you’ve done all the analysis and predictions, then you can apply AI to run simulations on potential changes in your funnel, and allow your AI to make the best decisions . This is the last frontier where you want to take your product.


Hi! My name is Leo Celis. I’m an entrepreneur and Python developer specialized in Ad Tech and MarTech.

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