“(…) you can drill down into discrepancies between forecast and actual performance at the keyword level to get a better look at why search traffic varied.”

Adobe’s advertiser guide to a higher return on ad spend

Most of the agencies take a human vs. Skynet approach when it comes to using AI vs. manually optimizing campaigns.

The reality is that even with simple ML implementations you can easily compare a human performance forecasts, with a machine forecasts.

In the beginning, the analysts will win, which is fine, because you can use the learnings to keep training the machine.

If you know which are the factors (features) that are influencing a campaign’s performance, you can feed the ML algorithm and produce machine forecasts using more than your Excel’s 1M rows limit.

leocelis

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

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