But with Google now having built a much faster Tensorflow processor underpinning its AI efforts, you can bet their systems for finding users going through a particular life event will be really good and useful for advertisers. In the example they gave, they showed how Shadow Making people from different cultures might look for different things related to a marriage. Google's machine learning can pick up these differences and know that it corresponds to the stage of married life. The best ad automatically We've all been doing A/B ad testing for years. But that Shadow Making becomes much less relevant if you look at what Google is now able to do with AI. Sridhar Ramaswamy, Senior Vice President of Ads and Commerce, showed an example of three users all searching for something fairly generic (like , but each receiving a different ad from the same advertiser.
The various ads were not driven by audience bid Shadow Making adjustments or anything else we had control of. Rather, it was AdWords predicting each user's preferences to display subtle variations in ad text, focusing either on price, value, or selection. As someone who has created ad optimization tools in our software suite at Optmyzr, what I've heard is that we should focus primarily on creating a ton of ad variations and then let the machines decide which Shadow Making ones to broadcast. What this means for advertisers is that creating many ad variations is likely to become a bigger task than before, so that we can feed the machine all the possible variations it needs to make a incredible optimization. Data-Driven Attribution Made Easy Search Engine Land paid media reporter Ginny Marvin has written a great rundown of what Google Attribution is, an important.
Piece to read if you're wondering why Google decided Shadow Making it needed to create an additional tool to do attribution modeling (we have already integrated AdWords, Analytics and DoubleClick). I'm excited about this new offering because when I got to play with it I saw how quick and easy it was to get started. But easy setup doesn't make sense unless the tool is also really good, so the real reason for my excitement is that data-driven attribution modeling is now becoming much more accessible. The problem with attribution models is that they are our best attempt to model real-world behavior with a somewhat limited toolset. With improved store visit data, store Shadow Making sales data, easier data consolidation and Google AI - four event themes - we no longer have to struggle to trying to do something really complicated by hand. Data-driven models assess each touchpoint's contribution to the end result.