Attribution Modeling and Weighting by Tony Wright (Part 1)

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First of all, let's talk a little bit about attribution. I'm assuming most of the people that are watching this video understand attribution to an extent. We understand that most of us have been for years looking at the last click to determine what gets the credit for a sale. We also usually understand it's probably not the best way for us to look at what our resources and money, where they need to go, but it's always been how do we get beyond that? Google Analytics has provided us with some tools, and we're going to talk about those later. There are also a lot of other great tools out there for attribution specifically, custom attribution weighting, but I'm not going to go into those today. Maybe in a video down the road, we might review some of those tools.

For right now, I'm going to concentrate on Google Analytics. Attribution, obviously the holy grail would be if we could understand every interaction that a consumer had before they actually purchased or gave us their information or whatever conversion point we're trying to get the consumer to do. The problem with ... If we can pretty much do that, we can pretty much track most of the interactions online, but the problem is understanding the intent of that consumer because even when we start going, for instance, if we have two consumers that have the exact same path, their intent or what drove them to take that path may be completely different. The holy grail would be to understand intent. Maybe someday with machine learning and AI, we'll get there, but I have a feeling that we're talking about humans here. We're not always rational beings, so understanding intent is not necessarily going to happen. I think that that's something that you need to understand when you're looking at weighting and doing attribution modeling.

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