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Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models
Avinash Kaushik / Occam's Razor / August 12, 2013
There are few things more complicated in analytics (all analytics, big data and huge data!) than multi-channel attribution modeling.
We have fought valiant battles, paid expensive consultants, purchased a crazy amount of software, and achieved an implementation high that is quickly, followed by a ” gosh darn it where is my return on investment from all this?” low.
A lot of that is because of all the stuff we don’t know. There is lots of missing data. And as if that were not enough, there is lots of unknowable data. Neither of which has stopped Gurus and Masters and Agency High Priests from trumpeting here’s the next thing directly from Lord Krishna that will solve all your problems.
So, let’s apply Occam’s Razor to this complicated challenge. Let’s try to make some sense of it all.
By the time you are done with this post you’ll have complete knowledge of what’s ugly and bad when it comes to attribution modeling. You’ll know how to use the good model, even if it is far from perfect. I’ll close with a custom attribution model into which you can insert all your biases – sorry, I mean expertise – and get something better than good to make incremental progress from where you are today.
My macro goal is to make you dangerously informed. By the end of this post, if you pay attention, you’ll know the often hidden nuances and you’ll be dangerous to any analyst/consultant/vendor who walks into your cubicle/office with I’ve got the God’s-gift-to-humanity, easy-to-implement solution with insights riding out to you on a Unicorn.