From the course: Marketing Attribution and Mix Modeling

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Data-driven attribution and machine learning

Data-driven attribution and machine learning

From the course: Marketing Attribution and Mix Modeling

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Data-driven attribution and machine learning

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- [Instructor] Data-driven models are the most sophisticated, but also the most robust and accurate models available. Most other attribution models require human intervention to calculate and update. Data-driven models work in the background automatically, and therefore can be less biased. We take the example of a user who read some of your blog posts and then signed up for email lists, got an email and came through to the website, but they didn't want to purchase straight away, and a few days later they saw an ad, clicked on the ad and purchased. On the last click, 100% of the credit would go to the ad, even though the email and the blog also played a big role. With data-driven, the credit would be shared based on the uplift that that channel drives and that's figured out algorithmically using machine learning. So it might find that the ad actually deserved a decent amount of credit, but also the blog deserved a lot of…

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