From the course: Marketing Attribution and Mix Modeling
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Linear attribution: Treating all touches equally
From the course: Marketing Attribution and Mix Modeling
Linear attribution: Treating all touches equally
- [Instructor] Sharing the credit equally. That concept feels almost too simplistic, but it can be surprisingly useful. Unlike other models, with linear attribution, you're making no value judgment about how each touch contributed. Credit is always shared equally across all touches, so this model can serve as niche channels that otherwise would be buried for lack of data. Let's take this simple example. If the user is reading through a blog post and then leaves the website, later is re-targeted with an ad, clicks on the ad, comes back to the website, doesn't purchase, but they do sign up for the email, and then when they get an email a few days later, they click on that email, and then finally purchase. Under the last click model, which is default, the email would get 100% of that credit, even though they wouldn't be on the email list in the first place without the ad and they wouldn't have considered clicking on the…
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Contents
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Last-click attribution: The default model1m 54s
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(Locked)
Time decay and conversion lags2m 42s
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(Locked)
Linear attribution: Treating all touches equally2m 14s
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(Locked)
First-click models: From awareness to acquisition2m 41s
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(Locked)
Position-based models and assigning credit2m 14s
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(Locked)
Data-driven attribution and machine learning2m 4s
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(Locked)
Click windows and view-through conversions3m 37s
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