From the course: Marketing Attribution and Mix Modeling

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Feature transformation with diminishing returns and adstocks

Feature transformation with diminishing returns and adstocks

From the course: Marketing Attribution and Mix Modeling

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Feature transformation with diminishing returns and adstocks

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- [Instructor] Not all variables have a linear relationship with sales. For example, brand marketing tends to have a lagged impact long past when the ad ran. And most advertising gets less efficient at higher spend levels. Let's work through how to transform the nonlinear variables to use in marketing mix models. This marketing mix model has already built using the LINEST function here. It's for an ice cream store and we want to know whether there is a lagged effect and also a diminishing returns effect. So, we're going to work through how to do this and build some intuition for what that means. Okay. So we're going to walk through adstocks first. So to add adstocks, I think it's quite helpful just to make an adstock cell up here and then zero is the default adstock. Then in order to make adstocks work, we need to multiply the data by this adstock, right? So in the first cell, we can see we're referencing data C2, and…

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