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Monday, July 18, 2022

Modeling Marketing Mix Using Smoothing Splines

 Recall a similar set of problems occurring in the enterprise, this could be useful. Computing and statistical -Technical.

Modeling Marketing Mix Using Smoothing Splines  By Slava Kisilevich in TowardsDataScience

Capturing non-linear advertising saturation and diminishing returns without explicitly transforming media variables

The established approach among marketers for modeling marketing mix is to apply linear regression models which assume the relationship between marketing activities such as advertisement spend and the response variable (sales, revenue) is linear. Prior to modeling, media spend variables should undergo two necessary transformations to properly capture the carryover effect and the saturation effect of the advertisement spend. It is known, that advertisement spend is not linear with respect to the response variable and follows the law of diminishing returns. However, the functional form of the saturation curve is not known in advance. 

Therefore, the modeler should first hypothesize about the possible transformation functions that should be applied to each media activity channel to match the true spend-to-response relationship. In this article, I show an alternative approach to modeling marketing mix by using Smoothing Splines, which is the way to model the non-linear relationship between dependent and independent variables within the framework of a linear model. By following this approach, the model will establish the non-linear relationship between media activity variables and the response variable without the need to transform those independent variables to account for the non-linear relationships.  ... ' 

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