Novel Trifecta Approach To Feature Selection In MMM
Novel Trifecta Approach to Feature Selection in MMM
Feature selection plays an important role in Marketing Mix Modeling (MMM). Incorporating
features in the model that best predicts or explains the dependent variable is essential for a
good model.
Traditionally in MMM, the feature selection process has been based on correlation and
domain knowledge. In this approach, the variables are shortlisted first based on the
correlation with the KPI. Then a domain expert weighs in on the variables provided and
selects the variables based on a certain correlation threshold + domain knowledge.
Using the traditional approach has its pitfalls; Correlation only measures the linear
relationship between the feature and the KPI. So, you can miss out on important variables
that are non-linearly related and provide significant information about the KPI.
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