Feature importance when there is multicollinearity

How am I supposed to figure out feature importance for a churn model if a lot of my independent variables are highly correlated?

For example weekly active users vs power users [these are just highly engaged users] vs. viral users [these are users that share our product]

VIF is screaming at me saying 50% of my features are way above the “rule of thumb” 10.

Correlation matrix is also showing >.8 on a lot of features.

But I’m still trying …

datascience feature importance multicollinearity