I'm working on clustering email addresses using K-means based on their value to and engagement with the company (metrics such as % of emails opened, # of web browsing sessions, etc). I would like to use days since last purchase as a feature to model on (capped at 365 days), but ~37% of the email addresses have never made purchases.
What would be the best way to deal with these missing values? I don't want to remove those data points as they are still important and make up a significant portion of the entire data set.
Note that I have data on email subscription date - could this be used in place of the missing values?