In an R Bloggers post, the author suggests that if more than 5% of the observations in a sample feature is missing, you might want to consider dropping that sample feature entirely. My question is, is there a certain threshold where it's acceptable to drop an entire feature due to missingness? If so, what's an acceptable threshold (i.e. at what percentage should you drop an entire feature completely)?
I have an explanatory variable in my data set where 99% of the values for the last purchase date for a customer is missing. The goal of the analysis is essentially to identify who the top customers are and how they vary from the others. Sure, the last purchase date intuitively seems like an important feature that could explain whether or not a customer is a "Top Customer," but with 99% of the data missing, what are my options?