Using Machine Learning to create marketing segments
Wow, what a great question! This is a critical task for anyone who works with data in a marketing function. 

First, I'll address your question about pairing the prediction to email. Your email is serving as a unique identifier. When you split your data set, you'll have to keep the email and suppress it from the prediction, and use it to match/compare to the validation data. 

If these are new predictions, you could create a new column in your data that contains the "predicted" value. Specifics on this process would be better suited for another community, probably StackOverflow.

Second, I'd like to talk about what you're trying to achieve.

I think there are two questions ways you could approach this with the work you've already done:


Identify who is most likely to subscribe, and focus your efforts on creating nurturing campaigns to "warm" these prospects. The final goal is to get a subscription.

Here you're going to be using those "events" that you mentioned. What exactly do you have? in the past. I've just dumped web activity data and started working with that. You'll have to engineer a lot of features to capture information that your web activity won't report on (these could be # of logins, # of checkouts, time since last visit, this is where you need to get creative).

Take this data and try to make some predicte who is likely to subscribe. In some sales industries, this is refereed to as a lead score.

Since you're using R, you could try using XGBoost -https://cran.r-project.org/web/packages/xgboost/vignettes/xgboostPresentation.html. Since you're trying to predict a binary variable, you could use their decision tree.

This is going to take a while, so don't get frustrated if you don't this to production in the first week (or month). Here's some reading:

https://datastories.com/gallery/predictive-user-scoring-example

https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/

https://rdatascientist.wordpress.com/2015/01/26/predictive-lead-scoring-using-r-first-of-a-two-part-blog-series/
Identify which types of customers you want to acquire, and focus on building campaigns to engage these types of customers. The final goal here is to acquire quality customers.

This is more marketing and strategizing than it is machine learning. Don't get me wrong, there is maching learning in identifying the clusters. Here's a great example I used: http://www.kimberlycoffey.com/blog/2016/8/k-means-clustering-for-customer-segmentation.

Once you identify what the different segments and what behavior they exhibit, you have to team up with the other stakeholders and try to decide what kind of customers do you want? Whats the benefit is getting a customer who makes one large purchase a year vs. a customer who makes five smaller purchases a year? You'll have to take your time identifying customers/prospects who are the most valuable, and plan a strategy around that.