Churn Prediction Training Set
A simple way to go would be to use Option 1. Thus, each row could be uniquely identified and you could perform classification easily. You could additionally add a column which mentioned the week in which they churned. This would be very similar to a Type 2 Slowly Changing Dimension. Go wide with your dataset, ie: keep adding as many columns as necessary. That way, even if you have to look at it in Excel before creating a classification model, it'll be easier. If you are using a Random Forest classification algorithm or any other tree based algorthim, this option would make good splits on nodes.

Option 2 would be possible, however, it would result in a very deep dataset which would grow over time if you decide to update it frequently.