Identify members who are likely to switch where they receive drug administration
Given that the data is labeled, just perform supervised approaches, they will almost always beat unsupervised.

Intuition why thats the case is because we dont have target function in unsupervised approach. In other words function that discriminates classes given our data set. I like to think that in unsupervised learning this function is identity function and not (what in reality is) some complex one. Given an entire data set, we are asking for a partitioning of the input space, with particular properties (the partitions contain almost all examples, each partition is not too large, the partitions are not too close to one another) but this kind of partitioning does not map to the real classes (since we are expecting classes that are clearly seperated in the dataitself but in reality they have some non-linear seperation, and this non-linear/complex behaviour is represented in terms of some function. That supervised approaches approximate (you can think of almost every supervised approaches as an optimisation))