Since the data is unlabeled you cannot classify the data in any straightforward way.
Is there anyway you can manually identify a few of the data points as potential user class and non potential users? If so, you can build a classifier this way with your now known labels.
Another possible solution is to do a cluster analysis of your data and see if you can get good separation with two clusters. Try to see if the two clusters give you any separation of users that can be classified into these two groups.
For new users you can then assign them to the existing clusters. This is very crude way of doing classification with unlabeled data.
Based on your comments I think you are looking for semi-supervised learning. Here is an excerpt from wikipedia (http://en.wikipedia.org/wiki/Semi-supervised_learning):
Semi-supervised learning is a class of supervised learning tasks and
techniques that also make use of unlabeled data for training -
typically a small amount of labeled data with a large amount of
unlabeled data. Semi-supervised learning falls between unsupervised
learning (without any labeled training data) and supervised learning
(with completely labeled training data). Many machine-learning
researchers have found that unlabeled data, when used in conjunction
with a small amount of labeled data, can produce considerable
improvement in learning accuracy.
So basically you will try to label some of your data and then treat it as a semi-supervised problem. I am not sure what language you are using but you can take a look at python's scikit-learn library which has some methods for semi-supervised learning: http://scikit-learn.org/stable/modules/label_propagation.html