I am interested in building a model to predict the binary outcome, retention (1 - retained; 0 - not retained) with various potential predictor variables (either continuous or categorical).
With that being said, I have a dataset containing multiple records (magazine subscriptions) for some subjects. For example, I have 4 records at the magazine subscription level for Joe Smith (3 of which he is retained, while the other record he is not retained) and 7 magazine subscription records for John Doe (4 of which he is retained, while the other 3 records he is not retained).
My initial thoughts were to use logistic regression using a single randomly selected magazine subscription for each subject. For example, randomly selecting 1 of the 4 magazine subscriptions for Joe Smith and 1 of the 7 magazine subscriptions for John Doe. Obviously, I would be losing a great deal of my data, which makes me think that there has to be a better way of modeling this data.
What method would you be best to predict retention with data such as this?