Dealing with events that have not yet happened when building a model
This setting is common in reliability, health care, and mortality. The statistical analysis method is called Survival Analysis. All users are coded according to their start date (or week or month).  You use the empirical data to estimate the survival function, which is the probability that the time of defection is later than some specified time t.

Your baseline model will estimate survival function for all users.  Then you can do more sophisticated modeling to estimate what factors or behaviors might predict defection (churn), given your baseline survival function.  Basically, any model that is predictive will yield a survival probability that is significantly lower than the baseline.



There's another approach which involves attempting to identify precursor events patterns or user behavior pattern that foreshadow defection. Any given event/behavior pattern might occur for users that defect, or for users that stay. For this analysis, you may need to censor your data to only include users that have been members for some minimum period of time. The minimum time period can be estimated using your estimate of survival function, or even simple histogram analysis of the distribution of membership period for users who have defected.