Is it problematic if one predictor in a set accounts for almost all the prediction? I am running a logistic regression with customer event data with multiple predictors.
However, one variable is extremely important, alone predicting 60% of the customers for the event. When this main predictor is included in the model, other predictors add very little to prediction over and above this main predictor. 
This main predictor is not a post event variable. The variable has full business support to be in the model.


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*Given this, is it still okay to retain this main predictor variable in the model?

*Does this suggest that there is anything wrong with the model?

 A: I understand your gut feeling. But depending on the type of response and predictor, this has not to be unsual (example: response = "weight", predictors "height" and others with presumably less meaning like "state", "favorite movie" etc.).
However, you should check that for the creation of the predictor only information has been used which was available at that time.
Here is an example:
Suppose you want to predict an event of the next day. You have raw data from day 1 to 10. Now you create one predictor by calculating a statistic over all 10 days and other predictors using only "local" information (i.e. directly extracted from a single event on a single day, i.e. "event start time"). Now you build a model from day 1 to 5 using this predictors to predict the events on day 6. The model is flawed because it contains information from day 6-10, information which is never available when building the model for real (i.e. not simulation) usage.
If this check has been succesful (I guess you mean that with "not a post-event variable"), then it shouldn't be a problem. In one of my projects, something similar occurred. Checking on the meaning of the variables indeed revealed, that the detected relationship (i.e. variable importance) was trivial, but true ;).
