We also have customers who do not fill out all of the questions. This is missing data. My question is at what point (what percent of data missing) should you start getting concerned? Most of this data is ordinal in nature and multiple imputations works less well I am told with that than other data.

Professor Harrell did not make my day with this...

"Statistical software packages use casewise deletion in handling missing predictors; that is, any subject having any predictor or Y missing will be excluded from a regression analysis. Casewise deletion results in regression coefficient estimates that can be terribly biased, imprecise, or both353. First consider an example where bias is the problem. Suppose that the response is death and the predictors are age, sex, and blood pressure, and that age and sex were recorded for every subject. Suppose that blood pressure was not measured for a fraction of 0.10 of the subjects, and the most common reason for not obtaining a blood pressure was that the subject was about to die. Deletion of these very sick patients will cause a major bias (downward) in the model’s 3 intercept parameter. In general, casewise deletion will bias the estimate of the model’s intercept parameter (as well as others) when the probability of a case being incomplete is related to Y and not just to X"