We use Logistic Regressions where a variable we are trying to explain or predict are categorical in nature while the explanatory variables are a mixture of categorical and scale variables. Logistic Regression is useful in classification problems and scoring models. One example could be classifying potential defaulters on bank loans based on credit history. They can be used to predict likely responders to direct mailers, purchasers of cars after trial and in all such cases where we want to classify customers distinct categories.
Using logistic/multiple logistic regression, we can build our model based on an existing dataset, test its predictive validity and then deploy it on new data to predictively classify customers.