Weight of Evidence (WOE) and Information Value (IV) have become important tools for analyzing and
modeling binary outcomes such as default in payment, response to a marketing campaign, etc. This
application encounters difficulty when dealing with continuous outcomes because non-occurrence is
either unquantifiable or non-existent. Going back to the fundamentals of Information Theory, this paper
suggests a set of alternative formulae that attempts to dichotomize high occurrences and low occurrences
in order to expand the use of WOE and IV to continuous outcomes such as sales volume, loss amount,
etc. A SAS® macro program is provided that will efficiently evaluate the predictive power of continuous,
ordinal and categorical variables together and yield useful suggestions for variable reduction/selection,
segmentation and subsequent linear or logistic regressions.