The goal of response modeling is to either increase response to a given solicitation quantity or reduce the solicitation quantity without suffering a drop in response. Otherwise stated, you can drive the numerator (response) or shrink the denominator (expense). In truth, after your analytical consultants have worked their magic and left the stage, you won't know for sure whether you've achieved either of these goals until you conduct "live" testing. Never- theless, important signals about your model's predictive ability can be found in the documentation that was left behind by the model builder.
The output in Figure 1 is for a logistic regression model (LRM). We use a SAS example only because it is a frequently used statistical software, and LRM is one of the most common response modeling techniques. Other packages and algorithms produce similar tables.
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