Model description, both at the overall level on what variables are most important to the forecast, and reasons for an individual record forecast can be a competitive advantage in a business or consulting situation. Design objectives for this model description include 1) describe the model in terms of variables understandable to the target audience 2) independent of the predictive algorithm, 3) support a single or ensemble of models, 4) pick up non-linearities in variables and 5) pick up interaction effects between variables. The Segmented Sensitivity Analysis (SSA) method has been used by the author for dozens of projects over years in a variety of data mining projects. As with other descriptive techniques, there is a bit of art when developing a solution for a specific use case. A number of SSA variations and use cases will be discussed to illustrate how the system can be adapted. The SSA model description can also be helpful during the model building process, as well as detecting and describing model drift over time, as the behavior of the scoring data slowly changes or drifts from the training data.