Sensitivity Analysis (SA) is the study of how variation in the model target variable can be partitioned to different sources of variation in the predictor variables, which define the model itself. Originally, SA was used to increase the confidence in the model and its predictions by providing an understanding of the rates of change in the target variable due to changes in the predictor variables. SA can be found in areas such as economics, social sciences, and risk assessment. However, it has not become an analytic stable in database marketing despite its value in determining: a) model resemblance with the behavior under study, b) quality of model definition, c) predictor variables that mostly contribute to the target variable's variability, d) interactions among predictor variables, e) the region in the predictor variable space for which the model variation is maximum, and f) optimal regions within the predictor variables space for use in subsequent studies. The purpose of this article is to illustrate SA for database marketing models, one acquisition model and one retention model, to hopefully stimulate interest in such analyses.