SAS GF paper on RPM - SAS Global Forum 2010 Customer...

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Paper 113-2010 Rapid Predictive Modeling for Customer Intelligence Wayne Thompson and David Duling, SAS Institute Inc., Cary, NC ABSTRACT Business analysts often need to develop campaigns based on predictive models to select which customers to target. The models often need to be developed in a short amount of time without always relying on a statistician. For these cases, SAS has developed the SAS ® Rapid Predictive Modeler for use by customers of SAS ® Enterprise Miner™. SAS Rapid Predictive Modeler runs as a customized task in either SAS ® Enterprise Guide ® or the SAS ® Add-In for Microsoft Office. It automatically treats the data to handle outliers, missing values, rare target events, skewed data, collinearity, variable selection, and model selection. The results are presented in business terms as a simple-to- understand scorecard. The final model can be registered to the SAS ® Model Manager and deployed to either a SAS ® server or through a SAS ® Scoring Accelerator to a relational database. The presentation covers both the implementation and results from an example analysis. INTRODUCTION The SAS Rapid Predictive Modeler has been created to ease the process of creating efficient, accurate, and robust data mining models. It requires minimal user input and produces reports that are suitable for business presentations. SAS Rapid Predictive Modeler models have been designed for common classification and prediction scenarios such a s customer acquisition, up-selling, cross-selling, retention, churn, return on investment, and many others. In fact, SAS Rapid Predictive Modeler is suitable for any scenario in which the data are well-defined and a data mining model needs to be created quickly and accurately. SAS Rapid Predictive Modeler models are easy to produce. You must supply a data set in which every row contains a set of independent predictor variables (known as inputs) and at least one dependent target variable. There are no other required selections. The SAS Rapid Predictive Modeler decides whether variables are continuous or categorical and which variables should be included in the model, and it uses a broad class of classical and modern modeling techniques to ensure the model is accurate and robust. Three modeling methodologies are used: basic, intermediate, and advanced. The basic model develops a good baseline model quickly. The i