SAS GF paper on RPM

SAS GF paper on RPM

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Unformatted text preview: mon business graphics such as lift charts. You can choose additional details such as statistical goodness-offit measures and crosstabulations. The output is presented directly to you and also saved to a PDF or RTF file for inclusion in custom reports. SAS Rapid Predictive Modeler models are easy to deploy into scoring systems. The models are saved as Base SAS ® code that can be executed on any Base SAS installation. You can register the models to the SAS Metadata Server for direct use in other products such as SAS Enterprise Guide, SAS® Data Integration Studio, and SAS Model Manager. These products can automate the execution of score code and the deployment to other systems. SAS Rapid Predictive Modeler score code is also fully compatible with the SAS Scoring Accelerators for Teradata, Aster, Netezza, and DB2. SAS Rapid Predictive Modeler models are compatible with SAS Enterprise Miner. In fact, the models are built using the functions of SAS Enterprise Miner. Models can be opened as projects and diagrams inside SAS Enterprise Miner for investigations into diagnostics, behaviors, and alternatives. SAS Rapid Predictive Modeler and Enterprise Miner users can collaborate on projects to produce the best model for each target. SAS Rapid Predictive Modeler models can also be executed later using the batch processing facility of SAS Enterprise Miner. 1 SAS Global Forum 2010 Customer Intelligence SAS Rapid Predictive Modeler builds the following models for standard data mining classification and regression problems: • Classification models predict the value of a discrete variable such as True or False; High, Medium, or Low; Purchase or Decline; and Churn or Continue. • Regression models predict the value of a real number variable such as Revenue, Sales, or Success Rate. Creating models of last year’s sales receipts combined with demographics, credit ratings, and segmentation strategies is called model training. You can save these models as SAS programs that can be used to predict values on new input data in the absence of target data. These score code programs can be used in any Base SAS environment. You can apply the score code to new data to make business decisions such as how much capacity to build or which customers to select for special offers. This process is named model scoring. CHURN CLASSIFICATION EXAMPLE SAS Rapid Predictive Modeler is best presented through an example analysis. Churn refers to the tendency of a subscriber to switch providers; it is one of the most common problems faced globally in the telecommunication industry. Reasons why a customer might churn include a competitive stimulation, unhappiness with service after the sale, dissatisfaction with quality of services, a move to another location, or disconnection by the provider due to account delinquency. The objective of this analysis is to build a classification model quickly and easily to measure the propensity for an active customer to churn. This enables service agents to tak...
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This note was uploaded on 09/30/2013 for the course FINANCE 4013 taught by Professor Jamescameron during the Summer '10 term at Ohio State.

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