This chapter covers setting up a modeling environment for users of Siebel Data Mining Workbench or Siebel Miner. It includes the following topics:

• Modeling with Siebel Data Mining
• Identifying Data Mining Data Sources
• Preparing Data for Data Mining Modeling
• Building Measures with Siebel Analytics
• Training Data Mining Models
• Choosing a Representative Data Sample for Modeling

NOTE:  This chapter does not provide specific instructions on using Siebel Data Mining Workbench or Siebel Miner. To do specific data mining modeling tasks with these two products, see the KnowledgeStudio User Guide and the Siebel Miner documentation in the Angoss folder on the Siebel eBusiness Third-Party Bookshelf.

This chapter uses the example of a wireless service provider managing customer churn with the help of predictive analytics. Using predictive analytics to pursue business objectives other than churn management follows a very similar setup and configuration process.