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Building Measures with Siebel Analytics

In most cases, the existing raw content available in your data sources is not enough to build rich, meaningful data mining models. A customer mining record should include industry- and problem-specific measures that are derived from existing information sources. Use Siebel Analytics to derive measures with predictive power that describe customer behavior with relevance to your immediate data mining task. Choose from a variety of functions to build derived measures in Siebel Analytics.

Building Measures for Siebel Analytics Applications Users

NOTE:  This information applies to both Siebel Miner and Siebel Data Mining Workbench users.

There are predefined measures in the Siebel Data Warehouse that you can use as a basis for building additional measures and enhancing your CMR. The Siebel Data Warehouse provides a prebuilt repository of measures tailored to industry-specific customer and business analytics needs.

Example of Building Measures with Siebel Analytics

To build a churn model, the wireless service provider needs to derive measures that capture cell phone usage patterns. Based on the source variable—Minutes Used—in the physical layer of Siebel Analytics, the provider derives a new variable—%Change in Minutes Used—in the business layer of Siebel Analytics. See the Siebel Analytics Server Administration Guide for details on defining metadata to build meaningful measures.