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Data Mining Subject Area in Siebel Answers

NOTE:  This section applies only to Siebel Miner and Siebel Analytics Applications users.

Stored in the Siebel Data Warehouse, batch scores are analyzed with Siebel Answers. For this purpose, Siebel Analytics provides prebuilt metadata that links batch scores to customer profile data and other Relationship entities. This metadata consists of a dedicated data mining subject area and additions to various subject areas that allows analysis of batch scores in the context of customer data. This data mining subject area exposes information residing in the Siebel Miner repository, including model metadata and scoring data. Figure 5 shows the contents of the data mining subject area.

Figure 5.  Siebel Data Mining Subject Area
Click for full size image

The content of this subject area is also available in the following subject areas:

  • Campaigns
  • Campaigns Overview
  • Consumers
  • Consumers Overview
  • Customers
  • Customers Overview

Example of Using the Data Mining Subject Area

For example, in order to display the average churn score in relation to the customer profile attribute Marital Status:

  1. Go to the Customers subject area.
  2. Open the Contact or Prospect/Contact Profile table.
  3. Select the Marital Status column.
  4. Open the Data Mining Models table.
  5. Select the Score Model Name column.
  6. Add the filter "is equal to" <Churn Score Model Name>.
  7. Remove the Score Model Name column.
  8. Open the Facts/Data Mining Facts table.
  9. Select the Average Score column.
  10. Execute the Query.

In addition, the data mining demo subject area exposes data provided with Siebel Miner demo templates. See the Siebel Miner documentation in the Angoss folder on the Siebel eBusiness Third-Party Bookshelf for a description of demo data mining templates.

NOTE:  To use the existing analytics metadata that joins batch score records to the Account, Contact, or Product entities, the ID column of batch score records must match the ROW_WID of these entities.

    
Data Mining Deployment Guide