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Data Mining Deployment Guide > Deploying Real-Time Scoring with Siebel Data Mining >
Process of Configuring Siebel Operational Applications for Real-Time Deployment
After you have defined the requirements for your real-time deployment, use Siebel Tools to configure both the identified Siebel objects and Intelligent Interaction Manager to handle the model trigger. Depending on the number and complexity of measures defined in Siebel Analytics as independent model variables, as well as the number of expected concurrent users of your real-time application, consider prefetching these Analytics measures before the trigger event takes place, to guarantee adequate response times. You can either prefetch Analytics measures into run-time memory upon identifying a customer, or store scheduled batch-generated measures into the Siebel OLTP database. The choice depends on various factors: - If the model needs to be triggered shortly after the customer has been identified, prefetching model input values from Analytics at run time is not feasible.
- Storing Analytics measures in the OLTP database may impose some administrative overhead and also requires additional storage space. If a sufficient time lag exists between identifying the customer and executing the model, this period may be used to access model inputs from Siebel Analytics in real time, and hold these values temporally in memory until the model trigger takes place. In most cases though, calculating Analytics measures in real time is not an option.
The rest of this section assumes that all input variables have been committed to business component fields before calling the model. The following steps show how to configure a Siebel operational application (such as call center) for real-time scoring of a predictive model using calculated fields. An alternative approach using eScripting is described in Setting Up Batch Scoring with Siebel Data Mining. To configure an application for real-time scoring
- Using Siebel Tools, create two new calculated fields on the business component that expose the real-time score.
- The first field will hold a Siebel Analytics logical SQL statement that will fetch the real-time score. A model is essentially treated as a table in a database.
- The second field will hold the real-time score returned by Siebel Analytics.
- In the first calculated field that will hold the Siebel Analytics logical SQL, enter a statement with the following syntax:
'SELECT <Score Column> FROM <Model Subject Area>
WHERE <Model Input Variable 1> = [Business Component Field 1]
AND <Model Input Variable 2> = [Business Component Field 2]
AND <Model Input Variable 3> = [Business Component Field 3]
...
AND <Model Input Variable n> = [Business Component Field n]'
In this example, an SQL statement fetches a churn propensity score through Siebel Analytics. The new calculated field is called Churn Model SQL and belongs to the business component Contact. The SQL statement might look like this:
'SELECT ChurnPropensity FROM ChurnModel
WHERE Age = '''+[Age]+'''
AND Gender = '''+[Sex]+'''
AND '% Change Min Used' = '''+[% Change Minutes]+'''
AND '# Time Plan Changed' = '''+[# Plan Changes]'+''''
The preceding example shows the proper use of escape characters to concatenate string and field values. For details on how the Churn Model subject area in Siebel Analytics invokes the predictive model and fetches the score, see Process of Configuring Siebel Analytics for Real-Time Deployment.
- In the second calculated field that will eventually store the real-time score, enter the following expression:
InvokeServiceMethod("IIM", "ExecuteQuery","Username='<Siebel Analytics Generic user>', Password='<password>',DSN='<Analytics ODBC DSN>',
Query=[Field holding logical SQL]", QueryResult)
<Analytics ODBC DSN> must be created on the machine running the Siebel Server and it must point to the Siebel Analytics Server that is connected to the predictive model.
Following standard Siebel Tools usage directions, expose the field that will hold the score in the applet of your choice.
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