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Customers with account ages less than or equal to 21.5 months who have lower average call durations measured in
seconds during the weekdays and who also tend to have both smaller percent usage increases from month to month
and older equipment are more likely churners. Involuntary churn occurs when the customer account is closed by the
provider. Customers with high average numbers of days in delinquency are naturally more at more risk of being
canceled. Customers who are dissatisfied with call quality or have billing problems are assigned a much higher score
than customers who are just calling to check their account status. Moving and the other attributes for complaint code
also have fairly high scores. Figure 10. SAS Rapid Predictive Modeler Scorecard Shown in SAS Add-In for Microsoft Office SAVING THE ANALYSIS TO A SAS ENTERPRISE MINER PROJECT
You can optionally save your SAS Rapid Predictive Modeler analysis to a SAS Enterprise Miner project via the
Options panel of the SAS Rapid Predictive Modeler task. This provides tremendous flexibility to share and customize
the model using additional SAS Enterprise Miner tools. The ability to extend SAS Rapid Predictive Modeler models
within SAS Enterprise Miner supports more of a white-box-versus-black-box automated modeling delivery and also
fosters collaboration between business analysts and experienced data miners. The model can be registered from
SAS Enterprise Miner to the SAS Metadata Repository for importing into other SAS Enterprise Miner process flows to
support integrated model comparison, or it can be deployed to other SAS applications.
Several functions in SAS Enteprise Miner were modified to enable the automated analysis of the SAS Rapid
Predictive Modeler; these functions include the Input Data Node, the Transform Node, the Score Node, the Metadata
Node, and the Reporter node. These changes also directly benefit SAS Enterprise Miner users.
The basic, intermediate, and advanced model processes were developed based on numerius tests with customer
data and simulations of various known modeling scenarios. The basic model process developed includes the
following steps implemented as SAS Enterprise Miner process flow diagrams:
1. data summarization and detection of ID, class, and continuous variables 2. imputation of missing values by using SAS Enterprise Miner techniques 10 SAS Global Forum 2010 Customer Intelligence 3. statistical sampling to produce an efficient and representative data set that accounts for rare values and
skewed distributions 4. partitioning rows into training and validation data 5. transformations that reveal nonlinearity and contain the effects of outliers 6. variable selection methods 7. reliable modeling techniques that produce interpretable functions 8. model selection based on statistics that promote generalization You can open SAS Rapid Predictive Modeler projects inside SAS Enterprise Miner to examine these details and try
out modifications and alternatives. REGIST...
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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.
- Summer '10