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SPSS Modeler Social Network Analysis addresses this problem by processing relationship information into additional fields that can be included in models. These derived key performance indicators measure social characteristics for individuals. Combining these social properties with individual-based measures provides a better overview of individuals and consequently can improve the predictive accuracy of your models.

Many approaches to modeling behavior focus on the individual. They use a variety of data about individuals to generate a model that uses the key indicators of the behavior to predict it. If any individual has values for the key indicators that are associated with the occurrence of the behavior, that individual can be targeted for special attention designed to prevent the behavior.

Consider approaches to modeling churn, in which a customer terminates his or her relationship with a company. The cost of retaining customers is significantly lower than the cost of replacing them, making the ability to identify customers at risk of churning vital. An analyst often uses a number of Key Performance Indicators to describe customers, including demographic information and recent call patterns for each individual customer. Predictive models based on these fields use changes in customer call patterns that are consistent with call patterns of customers who have churned in the past to identify people having an increased churn risk. Customers identified as being at risk receive additional customer service or service options in an effort to retain them.

These methods overlook social information that may significantly affect the behavior of a customer. Information about a company and about what other people are doing flows across the relationships to impact people. As a result, relationships with other people allow those people to influence a person’s decisions and actions. Analyses that include only individual measures are omitting important factors having predictive capabilities.

SPSS Modeler Social Network Analysis consists of two primary components:

• SPSS Modeler Social Network Analysis nodes added to the IBM® SPSS® Modeler environment that enable the inclusion of social analytic techniques in streams.

• SPSS Modeler Server Social Network Analysis, which adds processing of the node specifications to IBM® SPSS® Modeler Server. IBM SPSS Modeler Server Social Network Analysis efficiently processes massive amounts of network data, which may include millions of individuals and relationships, into a relatively small number of fields for further analysis.

For example, SPSS Modeler Social Network Analysis identifies the individuals in a network that are most affected by specific people churning. Furthermore, you can discover groups of individuals in a network that are at an increased risk of churn. By incorporating Key Performance Indicators for these effects in your models, you can improve their overall performance.