IBM SPSS Modeler Features
By integrating predictive analytics with decision management, scoring and optimization in your organization's processes and operational systems, SPSS Modeler helps your users and systems make the right decision every time.
Automate and optimize transactional decisions by combining predictive analytics, rules and scoring to deliver recommended actions in real time. Decision management capabilities enable the integration of predictive analytics and business rules into an organization’s processes to optimize and automate high-volume decisions at the point of impact.
Use a variety of modeling approaches in a single run and then compare the results of the different modeling methods. Select which models to use in deployment, without having to run them all individually and then compare performance. Choose from three automated modeling methods: Auto Classifier, Auto Numeric and Auto Cluster.
Go beyond the analysis of structured numerical data and include information from unstructured text data, such as web activity, blog content, customer feedback, emails and social media comments. Capture key concepts, themes, sentiments and trends and ultimately improve the accuracy of your predictive models.
Identity resolution is vital in a number of fields, including customer relationship management, national security, fraud detection and prevention of money laundering. Entity analytics improves the coherence and consistency of data by resolving like entities even when the entities do not share any key values.
Social network analysis examines the relationships between social entities and the implications of these relationships on an individual’s behavior. It is particularly useful for those in telecommunications and other industries concerned about attrition (or churn). By identifying groups, group leaders and whether others will be affected based on influence, predictive models can be built on an individual and enhanced with their group and social behavior data.
Geospatial analytics explore the relationship between data elements that are tied to a geographic location. When combined with current and historical data, information such as latitude and longitude, postal codes and addresses can reveal deeper insights about people and events and improve predictive accuracy. Geospatial analytics is frequently used in fields such as disease surveillance, law enforcement and building and facilities management.
Deployment bridges the gap between analytics and action by providing results to people and processes on a schedule or in real time, and enables organizations to realize the full benefit of predictive analytics. SPSS Modeler streams can be embedded as predictive services to support better decision-making in areas such as marketing, pricing and fraud detection. SPSS Modeler Gold streams can also be deployed as scenarios for the purposes of model refresh, automated job scheduling, or use by other predictive applications. In addition, SPSS Modeler Gold on Cloud removes the overhead and expense of onsite deployments for agile analytics.
More reasons to use SPSS Modeler
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Discover hidden insights in geospatial data
Use location and geographic information to enhance your predictive models in SPSS Modeler.