IBM SPSS Modeler
IBM SPSS Modeler can rapidly provide ROI and enables organisations to proactively and repeatedly reduce costs while increasing productivity.
IBM SPSS Modeler is a powerful data mining and text analytics platform that is designed to build predictive models and bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise. SPSS Modeler scales from desktop deployments to integration with operational systems to provide users with a range of advanced algorithms and techniques without unnecessary complexity in data transformations. The visual interface of SPSS Modeler allows users to leverage statistical and data mining algorithms without programming.
SPSS Modeler is available in two editions:
Both editions are available in desktop and server configurations.
More functionality is available in IBM SPSS Modeler if IBM SPSS Statistics is purchased. Data prepared in IBM SPSS Statistics can be exported to IBM SPSS Modeler, where the statistical procedures that are available in IBM SPSS Statistics can be performed. Models that are created using the predictive model markup language (PMML) can also be shared easily between IBM SPSS Modeler and IBM SPSS Statistics
What can IBM SPSS Modeler be used for?
IBM SPSS Modeler can be used to build data-driven predictive models for risk management: measuring, tracking and computing risk as a core process. Predictive modeling improves on standard actuarial methods by incorporating additional analytical automation, and by generalizing to a broader set of customer variables. By using predictive modeling the enterprise can rank customers by level of risk, manage risk more precisely, effectively transforming risk into opportunity. OLSPS Analytics has developed Claims Segmentation Solution powered by IBM SPSS Moldeler.
Customer Analytics and Customer Relationship Management
By using SPSS Modeler and predictive analytics the enterprise learns from its cumulative experience (data) about customer response (or lack thereof), purchase decisions, acquisitions, outright defection, acts of fraud, credit defaulting, complaints of a faulty product component, etc. Predictive analytics taps this rich vein of experience, mining it to generate predictive models. Core analytical methods maximize model performance by tuning across training data. In this way, model generation is an act of learning from the experience encoded in data.
Predictive Models designed with SPSS Modeler can attain 7 strategic objectives of any organisation
SPSS Modeler uses enterprise data to generate predictive models across business units of the organization, including marketing, sales, fraud detection, the call center and core business capacity (e.g., product assembly). Predictive Models can attain following strategic objectives of any organization:
Seven strategic objectives an enterprise can achieve with IBM SPSS Modeler
|1.Compete||Secure the most powerful and unique competitive stronghold|
|2.Grow||Increase sales and retain customers competitively|
|3.Enforce||Maintain business integrity by managing fraud|
|4.Improve||Advance your core business capacity competitively|
|5.Satisfy||Meet today’s escalating consumer expectations|
|6.Learn||Employ today’s most advanced analytics|
|7.Act||Render business intelligence and analytics truly actionable|
Who typically uses IBM SPSS Statistics Modeler?
SPSS Modeler has been used in these and other industries:
- Customer analytics and customer relationship management (CRM)
- Fraud detection and prevention
- Optimizing insurance claims
- Risk management manufacturing quality improvement
- Healthcare quality improvement
- Forecasting demand or sales
- Law enforcement and border security
- Academic Institutions
- Telecommunication companies
- Predicting movie box office receipts
Applications like these can rapidly provide ROI, and can enable organizations to proactively and repeatedly reduce costs while increasing productivity.