Original definition Predictive Analytics SPSS Jan 15, 2003 Intriduction Slides
- 1. Copyright 2003, SPSS Inc.Copyright 2003, SPSS Inc. 11 Predictive Analytics: Defined Matt Cutler Vice President, Corporate Marketing January 15, 2003
- 2. Objective Overall impression Term is widely used both internally and externally Market has little common agreement around exactly what the term means Definition The 5 C’s of Corporate Communications: clear, concise, compelling, credible, and coherent
- 3. Approach Value statement One sentence that communicates the core value of Predictive Analytics. Definition: Several paragraphs Four paragraphs of detailed, dense content that cover all of the facets Predictive Analytics
- 4. Value Statement Predictive analytics connects data to effective action by drawing reliable conclusions about current conditions and future events.
- 5. Definition (Lots Here) Predictive analytics, like enterprise resource planning (ERP) and customer relationship management (CRM), is both a business process and a set of related technologies. Predictive analytics leverages an organization’s business knowledge by applying sophisticated analytic techniques to enterprise data. The resulting insights can lead to actions that demonstrably change how people behave as customers, employees, patients, students, and citizens. The predictive analytics process begins by exploring how specific business issues relate to data describing people’s characteristics, attitudes, and behavior. These numeric and free-form data sets, which originate from both internal systems and third party providers, are cleansed, transformed, and evaluated using statistical, mathematical, and other algorithmic techniques. These techniques generate models for classification, segmentation, forecasting, pattern recognition, sequence and association detection, anomaly identification, profiling, propensity scoring, rule induction, text mining, and advanced visualization. Combining predictive analytic models with organizational business knowledge provides insight into such critical issues as customer acquisition and retention, up-selling and cross-selling, fraud detection, and outcome improvement. Through measuring uncertainty surrounding these issues, predictive analytics enables proactive risk management, refining key decision making processes through controlled, iterative testing of potential actions and their likely intended—and unintended—consequences. These findings and their corresponding business rules can then be deployed within front-line operational systems to identify new revenue opportunities, measurable cost savings, repeatable process improvements, and sustainable competitive advantages. Predictive analytics carries strategic and tactical ramifications for organizations that recognize the inherent value locked within their existing enterprise data. Strategically, predictive analytics provides a quantitative foundation for rapidly identifying, objectively evaluating, and confidently pursuing new market opportunities. Tactically, predictive analytics identifies precisely whom to target, how to reach them, when to make contact, and what messages should be communicated. Top Level OverviewTop Level Overview Data & AnalysisData & Analysis Applications & ImpactApplications & Impact Major RamificationsMajor Ramifications
- 6. Definition: 1st Paragraph Top Level Overview Predictive analytics, like enterprise resource planning (ERP) and customer relationship management (CRM), is both a business process and a set of related technologies. Predictive analytics leverages an organization’s business knowledge by applying sophisticated analytic techniques to enterprise data. The resulting insights can lead to actions that demonstrably change how people behave as customers, employees, patients, students, and citizens.
- 7. Definition: 2nd Paragraph Data & Analysis The predictive analytics process begins by exploring how specific business issues relate to data describing people’s characteristics, attitudes, and behavior. These numeric and free-form data sets, which originate from both internal systems and third party providers, are cleansed, transformed, and evaluated using statistical, mathematical, and other algorithmic techniques. These techniques generate models for classification, segmentation, forecasting, pattern recognition, sequence and association detection, anomaly identification, profiling, propensity scoring, rule induction, text mining, and advanced visualization.
- 8. Definition: 3rd Paragraph Applications & Impact Combining predictive analytic models with organizational business knowledge provides insight into such critical issues as customer acquisition and retention, up-selling and cross-selling, fraud detection, and outcome improvement. Through measuring uncertainty surrounding these issues, predictive analytics enables proactive risk management, refining key decision making processes through controlled, iterative testing of potential actions and their likely intended—and unintended—consequences. These findings and their corresponding business rules can then be deployed within front-line operational systems to identify new revenue opportunities, measurable cost savings, repeatable process improvements, and sustainable competitive advantages.
- 9. Definition: 4th Paragraph Major Ramifications Predictive analytics carries strategic and tactical ramifications for organizations that recognize the inherent value locked within their existing enterprise data. Strategically, predictive analytics provides a quantitative foundation for rapidly identifying, objectively evaluating, and confidently pursuing new market opportunities. Tactically, predictive analytics identifies precisely whom to target, how to reach them, when to make contact, and what messages should be communicated.
- 10. JB BigWig & Wolfgang Stats Predictive analytics, like enterprise resource planning (ERP) and customer relationship management (CRM), is both a business process and a set of related technologies. Predictive analytics leverages an organization’s business knowledge by applying sophisticated analytic techniques to enterprise data. The resulting insights can lead to actions that demonstrably change how people behave as customers, employees, patients, students, and citizens. The predictive analytics process begins by exploring how specific business issues relate to data describing people’s characteristics, attitudes, and behavior. These numeric and free-form data sets, which originate from both internal systems and third party providers, are cleansed, transformed, and evaluated using statistical, mathematical, and other algorithmic techniques. These techniques generate models for classification, segmentation, forecasting, pattern recognition, sequence and association detection, anomaly identification, profiling, propensity scoring, rule induction, text mining, and advanced visualization. Combining predictive analytic models with organizational business knowledge provides insight into such critical issues as customer acquisition and retention, up-selling and cross-selling, fraud detection, and outcome improvement. Through measuring uncertainty surrounding these issues, predictive analytics enables proactive risk management, refining key decision making processes through controlled, iterative testing of potential actions and their likely intended—and unintended—consequences. These findings and their corresponding business rules can then be deployed within front-line operational systems to identify new revenue opportunities, measurable cost savings, repeatable process improvements, and sustainable competitive advantages. Predictive analytics carries strategic and tactical ramifications for organizations that recognize the inherent value locked within their existing enterprise data. Strategically, predictive analytics provides a quantitative foundation for rapidly identifying, objectively evaluating, and confidently pursuing new market opportunities. Tactically, predictive analytics identifies precisely whom to target, how to reach them, when to make contact, and what messages should be communicated. Business CaseBusiness Case Technology SpecificsTechnology Specifics Working TogetherWorking Together Organizational ImpactOrganizational Impact
- 11. Definition & SPSS Technology Statistics offerings Data mining & text mining offerings Web analytics offerings Market research offerings OLAP, reporting & visualization offerings
- 12. Predictive Analytics: Defined Predictive analytics connects data to effective action by drawing reliable conclusions about current conditions and future events.