1. Bruce Kolodziej Analytics Sales Manager Predictive Analytics and WebFOCUS RStat Overview April 14, 2011

2. Copyright 2007, Information Builders. Slide 2 Agenda  Predictive Analytics (PA) Overview  Relationship of PA to Business Intelligence  What is a Predictive Model and What are the Best Practices for PA?  WebFOCUS RStat Value Proposition  Vertical Applications of PA  RStat Demonstration  PA Summary  Why RStat?  Q&A

3. What is Predictive Analytics? Predictive Analytics (PA) helps one to…  Discover/understand what’s going on  Predict what’s going to happen  Improve overall decision making  Improve business processes  Create a competitive edge! Predictive Analytics IS a key business process…  “Learning from experience”  Not new  User-centric, interactive  Leverages analysis technologies and computing power  Keeps the focus on the business issue  An information-based approach to decision making  Results are mainly used in a forward-looking style  “Next Gen BI”

4. Extending Business Intelligence with Predictive Analytics Degree of Intelligence Standard Reports Ad Hoc Reports Query/Drill Down KPIs/Alerts What happened? How many, how often, where? Where exactly is the problem? What actions are needed? Rear View Statistical Analysis Forecasting/Extrapolation Predictive Modeling Optimization Why is this happening? What if these trends continue? What will happen next? What is the best that can happen? Forward View Note: Adapted from “Competing on Analytics”

5. Copyright 2007, Information Builders. Slide 5 Predictive Analytics & Business Intelligence Business Intelligence  User driven  Rear view  Manual methods  All attributes are equally important  Reportable info  Top-down  Experience-driven Predictive Analytics  Data driven  Forward view  Automated methods  A few attributes are the keys  Actionable info  Bottoms-up  Data-driven

6. Predictive Analytics & Business Intelligence Business Intelligence  Reports, metrics, dashboards up to this point in time  User-driven to explore data and interpret results  Based on experience and gut-feel Predictive Analytics  Automatically discover important patterns  Learn from historical data and create predictive models  Consistent, objective, efficient, fact-based Deploying Predictive Models  Leverage current and historical data  Make predictions on current and future cases  Deploy as business decisions to enhance outcomes Reactive Proactive

7. Business Intelligence with Predictive Analytics Copyright 2007, Information Builders. Slide 7 Business Intelligence + Predictive Modeling = 145% ROI Business Intelligence = 89% ROI Business Intelligence + Predictive Modeling = 145% ROI Business Intelligence = 89% ROI Median ROI Source: “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”

8. Copyright 2007, Information Builders. Slide 8 Predictive Analytics 101  I have a variety of data (transactions, demographics, offers, responses, accounts, policies, claims, from a variety of sources)  I’d like to predict the likely future behavior of a customer  I use historic data that has examples of that behavior Age Education Marital Gender Occupation Historic Response to Offer 21 College Single Male Engineer Yes 23 HSgrad Single Male Administrator No 29 HSgrad Married Female Bus. Owner Yes  Build a model (find the patterns) then use the model to predict that behavior for new records Age Education Marital Gender Occupation Predicted Response to Offer 24 HSGrad Married Male Engineer No 27 College Single Female Bus. Owner Yes 31 PhD Married Male Bus. Owner Yes

9. Copyright 2007, Information Builders. Slide 9 Predictive Analytics Best Practices  Focus on bottom-line business initiatives  Revenue generating or cost saving  Data access / preparation & deployment of results are crucial  Usually this is the majority of the effort  Ensure the model provides better decisions than the current approach to that decision  Model evaluation should not focus on the statistical performance  Take a total cost of ownership and value proposition approach to PA  Why pay for techniques that may not be used or a solution that has a steep learning curve

10. Copyright 2007, Information Builders. Slide 10  Leverages widely available statistical models to improve decision making  Decisions based on high probability – NOT “gut- feel”  Makes building “scoring” systems easy  Enables predictive applications at a fraction of the cost of other solutions  Based on “R” open source system Business Value: By binding predictive analytics with WebFOCUS you can embed high probability directions, scores and expected outcomes into frontline operational processes, improving returns. WebFOCUS RStat Predictive Analytics

11.  Open  Integrated with WebFOCUS  Deploys results to non-technical, business end users automatically, where decisions are being made  Allows for easy data access and data preparation  Single server for BI and PA, eliminating additional software and maintenance costs  Low Total Cost of Ownership  Eliminates some or all statistical software licensing costs  Organizations pay only usage and support  R language is not required for deployment  In contrast, a third-party scoring engine would require additional servers adding maintenance and licensing costs  Why pay for techniques you may never use? WebFOCUS RStat Value Proposition

12.  Usability  User-friendly interface  Advanced analytics without coding or syntax  Good exploratory and graphing capabilities  Extends very broadly with R package  2000 packaged extensions provides instant access to more models and techniques than any other statistical software  Contains the most commonly used predictive and exploratory modeling techniques from the fields of data mining and statistics  Both exploratory and predictive modeling capabilities  Quick Time to Market  Openness, low TCO and usability combine for a quick time to market and high value for our customers WebFOCUS RStat Value Proposition

13. Financial Services Applications of PA  Growth  Acquisition targeting  Organic growth  Cross selling, up selling, retention (churn)  Promotion targeting  Who to target, which offer, which channel, what time  Customer segmentation  Groupings of like customers  Predicting customer lifetime value  Profitability  Inter-department analysis of promoting products to low-risk customers  Collections and recovery  Managing risk  Credit approvals  Predicting credit risk  Anti-money laundering  Fraud detection / prevention

14. Insurance Applications of PA  Growth  Acquisition targeting  Organic growth  Cross selling, up selling, retention (churn)  Customer segmentation  Groupings of like customers  Predicting customer lifetime value  Promotion targeting  Who to target, which offer, which channel, what time  Profitability  Inter-department analysis of promoting products to low-risk customers  Managing risk  Pricing / underwriting of policies  Predicting claim risk and severity  Fraudulent claim detection / prevention  Claims processing  Claim to agent routing  Fast tracking claims

15. Telecommunications Applications of PA  Growth  Acquisition targeting  Organic growth  Cross-selling, up-selling, retention (churn)  Customer segmentation  Groupings of like customers  Promotion targeting  Who to target, which offer, which channel, what time  Predicting customer lifetime value  Profitability  Inter-department analysis of promoting products to low-risk customers  Collections and recovery  Managing risk  Predicting credit risk  Fraud detection / prevention

16. Law Enforcement Applications of PA  Crime predictions  Enhance resource allocation to minimize crime occurrences  Minimize costs by deploying resources more effectively  Provide actionable, predictive information to the front lines

17. Government Applications of PA  Child Welfare  Match children with foster parents  Social Security  Score disability claims for fast processing  Tax Collection  Target past-due tax collections  Customs  Identify risky cargo containers for inspections  Medicare/Medicaid  Detect fraudulent claims & providers  Eligibility decisions  Armed Forces  Predict success rates during recruitment and re-enrollment  Predict troop allocation Copyright 2010, Information Builders. Slide 17

18. WebFOCUS RStat Demonstration  Walk through the RStat interface  Demo scenario of targeting customers with an offer  Using attributes of age, gender, marital status, occupation, income and education  We’ll build a model to uncover the patterns related to responders and non-responders historically  Then apply the model to a new data set to predict future responders and non-responders  Assists an organization with targeting their offers efficiently and cost-effectively  Focus on ease of use, broad range of capabilities and easy deployment of predictive results to end-users

19. WebFOCUS Dashboard Displaying Predictive Output GIS, active report and graphical output of predicted responses to a marketing campaign

20. Copyright 2007, Information Builders. Slide 20 Predictive Analytics Summary Organizations use predictive analytics to:  Reduce marketing/operational costs  Increase sales  Reduce defects  Improve site location  Increase web site profitability  Improve cross-sell/up-sell campaigns  Increase retention/loyalty  Detect and prevent fraud  Identify credit risks  Acquire new customers  Improve assortment planning ROI is realized when:  Decision-making is improved with forward-looking views of likely behavior  Results are widely-distributed to end users where decisions are made

21. Copyright 2007, Information Builders. Slide 21 Why WebFOCUS RStat? Summary of Differentiators  Integrated Solution  Data access and preparation, business intelligence, predictive model building and deployment of results all in one integrated platform  Historical, present and future views  Cost Effective  Based on open-source R, RStat is the best value on the market  Contains the most commonly used techniques  Why pay for techniques that will rarely, if ever, be used?  If another technique is needed, the R language is equipped  Predictive Analytics and Statistical Analysis Together  Covers a wide variety of business objectives and data sources  RStat is a General Purpose Analytic Solution  Not a niche product for risk or fraud or churn or quality or cross-selling analysis. RStat is all of these = maximum value and ROI

22. Wrap-up  Thank you for your time today!  For additional information or if you have any questions, please contact  Bruce Kolodziej, Analytics Manager  Bruce_Kolodziej@ibi.com Bruce_Kolodziej@ibi.com  917.968.6035  Or contact your local Information Builders Account Executive