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Bruce Kolodziej
Analytics Sales Manager
Predictive Analytics and WebFOCUS RStat Overview
April 14, 2011
Copyright 2007, Information Builders. Slide 2
Agenda
Predictive Analytics (PA) OverviewRelationship of PA to Business Intelligence What is a Predictive Model and What are the Best
Practices for PA?WebFOCUS RStat Value PropositionVertical Applications of PARStat DemonstrationPA SummaryWhy RStat?Q&A
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”
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?
Rea
r V
iew
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?
Fo
rwar
d V
iew
Note: Adapted from “Competing on Analytics”
Copyright 2007, Information Builders. Slide 5
Predictive Analytics & Business Intelligence
Business Intelligence User driven Rear view Manual methodsAll attributes are
equally important Reportable info Top-down Experience-driven
Predictive Analytics Data driven Forward view Automated methodsA few attributes are
the keys Actionable info Bottoms-up Data-driven
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
Business Intelligence with Predictive Analytics
Copyright 2007, Information Builders. Slide 7
Business Intelligence + Predictive Modeling = 145% ROI
Business Intelligence = 89% ROI
Median ROI
Source: “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”
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
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
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 RStatPredictive Analytics
Open Integrated with WebFOCUSDeploys 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
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
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
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
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
Law Enforcement Applications of PA
Crime predictions Enhance resource allocation to minimize crime
occurrencesMinimize costs by deploying resources more
effectively Provide actionable, predictive information to the
front lines
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
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
WebFOCUS Dashboard Displaying Predictive OutputGIS, active report and graphical output of predicted responses to a marketing campaign
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
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
Wrap-up
Thank you for your time today! For additional information or if you have any questions, please contact
Bruce Kolodziej, Analytics Manager [email protected] 917.968.6035 Or contact your local Information Builders Account Executive