Bruce Kolodziej

Analytics Sales Manager

Predictive Analytics and WebFOCUS RStat Overview

April 14, 2011

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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”

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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

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Business Intelligence + Predictive Modeling = 145% ROI

Business Intelligence = 89% ROI

Median ROI

Source: “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”

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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

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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

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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

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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

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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

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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 Bruce_Kolodziej@ibi.com 917.968.6035 Or contact your local Information Builders Account Executive