Predictive Analytics:Using Your Data and Our

Technology to Add

Value to Your Organization G o P r e d i c t i v e A n a l y t i c s , L L C

P r e d i c t i v e A n a l y t i c s , S y s t e m s E n g i n e e r i n g , & O p e r a t i o n s R e s e a r c h

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Why the Interest in Predictive Analytics

Personal interest began when I significantly contributed to the U.S. Army’s Recruiting Command mission success in Marketing, Strategic Concepts, and Strategic Planning positions:

Was a member of the marketing team that changed “Be All You Can Be” to “An Army of One”

Quickly understood that Recruiting Command had megabytes of data, which enabled a skilled analysts to:

Predict a Recruiter’s sales success

Predict and Target Markets

Create Market Segments

Predict contacts that transformed into successful contracts

Motivated Doctoral research at the University of Virginia to improve generalization in data mining and business intelligence models (created a library of proprietary models, R-code, and scripts)

Over 15 years experience in leading analytical research teams with diverse partnerships on innovating projects that have created value

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Saved Time & Money while

Improving Sales

Why the Interest in Predictive Analytics

Walmart used their data and discovered that prior to hurricanes landing on shore customers bought flashlights, batteries, ... and Pop-Tarts (cross sales)1

A Swiss telecom reduced customer defections (churning) from 20% to 5% using predictive analytics 1

Best Buy discovered that 7% of its customers account for 43% of its sales (target marketing)1

The Royal Shakespeare Company used seven years of customer transaction data to increase regular visits by 70% (marketing) 1

Predictive analytics is transforming health care... “you can’t see it (emerging symptoms) with the naked eye, but a computer can” Dr. Carolyn McGregor, University of Ontario 1

A major Canadian bank uses predictive analytics to increase campaign response rates by 600%, cut customer acquisition costs in half, and boost campaign ROI by 100% 2

Airlines increase revenue and customer satisfaction by better estimating the number of passengers who won’t show up for a flight 2

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1 The Economist, The Data Deluge, “Data, data everywhere”, February 27, 2010, pages 3-5 2 Wayne W. Eckerson, Predictive Analytics: Extending the Value of Your Data Warehousing Investment, TDWI Best Practices Report, 2007, page 6

What is predictive analyticsWikipedia: Predictive analytics encompasses a variety of techniques from statistics, data mining, and game theory that analyze current and historical facts to make predictions about future events

Deloitt: Predictive analytics is a set of statistical tools and technology that uses current and historic data to predict future behavior and these techniques can be applied across different industry sectors

WiT: Predictive Analytics is the ability to predict the future through deep analysis of historical trends and hidden relationships within organizational data. Predictive Analytics is not about peering into a crystal ball, but rather, using technology and tested algorithms to identify data relationships that influence likely outcomes

TDWI: Predictive analytics is an arcane set of techniques and technologies that bewilder many business and IT managers. It stirs together statistics, advanced mathematics, and artificial intelligence and adds a heavy dose of data management to create a potent brew that many would rather not drink!

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Go Predictive Analytic’s Definition

Predictive analytics discovers a useful function approximation to the real function that underlies the predictive relationship (or pattern) between the variables and the response 1

We discover the best functional approximation with its estimated parameters (or rules) to best predict the response with the least amount of error with your data 1

Two types of function approximation models:

Supervised: Use a random training set of data and withholds random test data set(s) for accuracy measurements and improvements (Neural Networks, SVM, Random Forest)

Unsupervised: Use all the data to describe like members (clustering and other multivariate statistical distance methods)

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1 John B. Halstead, Recruiter Selection Model and Implementation Within the United States Army, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 39, NO. 1, JANUARY 2009, pages 93-100

Some Applications

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0%

10%

20%

30%

40%

50%47%46%

41%41%40%

32%31% 30% 30%26% 25%

18% 17%

12%

Cross-sell/Upsell CampaignCustomer Acquisition ForecastingAttrition/Churn/Retain Fraud DetectionPromotions PricingDemand Planning Customer ServiceQuality Improvement SurveysSupply Chain Others

Based on 167 respondents who have implemented predictive analytics. Respondents could select multiple answers, Eckerson, page 6

Predictive Analytics in Practice

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High Value, Low Penetration: With stellar credentials, the perplexing thing about predictive analytics is why so many organizations have yet to employ it. According to research, only 21% of organizations have “fully” or “partially” implemented predictive analytics, while 19% have a project “under development” and a whopping 61% are still “exploring” the issue or have “no plans.” (Eckerson, page 4)

6%

15%

19%

16%

45%

Fully Implemented Partially ImplementedUnder Development No PlansExploring

Based on 833 respondents to a TDWI survey conducted August 2006

Predictive Analytics’ Barriers

Complexity: traditionally, developing sophisticated models is a slow, iterative, and labor intensive process

Time: same as above

Data: many corporate data contain errors and inconsistencies; yet most predictive models require clean, scrubbed, expertly formatted data to work

Processing Expense: complex analytics and scoring processes clog networks and slow system performance

Expertise: qualified predictive analysts who can create sophisticated and accurate models are hard to find, expensive to pay, and difficult to retain

Pricing: the price of most predictive analytic software and the required hardware is often beyond the reach of most midsize organizations and departments in large organizations

Barriers ~ Complexity

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Value = Savings($ and time)+ Sales / Investment

Value

Co

mplexit

y

Low

High

High

Prediction(What might

Happen)

Predictive Analytics

Monitoring(What is

Happening)Dashboards

Analysis(Why did it Happened)

Visualization tools

Reporting(what

Happened)

Query, reports,

Search tools

Barriers ~ Time

10

4%

14%

34%

34%

9%2%2%

hours daysweeks 1-3 months4-6 months 7-12 monthsno idea

Experience & Partnering Reduces Time

0% 10% 20% 30%Project Definition

Data Exploration

Data Preparation

Model creation, testing, validation

Scoring & Deploying

Managing

Other

Percentage of time groups spend on each phase in a predictive analytics project. Averages don’t equal 100% because respondents wrote a number for each phase. Based on 166 responses, Eckerson, page 12

Based on 163 respondents, Eckerson, page 15

Proprietary Models, Scripts, & Code

Reduce TimeIn Model Creation, Testing, Validation,

Scoring, and Deploying

Barriers ~ Pricing

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50%

20%

15%

10%5%

Staff SoftwareHardware External ServicesOther

Median numbers are based on 166 respondents whose groups have implemented predictive analytics, Eckerson, page 10

Annual Investment

85% Internal

Investment

15% External

Investment

Most Companies

Companies with High Value Programs

$600,000 $510,000 $90,000

$1,000,000 $850,000 $150,000

Partnering with Us Reduces these Barriers

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Complexity: traditionally, developing sophisticated models is a slow, iterative, and labor intensive process

Time: same as above

Data: many corporate data contain errors and inconsistencies; yet most predictive models require clean, scrubbed, expertly formatted data to work

Processing Expense: complex analytics and scoring processes clog networks and slow system performance

Expertise: qualified predictive analysts who can create sophisticated and accurate models are hard to find, expensive to pay, and difficult to retain

Pricing: the price of most predictive analytic software and the required hardware is often beyond the reach of most midsize organizations and departments in large organizations

Creating a Win-Win Partnership

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Project Definition

Data Exploration

Data Preparation

Modeling, Testing, & Validation

Deployment Managing

Expertise in Systems

Engineering, Science, Decision Making, &

thinking guide you to define measurable &

outcome based

Business Metrics

Experience Matters... Help you

explore your transaction, Demographic,

Polling, Generalized,

Contact, Survey,

Psych, & Web data For

Viable Modeling Variables

Experience Matters...

Leverage the Best

Technologies to Initially

Prepare Your Data & Save

TimePartnering Matters...

Proprietary Data

Selection Methods

Proprietary R Coded

Prediction Models &

Data Selection MethodsCreate

Customized Models with Excellent

Generalization Characteristics

We Create The Right

Deployment Method for

Your Needs... Freeing Your Network and Systems from Clogging and

Slowing

We Manage, Protect,&

Update Your Information,

Data, and Models

We Value Discretion

and Privacy

A Partnership Between U.S. Army Recruiting Command, Army Research Institute,

Personnel Decisions Research Institute, & us*

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* Public Information, which was also published and available at IEEE (John B. Halstead, Recruiter Selection Model and Implementation Within the United States Army, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 39, NO. 1, JANUARY 2009, pages 93-100)

0 1 2 3 4 5

01

23

45

Random Forest Model Predicted GWR vs GWR

Predicted Gross Write Rate

Gro

ss W

rite

Rat

e

GWR = -0.7345 + 1.6438GWR.HatR-Square = 0.9648Adjusted R-Square = 0.9648

Gro

ss W

rite

Rat

e

The Return on Investment

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Year 1 2 3 4 5

Discount Factor

BenefitsIncreased RevenueDecreased Costs

Annual Benefits

Present Value (Benefits)

Costs

One-Time Costs

Recurring Costs

Annual Costs

Present Value (Costs)

Net Value

Annual Net Value

Cumulative Net Value

Net Present Value

0.91 0.83 0.75 0.68 0.62

$12,000,000 $8,000,000 $4,000,000 $0 $0

$12,000,000 $8,000,000 $4,000,000 $0 $0

$10,909,091 $6,611,570 $3,005,259 $0 $0

$160,000 $0 $0 $0 $40,000

$2,000 $2,000 $2,000 $2,000 $2,000

$162,000 $2,000 $2,000 $2,000 $42,000

$147,273 $1,653 $1,503 $1,366 $26,079

$11,838,000 $7,998,000 $3,998,000 -$2,000 -$42,000

$11,838,000 $19,836,000 $23,834,000 $23,832,000 $23,790,000

$10,761,818 $6,609,917 $3,003,757 -$1,366 -$26,079

Annual ROI 7,307% 399,900% 199,900% -100% -100%

Present Value of Return on Investment

Net Present Value

Internal Rate of Return

11,440%

$20,348,047

0%

PV ROI = sum of net present value ÷ sum of present value of costs

NPV = sum of annual net present valuesIRR = The discount rate that yields an NPV of 0

ROI doesn’t include these

Other Benefits:1) Less Personnel Turnover

2) Less Workforce Stress3) More Job Satisfaction4) Better Skilled Sales

Force5) More

Production

Increase your company’s

GPA!

Your Questions?16

Where Do We Go From Here...Are You Ready To Earn Higher

Returns on Your Data?17

Contact Information

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Dr. John B. Halstead, Ph.D.

757.810.4008

jbhalstead@gopredictiveanalytics.com

Bio at http://www.linkedin.com/pub/john-halstead/7/3a1/b87

Additional Information athttp://www.zoominfo.com/Search/PersonDetail.aspx?PersonID=1110698208&searchSource=basic_ssb&singleSearchBox=john+b+halstead&personName=john+b+halstead

Vitae Available Upon Request