Go Predictive Analytics, LLC is a premier data mining and predictive analytics consulting company. We remove the barriers that loom large with creating and deploying data mining solutions for high ROI.

1. 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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2. 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
Saved Time &
Predict and Target Markets
Money while
Create Market Segments Improving Sales
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
2

3. 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
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
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4. What is predictive analytics
Wikipedia: 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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5. 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)
1John 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
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6. Some Applications
Cross-sell/Upsell Campaign
Customer Acquisition Forecasting
Attrition/Churn/Retain Fraud Detection
Promotions Pricing
Demand Planning Customer Service
Quality Improvement Surveys
Supply Chain Others
50%
47%46%
41%41%40% 40%
32% 31%
30% 30% 30%
26% 25%
20%
18%
17%
10%
12%
0%
Based on 167 respondents who have implemented predictive analytics. Respondents could select multiple
answers, Eckerson, page 6
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7. Predictive Analytics
in Practice
Fully Implemented Partially Implemented
Under Development No Plans
Exploring
6% High Value, Low Penetration:
With stellar credentials, the perplexing thing
15% about predictive analytics is why so many
organizations have yet to employ it.
45% According to research, only 21% of
organizations have “fully” or “partially”
implemented predictive analytics, while 19%
19% have a project “under development” and a
whopping 61% are still “exploring” the issue
or have “no plans.” (Eckerson, page 4)
16%
Based on 833 respondents to a TDWI survey
conducted August 2006
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8. 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

9. Barriers ~ Complexity
High
Prediction
(What might Predictive
Happen) Analytics
Complexity
Monitoring
(What is Dashboards
Happening)
Analysis
(Why did it Visualization
Happened) tools
Reporting Query,
(what reports,
Happened) Search tools
Low
Value High
Value = Savings($ and time)+ Sales / Investment
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10. Barriers ~ Time
hours days
weeks 1-3 months Proprietary Models,
4-6 months 7-12 months Scripts, & Code
no idea Reduce Time
In Model Creation,
Testing, Validation,
Scoring, and Deploying
2% 4%
2%
9% 14%
0% 10% 20% 30%
Project Definition
Data Exploration
Data Preparation
34% Model creation, testing, validation
Scoring & Deploying
34% Managing
Other
Based on 163 respondents, Eckerson, page 15
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
Experience & Partnering
Reduces Time
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11. Barriers ~ Pricing
Staff Software
Hardware External Services
Other
5%
10%
15% Annual
Investment
85%
Internal
15%
External
Investment Investment
Most Companies $600,000 $510,000 $90,000
50% Companies with High Value Programs $1,000,000 $850,000 $150,000
20%
Median numbers are based on 166 respondents whose groups have implemented
predictive analytics, Eckerson, page 10
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12. Partnering with Us Reduces these
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
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13. Creating a Win-Win
Partnership
Modeling,
Project Data Data Testing, & Managing
Definition Exploration Preparation Deployment
Validation
Experience Experience
Expertise in Matters... Matters... Proprietary
Systems Help you Leverage the R Coded We Create We Manage,
Engineering, explore your Best Prediction The Right Protect,&
Science, transaction, Technologies Models & Deployment Update Your
Decision Demographic, to Initially Data Method for Information,
Making, & Polling, Prepare Your Selection Your Needs... Data, and
thinking guide Generalized, Data & Save Methods
you to define Freeing Your Models
Contact, Time Create Network and
measurable & Survey, Partnering Customized Systems from We Value
outcome Psych, & Web Matters... Models with Clogging and Discretion
based data For Proprietary Excellent Slowing and Privacy
Business Viable Data Generalization
Metrics Modeling Selection Characteristics
Variables Methods
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14. A Partnership Between U.S. Army Recruiting Command,
Army Research Institute,
Personnel Decisions Research Institute, & us*
Random Forest Model Predicted GWR vs GWR
5
4
Gross Write Rate
3
Gross Write Rate
2
1 GWR = -0.7345 + 1.6438GWR.Hat
R-Square = 0.9648
Adjusted R-Square = 0.9648
0
0 1 2 3 4 5
Predicted Gross Write Rate
* 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)
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15. The Return on
Investment
Year 1 2 3 4 5
Discount Factor 0.91 0.83 0.75 0.68 0.62
Benefits
Increased Revenue
Decreased Costs $12,000,000 $8,000,000 $4,000,000 $0 $0
Annual Benefits $12,000,000 $8,000,000 $4,000,000 $0 $0
Present Value (Benefits) $10,909,091 $6,611,570 $3,005,259 $0 $0
Costs
One-Time Costs $160,000 $0 $0 $0 $40,000
Recurring Costs $2,000 $2,000 $2,000 $2,000 $2,000
Annual Costs $162,000 $2,000 $2,000 $2,000 $42,000
Present Value (Costs) $147,273 $1,653 $1,503 $1,366 $26,079
Net Value
Annual Net Value $11,838,000 $7,998,000 $3,998,000 -$2,000 -$42,000
Cumulative Net Value $11,838,000 $19,836,000 $23,834,000 $23,832,000 $23,790,000
Net Present Value $10,761,818 $6,609,917 $3,003,757 -$1,366 -$26,079
Annual ROI 7,307% 399,900% 199,900% -100% -100%
Increase Present Value of Return on Investment 11,440%
ROI doesn’t
Net Present Value $20,348,047
include these
0%
your company’s
Internal Rate of Return
Other Benefits:
1) Less Personnel Turnover
2) Less Workforce Stress
GPA! PV ROI = sum of net present value ÷ sum of
present value of costs
3) More Job Satisfaction
4) Better Skilled Sales
Force
NPV = sum of annual net present values 5) More
IRR = The discount rate that yields an NPV of 0 Production
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16. Your Questions?
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17. Where Do We Go From Here...
Are You Ready To Earn Higher
Returns on Your Data?
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18. Contact Information
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 at
http://www.zoominfo.com/Search/PersonDetail.aspx?PersonID=1110698208&searchSource=basic_ssb&singleSearchBox=john+b
+halstead&personName=john+b+halstead
Vitae Available Upon Request
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