Download - Go Predictive Analytics
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
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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
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