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« The Five Myths of Predictive Analytics » 1©2011-2012 Aryng.com
The Five Myths of Predictive Analytics
Piyanka JainPresident & CEO, Aryng Inc.
@AnalyticsQueen #PAWCON
email: [email protected]
Aryng.com/whitepaper.html
« The Five Myths of Predictive Analytics » 2©2011-2012 Aryng.com
Analytics Training for
Business Impact
Public Workshop
Onsite Training & Mentoring
Onsite Training & Mentoring
Strategic Analytics
Consulting
Online Training
“Data to Decisions” CoursesBusiness AnalyticsPredictive AnalyticsMarketing AnalyticsGoogle AnalyticsA/B Testing
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« The Five Myths of Predictive Analytics » 3©2011-2012 Aryng.com
Our Clients Include …
« The Five Myths of Predictive Analytics » 4©2011-2012 Aryng.com
Agenda: Predictive Analytics Myths
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« The Five Myths of Predictive Analytics » 5©2011-2012 Aryng.com
MYTH #5
“It’s the new kid in town!”
« The Five Myths of Predictive Analytics » 6©2011-2012 Aryng.com
When was the first application of Predictive Analytics?
A. 1960’s – Advent of computing powerB. 1930’s – Wall street crashC. 19th century – Birth of science as professionD. Before 15th century
Trivia
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« The Five Myths of Predictive Analytics » 7©2011-2012 Aryng.com
Predictive Analytics Coming of Age
1930 – Econometric society
1965– Computing power with IBM 360
1977 – Oracle’s commercial Relational Database
2000 – 2010: Narrowing of algorithms
2011: PA hits mainstream
First credit score model by
Fisher and Durand
« The Five Myths of Predictive Analytics » 8©2011-2012 Aryng.com
“Arranged Marriage?”
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« The Five Myths of Predictive Analytics » 9©2011-2012 Aryng.com
Astrology Time and Space equation to describe any moment in terms of planets, angles and distances.
Astrology and Mathematics
« The Five Myths of Predictive Analytics » 10©2011-2012 Aryng.com
Mathematical Calculation
36 predictive attributes
to describe a person
Bride: 36 Attributes
Groom: 36 Attributes
Higher the overlapping
attributes, greater the probability of
good match
Predictive Model and Arranged Marriages
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« The Five Myths of Predictive Analytics » 11©2011-2012 Aryng.com
MYTH #4
Perfect!
« The Five Myths of Predictive Analytics » 12©2011-2012 Aryng.com
Even the best model has tons of misclassification
Trying to predict “greens”
11 3
2 13
Green
Red
Green Red
Actual
Pre
dict
ed
Models Are Not Perfect
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« The Five Myths of Predictive Analytics » 13©2011-2012 Aryng.com
Model predicts by looking at likely-hood
Income > $40,000
Renter High Debt
Bad Risk Good Risk Bad Risk Good Risk
Yes
Yes No
No
Yes No
Model Prediction Is an Estimation
« The Five Myths of Predictive Analytics » 14©2011-2012 Aryng.com
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« The Five Myths of Predictive Analytics » 15©2011-2012 Aryng.com
Is always the best
technique!
MYTH #4
Predictive Analytics and Testing 16Copyright ©2011-2012 Aryng
When Not to Use Predictive AnalyticsCONSTRAINTS
Organizational Support, Opportunity Size
MONTH 1 MONTH 2 MONTH 3
FT 1 FT2
Historical Data and Minimum responders
Response
No Response
Required Accuracy of Prediction
Is ROI positive? Commitment for
actionability?
Need more accuracy?
Is minimum count
available?
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Predictive Analytics and Testing 17Copyright ©2011-2012 Aryng
Consider Simpler Techniques Instead
Advanced Techniques
Simpler Techniques
Predictive Analytics and Testing 18Copyright ©2011-2012 Aryng
MYTH #3
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Predictive Analytics and Testing 19Copyright ©2011-2012 Aryng
• Data specification and pull• Data cleaning and
preparation• Variable transformation and
selection• Model training and validation• Choosing the best model
based on business context
No “Press a Button” Solution
« The Five Myths of Predictive Analytics » 20©2011-2012 Aryng.com
• Data Scientist?• Needs qualified analyst with
hands-on experience in modeling
Model Building Needs Skills
Predictive Analytics Overview
Hands-on training
(commonly used techniques)
Build Models with access to mentors
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« The Five Myths of Predictive Analytics » 21©2011-2012 Aryng.com
Commonly Used Predictive and Advanced Techniques
Logistic Regression
Linear Regression
Decision Tree
Other techniques: Clustering (K-means), PCA, Factor Analysis, Time Series, Survival Analysis, Neural Network
« The Five Myths of Predictive Analytics » 22©2011-2012 Aryng.com
MYTH #2
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« The Five Myths of Predictive Analytics » 23©2011-2012 Aryng.com
STEPS
Effective Predictive Analytics Framework
2. Analysis
Plan
3. Data
Collection
4. Build
Model/ Derive
Insights
5.Recommendations
1. Business Question
f r o m i n q u i r y t o i n s i g h t s
f r o m i n s i g h t s t o i m p a c t
BADIR™: 5 steps from "data to decisions"™
« The Five Myths of Predictive Analytics » 24©2011-2012 Aryng.com
Predictive Analytics Using BADIR Process
ProblemOffering all products to all customers led to Bad Customer Experience and Wasted Marketing $.
Results
Reduced marketing spending by 70%Delivered incremental profits of $20M+
Process to solutionUsing BADIR process, we built the Next Best Product Recommendation Model optimized for adoption & profit.
STEPS2. Analysis
Plan3. Data
Collection4. Build
Model/Insight5.Recommen
dations1. Business Question
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« The Five Myths of Predictive Analytics » 25©2011-2012 Aryng.com
Build and Forget?
MYTH #1
« The Five Myths of Predictive Analytics » 26©2011-2012 Aryng.com
Product, pricing, policy changesCompetitive landscape changeEconomic condition changesInnovation …
Model Deteriorates Over Time!
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« The Five Myths of Predictive Analytics » 27©2011-2012 Aryng.com
STEPS
BADIR Framework and Model Tweaks
2. Analysis
Plan
3. Data
Collection
4. Build
Model/ Derive
Insights
5.Recommendations
1. Business Question
f r o m i n q u i r y t o i n s i g h t s
f r o m i n s i g h t s t o i m p a c t
C l i m a c t i c C h a n g e
N e w P o l i c i e s
P r o d u c t C h a n g e s
« The Five Myths of Predictive Analytics » 28©2011-2012 Aryng.com
Key Take-Aways
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« The Five Myths of Predictive Analytics » 29©2011-2012 Aryng.com
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