«The Five Myths of Predictive Analytics» 1

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1 The Five Myths of Predictive #PAWGov White paper: Piyanka Jain President & CEO, Aryng.com «The Five Myths of Predictive Analytics» 1

2 Analytics Training for Business Impact Strategic Analytics Consulting Data to Decisions Courses for Marketing Professionals Product Managers Data Scientists BI Professionals Public Workshop Onsite Training & Mentoring Online Training «The Five Myths of Predictive Analytics» 2

3 Our Clients Include «The Five Myths of Predictive Analytics» 3

4 Speaker Bio Piyanka, founder of Aryng - a premier analytics training and consulting company, is a well-regarded industry thought leader in analytics, keynoting at business and analytics conferences including Predictive Analytics World, Data Science Summit, TDWI Big Data Conference, Google Analytics User Conference, Business Performance conference on data driven decision making in an organization. With her 15 years of experience in analytics, she has had $100M+ demonstrated impact on business. Her prior roles include the head of NA Business Analytics at PayPal and senior marketing analytics position with Adobe. «The Five Myths of Predictive Analytics» 4

5 Agenda: Predictive Analytics Myths «The Five Myths of Predictive Analytics» 5

6 MYTH #1 It s the new kid in town! «The Five Myths of Predictive Analytics» 6

7 Question for the Audience When was the first application of Predictive Analytics? A s Advent of computing power B s Wall street crash C. 19 th century Birth of science as profession D. Before 15 th century «The Five Myths of Predictive Analytics» 7

8 Predictive Analytics Coming of Age First credit score model by Fisher and Durand 2011: PA hits mainstream : Narrowing of algorithms 1965 Computing power with IBM Econometric society 1977 Oracle s commercial Relational Database «The Five Myths of Predictive Analytics» 8

9 MYTH #2.a Perfect! «The Five Myths of Predictive Analytics» 9

10 Models Are Not Perfect Trying to predict greens Green Actual Red Predicted Green Red Even the best model has misclassification «The Five Myths of Predictive Analytics» 10

11 Model Prediction Is an Estimation No Income > $40,000 Yes Renter High Debt Yes No Yes No Bad Risk Good Risk Bad Risk Good Risk Model predicts by looking at likely-hood «The Five Myths of Predictive Analytics» 11

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13 MYTH #2.b Is always the best technique! «The Five Myths of Predictive Analytics» 13

14 When Not to Use Predictive Analytics CONSTRAINTS Organizational Support, Opportunity Size FT 1 FT2 Is ROI positive? Commitment for actionability? MONTH 1 MONTH 2 MONTH 3 Required Accuracy of Prediction Need more accuracy? Historical Data and Minimum responders Response No Response Is minimum count available? «The Five Myths of Predictive Analytics» 14

15 Consider Simpler Techniques Instead Advanced Techniques Simpler Techniques «The Five Myths of Predictive Analytics» 15

16 MYTH #3 «The Five Myths of Predictive Analytics» 16

17 No Press a Button Solution Data specification and pull Data cleaning and preparation Variable transformation and selection Model training and validation Choosing the best model based on business context «The Five Myths of Predictive Analytics» 17

18 Model Building Needs Skills Needs qualified analyst with hands-on experience in modeling Predictive Analytics Overview Hands-on training (commonly used techniques) Build Models with access to mentors «The Five Myths of Predictive Analytics» 18

19 Commonly Used Predictive and Advanced Techniques Logistic Regression Decision Tree Linear Regression Other techniques: Clustering (K-means), PCA, Factor Analysis, Time Series, Survival Analysis, Neural Network «The Five Myths of Predictive Analytics» 19

20 MYTH #4 «The Five Myths of Predictive Analytics» 20

21 Effective Predictive Analytics Framework BADIR : 5 steps from "data to decisions" from inquiry to insights STEPS 1. Business Question 2. Analysis Plan 3. Data Collection 4. Build Model/ Derive Insights 5. Recommendations from insights to impact «The Five Myths of Predictive Analytics» 21

22 Predictive Analytics Using BADIR Process STEPS 1. Business Question 2. Analysis Plan 3. Data Collection 4. Build Model/Insight 5.Recommen dations Problem Offering all products to all customers led to Bad Customer Experience and Wasted Marketing $. Process to solution Using BADIR process, we built the Next Best Product Recommendation Model optimized for adoption & profit. Results Reduced marketing spending by 70% Delivered incremental profits of $20M+ «The Five Myths of Predictive Analytics» 22

23 MYTH #5 Build and Forget? «The Five Myths of Predictive Analytics» 23

24 Model Deteriorates Over Time! Product, pricing, policy changes Competitive landscape change Economic condition changes Innovation «The Five Myths of Predictive Analytics» 24

25 BADIR Framework and Model Tweaks from inquiry to insights STEPS 1. Business Question 2. Analysis Plan 3. Data Collection 4. Build Model/ Derive Insights 5. Recomme ndations from insights to impact New Policies Climactic Change Product Changes «The Five Myths of Predictive Analytics» 25

26 Key Take-Aways «The Five Myths of Predictive Analytics» 26

27 Thank You! Download the whitepaper Upcoming Events: x 101 piyanka@aryng.com «The Five Myths of Predictive Analytics» 27