May 14-18, 2017
San Francisco
Delivering on the promise of data science
Click here for upcoming PAW events

Register Now!All level tracks Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level

Agenda Overview – San Francisco– May 14-18, 2017
Pre-Conference Workshops: Sunday, May 14, 2017
Full-day WorkshopRoom: Salon 3 & 4
Uplift Models: Optimizing the
Impact of Your Marketing

Kim Larsen, Stitch Fix
Full-day Workshop Room: Salon 5 & 6
Big Data: Proven Methods You
Need to Extract Big Value

Marc Smith, Connected Action Consulting Group

Pre-Conference Workshops: Monday, May 15, 2017
Full-day Workshop
Room: Nob Hill BC
Hadoop for Predictive Analytics: Hands-On Lab
James Casaletto, MapR Technologies
Two and a half hour Workshop
Room: Salon 5 & 6
R Bootcamp: For Newcomers to R
Max Kuhn, RStudio
Full-day Workshop
Room: Salon 3 & 4
Supercharging Prediction with Ensemble Models
Dean Abbott, Abbott Analytics
Full-day Workshop
Room: Salon 5 & 6
R for Predictive Modeling: A Hands-On Introduction
Max Kuhn, RStudio

Day 1: Tuesday, May 16, 2017
(PAW Workforce runs in parallel on this day - dual registration required)
8:00-8:45am RegistrationRoom: Grand Assembly
8:00-8:45am Networking BreakfastRoom: Salon 9
8:45-8:50am Conference Welcome Room: Salon 8
Eric Siegel, Predictive Analytics World
KEYNOTE • Room: Salon 8
The Right Analytics for the Job: Tips and Tricks for Success

Yanai Golany, Verizon
9:40-10:00am Diamond Sponsor PresentationRoom: Salon 8
Automated machine learning with DataRobot
Gourab De, DataRobot
10:00-10:30am Exhibits & Morning Coffee BreakRoom: Salon 9
  Track 1: All Levels
Room: Salon 5 & 6
Track 2: Expert/Practitioners
Room: Salon 8
Track 3: Churn modeling (for targeted customer retention)
Room: Salon 3 & 4
10:30-10:50am Analytics Management/
Model Interpretation Churn Modeling; Best Practices
Need a Data Scientist, Try Building a "DataScienceStein"All level tracks
Bryan Bennett, Northwestern University
Case Study: SmarterHQ
When Model Interpretation Matters: Understanding Complex Predictive Models
Dean Abbott, SmarterHQ
Predicting Churn - An Often Not-So-Easy Task All level tracks
Richard Boire, Environics Analytics
B2B Churn Modeling
10:55-11:15am Case Study: 7Geese All level tracks
Using Predictive Analytics to Improve Customer Retention

Craig Soules, Natero
11:20-11:40am Analytics Project Management Retail Analytics Retention with Email Marketing
Case Study: Cisco All level tracks
The Role of Decision Modeling in Creating Data Science Excellence

James Taylor, Decision Management Solutions
Tina Owenmark, Cisco
Case Study: SmarterHQ
Automated Retail Analytics - Omni-Channel and at Scale
William Komp, SmarterHQ
Case Studies: ILovemakeup, StarShop, Tibiona, Collectomania (eCommerce in EMEA)
7 Examples of Customer Retention with Predictive Email Marketing All level tracks
Kristina Pototska, TriggMine
Gaming Analytics; Marketing Segmentation for Churn Modeling
11:45am-12:05pm Case Study: A Leading Gaming Company All level tracks
Identifying Unique Gamer Types Using Predictive Analytics

Natasha Balac, Data Insight Discovery
Angel Evan, Angel Evan
12:05-1:45pm Lunch in Exhibit HallRoom: Salon 9
KEYNOTERoom: Salon 8
Case Study: FICO
Fraud Screening for 2/3rds of All Card Transactions: A Consortium and Its Data

Som Shahapurkar, FICO
Thought leadership; Analytics Management Best Practices Seasonality and
Other Contextual Factors
2:40-3:25pm Case Study: Wells FargoAll level tracks
Strategic Communication: Building a Bridge From Analytics to Business
Chemere Davis, Wells Fargo Bank
Q&A: Ask Karl and Steven Anything (about Best

Steven Ramirez, Beyond the Arc
Karl Rexer, Rexer Analytics
Case Study: Paychex
Retention Modeling in Uncertain Economic Times
Chip Galusha, Paychex
3:25-3:55pm Exhibits & Afternoon BreakRoom: Salon 9
  Track 1: All Levels
Room: Salon 5 & 6
Track 2: Expert/Practitioners
Room: Salon 8
Track 3: Churn modeling (for targeted customer retention)
Room: Salon 3 & 4
  Thought Leadership; Analytics Management Open Source Demo; Text Analytics Churn Modeling
3:55-4:15pm Case study: Dow Chemical Company All level tracks
Creating an Industrial Revolution for Analytics
Paul Speaker, The Dow Chemical Company
Semantic Natural Language Understanding with Spark, Machine-Learned Annotators & Deep-Learned Ontologies
David Talby, Atigeo
Case Study: LinkedIn
Predicting Customer Churn in a Subscription Business
Ming Ng, LinkedIn
B2B; Text Analytics
4:20-4:40pm Case study: Dow Chemical Company All level tracks
Listening Down the Value Chain: Using Text-based Predictive Models to Find New Opportunities for B-to-B Businesses
Michael Dessauer, The Dow Chemical Company
  Advertising Analytics Software Churn Modeling with Deep Learning; B2B
4:45-5:05pm Case Study: Visible World (A Comcast Company)All level tracks
Predictive Analytics for Yield Management

Bob Bress, Visible World (A Comcast Company)
Data Science Hype vs Reality: "Big Data" vs Single Machine Tools
Szilard Pafka, Epoch
Case Study: Microsoft
How Microsoft Predicted Churn of Cloud Customers Using Deep Learning and Explained Predictions in An Interpretable Way
Feng Zhu & Val Fontama, Microsoft
Acquisition Funnel for Higher Education
5:10-5:30pm Case Study: Becker CollegeAll level tracks
Enhancing the Quality of Predictive Modeling on College Enrollment
Feyzi Bagirov, Becker College
5:30-7:00pm Networking ReceptionRoom: Salon 9
7:15pm Dinner with Strangers
7:00-10:00pm Bay Area SAS Users Group MeetingRoom: Salon 9

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Day 2: Wednesday, May 17, 2017
(PAW Workforce runs in parallel on this day - dual registration required)
8:00-9:05am RegistrationRoom: Grand Assembly
8:00-9:05am Networking Breakfast Room: Salon 9
9:10- 9:55am
Special Plenary Session Room: Salon 8
What to Optimize? The Heart of Every Analytics Problem
Dr. John Elder, Elder Research
  Track 1: All Levels
Room: Salon 5 & 6
Track 2: Expert/Practitioners
Room: Salon 8
Track 3: Marketing applications
Room: Salon 3 & 4
Financial Service Applications External Data Online Marketing and Personalization
10:00-10:20am Case Study: Bizfi
How Predictive Analytics Can Drive Success in Fintech and Banking All level tracks

Steven Ramirez, Beyond the Arc
Case Study: Google
Machine Learning Models for Assessing Third Party Signals
Julian Bharadwaj, Google
Case Study:
Macy's Advanced Analytics in Customer Centric Strategies
Daqing Zhao, Macy's
10:25-10:45am Product Design Analytics
Case Study: GoProAll level tracks
Making Better Products with Predictive Analytics

Jules Mali, GoPro
10:45-11:15am Exhibits & Morning Coffee BreakRoom: Salon 9
11:15-11:35am Law Enforcement Applications Sourcing Labelled Data Marketing Optimization
Case Study: City of
Jersey City
All level tracks
Predictive Analytics and Data in City Government
Brian Platt, City of Jersey City
Case Study: Capital One
The Quest for Labeled Data: Integrating Human Steps
Ashish Bansal, Capital One
John Schlerf, Capital One
Redefining Analytics for Marketing
Jennifer Bertero, CA Technologies
11:40am-12:00pm Insurance
Case Study: The Co-operators General Insurance CompanyAll level tracks
Defining Optimal Segmentation Territories - 10 Years of Research
Frédérick Guillot, The Co-operators General Insurance Company
12:00-1:25pm Lunch in Exhibit HallRoom: Salon 9
KEYNOTERoom: Salon 8
The Centrality of a Detailed Understanding of Your Audience
Prerna Singh, Mashable
Sarah Guido, Mashable
Mashable Chief Data Scientist Haile Owusu replaced himself Friday with
two colleagues to deliver the keynote on his behalf.
2:15-3:00pm Expert PanelRoom: Salon 8
Women in Predictive Analytics: Opportunities and Challenges
Moderator: Theresa Kushner, VMWare
Morgane Ciot, DataRobot
Luba Gloukhova, Stanford Graduate School of Business
Aarti Gupta, Bain & Company
3:00-3:30pm Exhibits & Afternoon BreakRoom: Salon 9
  Track 1: All Levels
Room: Salon 5 & 6
Track 2: Expert/Practitioners
Room: Salon 8
Track 3: Marketing applications
Room: Salon 3 & 4
3:30pm-3:50pm Sports Analytics Fraud Detection; Insurance Personalized Marketing
NFL Predictive AnalyticsAll level tracks
Ash Pahwa, University of California Extension, Irvine, CAs
Case Study: Alberta
Blue Cross

Claim Pattern Anomalies - Making a Mole Hill Out of a Mountain

Darryl Humphrey, Alberta Blue Cross
Case Study: Sears Holdings Company
Omnichannel Measurement and Attribution as a Building Block for In-House Programmatic Solution
Kerem Tomak, Sears Holdings Company
Analytics Management
3:55-4:15pm Technical Abstractions for Lasting Analytic Deployment Competency All level tracks
Pete Foley, Open Data Group
  Supply Chain Insurance Personalized Marketing
4:15-4:35pm Case Study: PrintFleet All level tracks
Reducing Wasted Toner - Huge Savings for Service Providers and the Environment
Scott Hornbuckle, Photizo Group
Case Study:
Gallagher Bassett
Finding the Waypoint: A TPA and an Actuary with Predictive Analytics Reinvent Reserving (and it's not boring after all)

Gary Anderberg, Gallagher Bassett
Sandip Chatterjee, Gallagher Bassett
Abhi Butchibabu, gradient A.I.
The What and The How Matter When Talking to Customers - Even More Today
Karan Bhalla, EXL Analytics
Text Analytics
4:40-5:00pm Case Study: CDK Global All level tracks
Discovering Persuasive Language through Observing Customer Behavior
Jason Kessler, CDK Global

Post-Conference Workshops: Thursday, May 18, 2017
Full-day Workshop Room: Salon 3 & 4
The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common
Data Mining Mistakes

Dr. John Elder, Elder Research, Inc.
Full-day Workshop Room: Salon 5 & 6
Advanced Methods:
Data Preparation and Modeling Techniques

Dean Abbott, Abbott Analytics

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