Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level| Pre-Conference Workshops: Sunday, May 14, 2017 | |
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Uplift Models: Optimizing the Impact of Your Marketing  Kim Larsen, Stitch Fix |
Big Data: Proven Methods You Need to Extract Big Value  Marc Smith, Connected Action Consulting Group |
| Pre-Conference Workshops: Monday, May 15, 2017 | |||
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• Room: Nob Hill BC Hadoop for Predictive Analytics: Hands-On Lab James Casaletto, MapR Technologies |
• Room: Salon 5 & 6 R Bootcamp: For Newcomers to R Max Kuhn, RStudio |
• Room: Salon 3 & 4 Supercharging Prediction with Ensemble Models Dean Abbott, Abbott Analytics |
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• Room: Salon 5 & 6 R for Predictive Modeling: A Hands-On Introduction Max Kuhn, RStudio |
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| Day 1: Tuesday, May 16, 2017 (PAW Workforce runs in parallel on this day - dual registration required) |
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| 8:00-8:45am | Registration • Room: Grand Assembly | ||
| 8:00-8:45am | Networking Breakfast • Room: Salon 9 | ||
| 8:45-8:50am | Conference Welcome • Room: Salon 8 Eric Siegel, Predictive Analytics World | ||
| 8:50-9:40am |
KEYNOTE • Room: Salon 8 The Right Analytics for the Job: Tips and Tricks for Success Yanai Golany, Verizon |
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| 9:40-10:00am | Diamond Sponsor Presentation • Room: Salon 8 Automated machine learning with DataRobot Gourab De, DataRobot |
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| 10:00-10:30am | Exhibits & Morning Coffee Break • Room: 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 |
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| 10:30-10:50am | Analytics Management/ Team-Building |
Model Interpretation | Churn Modeling; Best Practices |
Need a Data Scientist, Try Building a "DataScienceStein" 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 Richard Boire, Environics Analytics |
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| B2B Churn Modeling | |||
| 10:55-11:15am | Case Study: 7Geese Using Predictive Analytics to Improve Customer Retention Craig Soules, Natero |
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| 11:20-11:40am | Analytics Project Management | Retail Analytics | Retention with Email Marketing |
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Case Study: Cisco 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 Kristina Pototska, TriggMine |
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| Gaming Analytics; Marketing Segmentation for Churn Modeling | |||
| 11:45am-12:05pm | Case Study: A Leading Gaming Company Identifying Unique Gamer Types Using Predictive Analytics Natasha Balac, Data Insight Discovery Angel Evan, Angel Evan |
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| 12:05-1:45pm | Lunch in Exhibit Hall • Room: Salon 9 | ||
| 1:45-2:30pm |
KEYNOTE • Room: Salon 8 Case Study: FICO Fraud Screening for 2/3rds of All Card Transactions: A Consortium and Its Data Som Shahapurkar, FICO |
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| Thought leadership; Analytics Management | Best Practices | Seasonality and Other Contextual Factors |
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| 2:40-3:25pm | Case Study: Wells Fargo Strategic Communication: Building a Bridge From Analytics to Business Chemere Davis, Wells Fargo Bank |
Q&A: Ask Karl and Steven Anything (about Best Practices) 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 Break • Room: 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 |
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| Thought Leadership; Analytics Management | Open Source Demo; Text Analytics | Churn Modeling | |
| 3:55-4:15pm | Case study: Dow Chemical Company
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
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 |
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| Advertising | Analytics Software | Churn Modeling with Deep Learning; B2B | |
| 4:45-5:05pm | Case Study: Visible World (A Comcast Company) 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 College Enhancing the Quality of Predictive Modeling on College Enrollment Feyzi Bagirov, Becker College |
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| 5:30-7:00pm | Networking Reception • Room: Salon 9 | ||
| 7:15pm | Dinner with Strangers | ||
| 7:00-10:00pm | Bay Area SAS Users Group Meeting • Room: Salon 9 |
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| Day 2: Wednesday, May 17, 2017 (PAW Workforce runs in parallel on this day - dual registration required) |
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| 8:00-9:05am | Registration • Room: 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 |
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| Track 1: All Levels Room: Salon 5 & 6 |
Track 2: Expert/Practitioners Room: Salon 8 |
Track 3: Marketing applications Room: Salon 3 & 4 |
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| 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 Steven Ramirez, Beyond the Arc |
Case Study: Google Machine Learning Models for Assessing Third Party Signals Julian Bharadwaj, Google | Case Study: Macys.com Macy's Advanced Analytics in Customer Centric Strategies Daqing Zhao, Macy's |
| 10:25-10:45am | Product Design Analytics | ||
| Case Study: GoPro Making Better Products with Predictive Analytics Jules Mali, GoPro |
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| 10:45-11:15am | Exhibits & Morning Coffee Break• Room: Salon 9 | ||
| 11:15-11:35am | Law Enforcement Applications | Sourcing Labelled Data | Marketing Optimization |
| Case Study: City of Jersey City 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 |
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| 11:40am-12:00pm | Insurance | ||
| Case Study: The Co-operators General Insurance Company Defining Optimal Segmentation Territories - 10 Years of Research Frédérick Guillot, The Co-operators General Insurance Company |
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| 12:00-1:25pm | Lunch in Exhibit Hall• Room: Salon 9 | ||
| 1:25-2:10pm |
KEYNOTE • Room: 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. |
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| 2:15-3:00pm |
Expert Panel• Room: Salon 8 Women in Predictive Analytics: Opportunities and Challenges Moderator: Theresa Kushner, VMWare Panelists: Morgane Ciot, DataRobot Luba Gloukhova, Stanford Graduate School of Business Aarti Gupta, Bain & Company |
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| 3:00-3:30pm | Exhibits & Afternoon Break• Room: 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 |
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| 3:30pm-3:50pm | Sports Analytics | Fraud Detection; Insurance | Personalized Marketing |
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NFL Predictive Analytics 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 |
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| Analytics Management | |||
| 3:55-4:15pm |
Technical Abstractions for Lasting Analytic Deployment Competency Pete Foley, Open Data Group |
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| Supply Chain | Insurance | Personalized Marketing | |
| 4:15-4:35pm | Case Study: PrintFleet 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 Discovering Persuasive Language through Observing Customer Behavior Jason Kessler, CDK Global |
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| Post-Conference Workshops: Thursday, May 18, 2017 | |
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The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes Dr. John Elder, Elder Research, Inc. |
Advanced Methods: Data Preparation and Modeling Techniques Dean Abbott, Abbott Analytics |
