Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level| Pre-Conference Workshop: Sunday, October 23, 2016 | |
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R Bootcamp: For Newcomers to R  Max Kuhn, Pfizer |
Big Data: Proven Methods You Need to Extract Big Value  Vladimir Barash, Graphika |
| Pre-Conference Workshop: Monday, October 24, 2016 | |||
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R for Predictive Modeling: A Hands-On Introduction Max Kuhn, Pfizer |
Advanced Methods Hands-on: Predictive Modeling Techniques Dean Abbott, Abbott Analytics, Inc Sponsored by: 
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| DAY 1, Tuesday, October 25, 2016 (PAW Healthcare & PAW Financial run in parallel on this day - dual registration required) |
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| 8:00-8:45am | Registration & Networking Breakfast Room: Exhibit Area 1A |
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| 8:45-8:50am | Conference Chair Welcome Eric Siegel, Predictive Analytics World Room: 1A25/1A26 | ||
| 8:50-9:40am |
KEYNOTE Weird Science: How to Know Your Predictive Discovery Is Not BS Eric Siegel, Predictive Analytics World Room: 1A25/1A26 |
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| 9:40-10:00am | Diamond Sponsor Presentation An Introduction to DataRobot Machine Learning Platform Greg Michaelson, DataRobot Room: 1A25/1A26  |
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| 10:00-10:30am | Exhibits & Morning Coffee Break Room: Exhibit Area 1A |
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| Track 1: All Levels Room: 1A23 |
Track 2: Expert/Practitioners Room: 1A25/1A26 |
Track 3: Uplift Modeling Room: 1A27 |
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| 10:30-11:15am | Model Overstatement | Retail Predictive Analytics | Uplift Modeling |
Developing an Analytics Discipline in Recognizing Model Overstatement Richard Boire, Environics Analytics |
Case Study: SmarterHQ The Revolution in Retail Customer Intelligence Dean Abbott, SmarterHQ |
Case Studies: Telenor; US Bank Uplift Modeling: Optimize for Influence and Persuade by the Numbers Eric Siegel, Predictive Analytics World |
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| 11:20am-12:05pm | Predictive Analytics for Sentiment | Economic Forecasting | Uplift Modeling |
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Case Study: A regional Bank in the Northeast US Sentiment Unchained: Accelerating Analytics to Understand Customer Behavior Joseph Blue, MapR |
Predicting Changes in Rental Prices in New York City Using Online Restaurant Reviews Anasse Bari, NYU Islam Tawfik, Learnvest |
Case Study: Miles & More (from Lufthansa) Using Predictive Models for Demand Simulation - Purchase, Response and Uplift Modeling in Practice Thomas Klein & Alexander Funkner, Miles & More GmbH |
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| 12:05-1:30pm | Lunch in Exhibit Hall Room: Exhibit Area 1A |
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| 1:30-2:15pm |
KEYNOTE Topic: Uplift Modeling Persuasion Modeling in Presidential Campaigns and How It Applies to Business Daniel Porter, BlueLabs, Obama for America Room: 1A25/1A26 |
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| 2:15-2:35pm | Gold Sponsor Presentations Room: 1A25/1A26 |
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| Thought Leadership | Model Deployment | Uplift Modeling | |
| 2:40-3:00pm | When Should We Trust Robots with Decisions? Vasant Dhar, NYU |
Five Best Practices for Deploying Analytic Models into Operations and Five Common Mistakes Robert Grossman, Open Data Group | Causality in Business Analytics: Uplift Models and Directed Acyclic Graphs Dominique Haughton, Bentley University Jonathan Haughton, Suffolk University |
| 3:05-3:25pm | Supply Chain Optimization | ||
| Predicting and Managing Behavior In Process Industry Supply Chains Gary Neights, Elemica |
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| 3:25-3:55pm |
Exhibits & Afternoon Break Room: Exhibit Area 1A |
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| 3:55-4:40pm | Special Plenary Session Case Study: Barclays The Rapidly Changing Big Data Landscape: Balancing the Power with the Dangers of Confusion Usama Fayyad, Barclays Room: 1A25/1A26 |
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| Track 1: All Levels Room: 1A23 |
Track 2: Expert/Practitioners Room: 1A25/1A26 |
Track 3: Uplift Modeling Room: 1A27 |
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| Legal Applications | Project Management with Analytics | Uplift Modeling | |
| 4:45-5:05pm | Case Study: LexisNexis Predicting the Fate of Legislative Bills and Finding Effective User Analytics for Business Decision Making Michael Cole, LexisNexis Gene Osgood, LexisNexis |
Case Study: The MITRE Corporation Applying Machine Learning Techniques to Improve Quality Richard Eng, The MITRE Corporation | Case Study: AIG Parametric Uplift Regression Models Matthew Flynn, AIG Science |
| 5:10-5:30pm | Acquisition Funnel for Higher Education | ||
| 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: Exhibit Area 1A |
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| DAY 2, Wednesday, October 26, 2016 (PAW Healthcare & PAW Financial run in parallel on this day - dual registration required) |
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| 8:00-9:05am | Registration & Networking Breakfast Room: Exhibit Area 1A |
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| 9:05-9:10am | Conference Chair Welcome Eric Siegel, Predictive Analytics World Room: 1A25/1A26 | ||
| 9:10-10:00am | Special Plenary Session Doing Space-Age Analytics with Our Hunter-Gatherer Brains John Elder, Elder Research, Inc. Room: 1A25/1A26 |
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| Track 1: All Levels Room: 1A23 |
Track 2: Expert/Practitioners Room: 1A25/1A26 |
Track 3: Ads & Marketing Room: 1A27 |
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| Workforce Analytics | Best Practices | TV Ad Targeting | |
| 10:05-10:50am | Case Study: Paychex Predicting Employee Churn with Anonymity Chip Galusha, Paychex |
Q&A:
Ask Karl and Dean Anything (about Best Practices) Dean Abbott, SmarterHQ Karl Rexer, Rexer Analytics |
Case Study:Visible World What Shall I Watch Now? Ensuring the Right TV Ads Are in Front of the Right Audiences Bob Bress, Visible World |
| 10:50-11:15am | Exhibits & Morning Coffee Break Room: Exhibit Area 1A |
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| 11:15-11:35am | Social Activism Engagement | Workforce Analytics | Marketing Applications |
| Case Study: Change.org Anatomy of a Social Movement: Using Analytics to Change the World Duncan Lockard, Change.org |
Predictive Job Maps: Optimizing a Workforce with a Network of Predictive Models Pasha Roberts, Talent Analytics, Corp. |
Case Study: Bitly Predictive Analytics for Different Business Types: Optimize All the Funnels Meina Zhou, Bitly |
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| 11:40am-12:00pm | Email Marketing | ||
| Case Study: Mountain America Credit Union Maximizing Net New Deposits of a Promotional Email Campaign with Predictive Analytics Lawrence Cowan, Cicero Group |
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| 12:00-1:15pm | Lunch in Exhibit Hall Room: Exhibit Area 1A |
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| 1:15-2:00pm |
KEYNOTE Case Study: CA Technologies The Impact of Analytics and Digital Transformation on Humans Saum Mathur, CA Technologies Room: 1A25/1A26 |
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| 2:00-2:15pm | Lightning Round of Vendor Presentations Room: 1A25/1A26 |
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| 2:15-3:00pm | Expert Panel Data Prep: Overcoming the Bottleneck and Nailing It Moderator: Eric Siegel, Predictive Analytics World Panelists: John Elder, Elder Research, Inc. Satadru Sengupta, DataRobot Pini Yakuel, Optimove Room: 1A25/1A26 |
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| 3:00-3:30pm |
Exhibits & Afternoon Break Room: Exhibit Area 1A |
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| Track 1: All Levels Room: 1A23 |
Track 2: Expert/Practitioners Room: 1A25/1A26 |
Track 3: Ads & Marketing Room: 1A27 |
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| Enterprise Platform | New Data Sources | Leads Scoring, B2B, Sales Funnel | |
| 3:30-3:50pm | Case Study: Johnson & Johnson Analytics at the Speed of Global Business: Delivering an Analytics Technology Platform Elena Alikhachkina, Johnson & Johnson |
Case Study: The Hive (U.N. Refugee Agency lab) The Data Set You Can No Longer Ignore: Consumer Engagement with Social Issues Rita Ko, The Hive |
Case Study: IBM Integrating Analytics and Design Thinking Approaches for Sales Order Propensity Prediction Pitipong Lin, IBM |
| 3:55-4:15pm | Social Data for Predictive | Labor Demand | |
| Case Study: Cars.com Predicting Consumer Review Engagement and Sentiment Using only Readily Available Social and Demographic Data Michael Spadafore, Marketing Associates |
Data-Driven Hiring of Data Scientists Michael Li, The Data Incubator | ||
| 4:20-5:05pm | Thought Leadership | Consumer Experience Optimization | Product Recommendations - Advanced Methods |
| Case Study: MIT Int Soc for Chief Data Officers How $1B Companies are Scaling the Chief Data and Analytics Officer Function Richard Wendell, Tellic |
Case Study: Verizon Best Practices Enhancing Contextual Experience with Predictive Analytics Madhu Raman, Verizon |
Graph Database for complex problems - Creating Personalized Food Item Recommendations at Restaurants Matthew Burris, Citibank | |
| Post-Conference Workshop: Thursday, October 27, 2016 | ||
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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. |
Supercharging Prediction with Ensemble Models Dean Abbott, Abbott Analytics Sponsored by: 
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Hadoop for Predictive Analytics: Hands-On Lab James Casaletto, MapR Technologies |
