Track 1 sessions are for All Levels
Track 2 sessions are Expert/Practitioner Level| Pre-Conference Workshop: Sunday, March 29, 2015 | |
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Uplift Models: Optimizing the Impact of Your Marketing  Kim Larsen, Stitch Fix Room: Foothill E |
R Bootcamp: For Newcomers to R Max Kuhn, Pfizer Room: Foothill E |
| Pre-Conference Workshops: Monday, March 30, 2015 | |||
| Full-day Workshop R for Predictive Modeling: A Hands-On Introduction Max Kuhn, Pfizer Room: Foothill G |
Full-day Workshop Big Data: Proven Methods You Need to Extract Big Value  Marc Smith, Connected Action Consulting Group Room: Nob Hill C/D |
Full-day Workshop Advanced Methods Hands-on: Predictive Modeling Techniques Dean Abbott, Abbott Analytics Sponsored by:  Room: Foothill E |
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| Day 1: Tuesday, March 31, 2015 (PAW Workforce and TAW run in parallel on this day - dual registration required) |
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| 8:00-8:45am | Registration & Networking Breakfast Room: Grand Assembly | |
| 8:45-8:50am | Conference Chair Welcome Eric Siegel, Predictive Analytics World Room: Golden Gate A | |
| 8:50-9:40am |
Keynote• Room: Golden Gate A Case Study: Smarter Remarketer The Revolution in Retail Customer Intelligence Dean Abbott, Smarter Remarketer |
9:40-10:00am | Diamond Sponsor Presentation • Room: Golden Gate A New Challenges: From Predictive to Prescriptive to Automated Analytics Thomas Hill, Dell Statistica Software
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| 10:00-10:30am | Exhibits & Morning Coffee Break Room: Salon 8/9 |
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| Track 1: All Levels Moderator: Philip O'Brien, Paychex Room: Salon 4-6 |
Track 2: Expert/Practitioners Moderator: Leanne Bateman, Brandeis Univ Room: Golden Gate A |
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| 10:30-11:15am | Sales Analytics | Methods; Marketing Applications |
| Case Study: Paychex Shaping Sales Strategy with Predictive Analytics Philip O'Brien, Paychex |
Data Driven Customer Segmentation Methods Jim Porzak, DS4CI.org | |
| 11:20-11:40am | Sales Analytics | Social Data |
| Case Study: RingCentral Going Beyond BI: How RingCentral Uses Predictive Analytics to Drive Revenue Growth Bill Dolby, RingCentral |
Think Link: Connecting to the Power of Social Networks with no Programing with NodeXL Marc Smith, Social Media Research Foundation |
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| 11:45am-12:05pm | Maritime Vessel Safety | |
| Case Study: RightShip The Impact of Predictive Analytics on Maritime Safety and Efficiency Bryan Guenther, RightShip |
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| 12:05-1:10pm | Lunch in the Exhibit Hall Room: Salon 8/9 |
Lunch & Learn • Room: Golden Gate A Driving Unified Customer Experience Insights and Impact: A Case Study Presentation with Lenovo Ashish Braganza, Lenovo Anees Merchant, Blueocean Market Intelligence  |
| 1:10-1:55pm |
Keynote • Room: Golden Gate A UPS Analytics – The Road to Optimization Jack Levis, UPS |
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| 1:55-2:15pm | Diamond Sponsor Presentation • Room: Golden Gate A Leveraging Best Practices with Oracle Advanced Analytics Charles Berger, Oracle  | |
| 2:15-2:35pm | Diamond Sponsor Presentation • Room: Golden Gate A Next Generation Customer Insights from Multichannel: A Case Study in the Use of Machine Learning and NLP Mark Eduljee, Microsoft Corp. Ashwin Mittal, Blueocean Market Intelligence | Track 1: All Levels Moderator: James Taylor, Decision Management Solutions Room: Salon 4-6 |
Track 2: Expert/Practitioners Moderator: Marc Smith, Connected Action Consulting Group Room: Golden Gate A |
| 2:40-3:00pm | Analytics Project Management | Best Practices |
| Case Study: University of Maryland Decision Modeling for Predictive Analytic Projects James Taylor, Decision Management Solutions Steve Knode, UMUC Chief Information Officer program |
Q&A: Ask Dean and Steven Anything (about Best Practices) Dean Abbott, Smarter Remarketer Steven Ramirez, Beyond the Arc | |
| 3:05-3:25pm | Analytics applications | |
| Data Alchemy, Mythology and the Quest for the Perpetual Data Machine Andreas Weigend, Social Data Lab & Gam Dias, Social Data Lab |
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| 3:25-3:55pm | Exhibits & Afternoon Break Room: Salon 8/9 |
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| 3:55-4:15pm | Marketing Analytics & Internal Marketing of Analytics | Uplift Modeling |
| Case Study: Duke Energy It's Not a Black Box! Explaining Predictive Models to the Masses Sarah Holder, Duke Energy |
Case Study: Harte Hanks Uplift Modeling Versus Traditional Response Modeling - Which One is Right for You? David Schey, Digitas | |
| 4:20-4:40pm | Analytics for Project Management | |
| Case Study: State Street Corp. How Can Predictive Analytics Help Avoid $1.2 Million In IT Project Development Costs? Scott Lancaster, State Street Corp. |
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| 4:45-5:30pm | Predictive Resource Allocation | Uplift Modeling |
| Case Study: Cisco Integrating Predictive Models within a Rules Engine for Resource Allocation Aaron Lanzen, Cisco |
Response Modeling is the Wrong Modeling: Maximize Impact with Uplift Modeling Kim Larsen, Stitch Fix | |
| 5:30-7:00pm | Networking Reception Room: Salon 8/9 |
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| 7:00-10:00pm | Bay Area useR Group Meeting Room: Salon 4-6 |
Bay Area SAS Users Group Meeting Room: Golden Gate A |
| Day 2: Wednesday, April 1, 2015 (PAW Workforce and TAW run in parallel on this day - dual registration required) (The Predictive Analytics Times Executive Breakfast also runs in parallel on this day) |
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| 8:00-8:45am | Registration Room: Grand Assembly |
Networking Breakfast Room: Salon 8/9 |
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| 8:45-8:50am | Opening and Welcome Room: Golden Gate A |
8:50-9:40am |
Special Plenary Session • Room: Golden Gate A Top Five Technical Tricks to Try when Trapped Dr. John Elder, Elder Research |
| 9:40-10:00am | Sponsor Presentation • Room: Golden Gate A Five Things that you Need to do to Increase the Efficiency of Predictive Models Vivek Desikan, LatentView 
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| Track 1: All Levels Moderator: Richard Boire, Boire Filler Group Room: Salon 4-6 |
Track 2: Expert/Practitioners Moderator: Brandon Purcell, Beyond the Arc Room: Golden Gate A |
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| 10:00-10:20am | Fraud Detection; Analytics in Gaming | Healthcare Applications | |
| Case Study: Activision Cheating Detection in Call of Duty Josh Hemann, Activision |
Case Study: New Directions Behavioral Health Predicting Undiagnosed Substance Abuse Kristopher Thodosoff & Fred Grunwald, New Directions Behavioral Health |
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| 10:25-10:45am | Small Business Applications | ||
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Case Study: A small pizza business Pizza Analytics & Optimization Mohamad Khatib, Nielsen |
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| 10:45-11:15am | Exhibits & Morning Coffee Break Room: Salon 8/9 |
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| 11:15-11:35am | Public Health / Food Safety | TV Ad Targeting | |
| Case Study: Chicago Department of Public Health Transforming Food Inspections Using Predictive Analytics Jay Bhatt, Chicago Department of Public Health |
Case Study: Visible World TV Audience Targeting through Predictive Analytics Bob Bress, Visible World |
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| 11:40am-12:00pm | Educating Analytics Practitioners | ||
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Case Study: Brandeis University Analytics in Academia: Making Sure the Rubber Meets the Road in the Real World Leanne Bateman, Brandeis University |
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| 12:00-1:15pm | Lunch in the Exhibit Hall Room: Salon 8/9 |
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| 1:15-2:00pm |
Expert Panel • Room: Golden Gate A Education and Training Options for Predictive Analytics Moderator: Eric Siegel, Predictive Analytics World Panelists: Dave Dimas, The University of California, Irvine Shawn Rogers, Dell Software Jack Brinson, Oracle |
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| 2:00-2:15pm | Vendor Elevator Pitches • Room: Golden Gate A 
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| INSURANCE | |||
| 2:15-3:00pm |
Keynote • Room: Golden Gate A Establishing Value: the "Making Impact Through Analytics" Framework Bin Mu, MetLife |
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| 3:00-3:30pm | Exhibits & Afternoon Break Room: Salon 8/9 |
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| Track 1: All Levels - INSURANCE Moderator: Steven Ramirez, Beyond the Arc Room: Salon 4-6 |
Track 2: Expert/Practitioners Moderator: Brandon Purcell, Beyond the Arc Room: Golden Gate A |
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| 3:30-3:50pm | Insurance; Unique Data Assets | Supply Chain Management; Methods | |
| Case Study: TransUnion What Unique Data Assets Reveal about Auto Insurance Shopping and Consumers John Ittner, TransUnion |
Case Study: 7-Eleven Converting Deep Dive Analytics to Structured Cadence for Optimizing Logistics Priorities Ron Jayaraman, 7-Eleven |
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| 3:55-4:15pm | Insurance; Sales Forecasting | Advertising Analytics | |
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Case Study: The Co-operators Insurance Co. Sales and Revenue Forecasting through Predictive Analytics Dominic Fortin, Co-operators |
Case Study: Spongecell
Drowning in Data: Predictive Analytics in a 24/7 World Byron Ellis, Spongecell |
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| 4:15-4:35pm | Insurance; Predictive Claims Management | Advanced Methods | |
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Case Study: Esurance Predictive Claims Management: Identifying the Optimal Repair or Loss Channel John Haller, CCC Information Services Donald Finley, Esurance |
Efficient Text Classification with Neural Nets Michael Tamir, Galvanize Daniel Hansen, Personagraph |
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| 4:40pm-5:00pm | Insurance; Loss Prediction | ||
| Predicting Extreme Behavior to Improve the Rating Structure for Travel Insurance Richard Boire, Boire Filler Group |
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| Post-Conference Workshops: Thursday, April 2, 2015 | ||
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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. Room: Nob Hill A/B |
Supercharging Prediction: Hands-On with Ensemble Models Dean Abbott, Abbott Analytics Sponsored by:  Room: Nob Hill C/D |
Text Analytics: From Getting Started to Successful Applications Tom Reamy, Program Chair, Text Analytics World, KAPS Group. LLC Room: Salon 10-11 |
