Conference Guide

March 14-15, 2011 S an Francisco parpartt of

Conference Guide

Keynote Speakers

Thomas Davenport President's Distinguished Professor, Babson College, Author, Competing on Analytics

Sugato Basu, Ph.D., Senior Research Scientist, Google

Eric Siegel, Ph.D., Conference Chair Predictive Analytics World Diamond Sponsors produced by www.predictiveanalyticsworld.com

Welcome!

Dear Analytical Innovators and Soothsayers, CONTENTS Conference Information...... 2 Welcome to the third West-Coast Predictive Analytics World. You have come to a business-focused event, loaded with Agenda Overview...... 3 predictive analytics case studies, expertise and resources. This Pre-Conference Workshops...... 6 conference brings professionals and experts together to keep predictive analytics on a forward trajectory; strengthening the Full Agenda and Descriptions.....8 impact it delivers and establishing new opportunities. Post-Conference Workshops.....18 PAW is now part of the brand new Data-Driven Business Week. Sponsors...... 22 This multi-conference “über-event” spans topics in analytics and beyond, reflecting the growing importance and visibility of Speakers...... 24 the industry. You benefit from this cross-pollination by access to cross-conference expositions and cross-registration options.

Each of the millions of business decisions driven by Presentations: predictive analytics are based on concrete evidence and Presentations from speakers who sound mathematics. That is truly an upgrade to the way provided them will be posted to the we do business. And everywhere you turn, this upgrade conference website. We will send you an is “installed” in new, innovative ways by driving different email as soon as they are posted. types of operational decisions with the scores produced by predictive models. PAW’s extensive array of case studies prove that these innovations deliver.

Some parts of predictive analytics are just plain fun. Every time you try it out on a new data set, it’s an entirely unique experiment - and usually a successful one at that. That’s the moment you realize the predictive power of your data, one column at a time. Next comes the combination of columns with predictive modeling, proving once again that this particular breed of science, as abstract as it is, works just as well outside the lab that bore it.

One last note for the math-heads. Beyond the thrill of validating a predictive model, I see another happy ending: The moment when a business leader focused on the bottom line gets excited about something as technical and arcane as a predictive score. That’s when you know this science has an important role in the world, today.

Eric Siegel, Ph.D. Program Chair Predictive Analytics World

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l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. Pre-Conference Workshops: Sunday March 13, 2011 l Driving Enterprise Decisions with Business Analytics s R for Predictive Modeling: A Hands-On Introduction 9:00am-4:30pm James Taylor, CEO, Decision Management Solutions Max Kuhn, Dir. Nonclinical Statistics, Pfizer - Salon 3 - Salon 4

DAY 1: Monday March 14, 2011 • Exhibit Hall Open - 10:00am-7:30pm 7:30-9:00am Registration & Breakfast Keynote: Five Ways Predictive Analytics Cuts Enterprise Risk 9:00-9:45am Eric Siegel, Ph.D., Program Chair, Predictive Analytics World - Golden Gate B Diamond Sponsor Presentation The Hefty Toll of Fraud: 9:45-10:05am How to Leverage Predictive Analytics and Social Network Analysis John C. Brocklebank, Ph.D., VP, SAS - Golden Gate B Case Study: Using Predictive Analytics To Reduce Credit Risk In Auto Dealer Financing 10:05-10:15am Rado Kotorov Ph.D., Director, SP Management & Competitive Strategy, Information Builders - Golden Gate B 10:15-10:35am Break / Exhibits • Exhibit Hall Open: 10:00am-7:30pm Track 1: e-Commerce; Thought Leadership Track 2: Survey Analysis - Golden Gate B - Salon 5 & 6 s Case Study: YMCA l Case Study: PayPal / eBay Turning Member Satisfaction Surveys Putting Predictive Analytics into Context: 10:35-11:20am into an Actionable Narrative The Analytics Value Chain Dean Abbott, Abbott Analytics & Piyanka Jain, PayPal Bill Lazarus, Seer Analytics

11:25am- Multiple Case Studies: Anheuser-Busch, the SSA, Netflix Data Mining Lessons Learned - Technical & Business - From Applied Projects 12:10pm John Elder, Ph.D., Elder Research - Golden Gate B

12:10-12:20pm Gold Sponsor Presentations: Lightning Round - Golden Gate B

12:20-1:30pm Birds of a Feather Lunch / Exhibits - Golden Gate Sponsored by

Keynote: Lessons Learned in Predictive Modeling for Ad Targeting 1:30-2:15pm Sugato Basu, Ph.D., Senior Research Scientist, Google - Golden Gate B Sponsored by Diamond Sponsor Presentation: The Analytical Revolution 2:15-2:35pm Colin Shearer, WW Industry Solutions Leader, IBM - Golden Gate B Track 1: Crowdsourcing Data Mining - Golden Gate B Track 2: Black Box Trading - Salon 5 & 6 l Case Study: University of Melbourne, s Case Study: Cerebellum Capital Chessmetrics, HPN Black Box Trading: Analytics in the Land of the 2:40-3:25pm Crowdsourcing Data Prediction Vampire Squid Anthony Goldbloom, Kaggle David Andre, Ph.D., Cerebellum Capital Track 1: e-Commerce and HR - Golden Gate B Track 2: Fraud Detection - Salon 5 & 6

l Case Study: Monster.com s Case Study: U.S. Postal Service, Office of Inspector 3:30-3:50pm A Holistic Predictive Analytics Methodology General - Fighting the Good Fraud Fight Yangling Zhang, Monster Worldwide Antonia de Medinaceli, Elder Research

Track 1: Thought Leader - Golden Gate B Track 2: Software Lab - Salon 5 & 6 Lab Session: Live Topical Demo l Case Study: Health Insurance Institutionalizing Analytics: What got 3:55-4:15pm Analytics by Industry/Role you here won’t get you there Jack Phillips, International Institute for Analytics Srikanth Velamakanni, Fractal Analytics

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. DAY 1: Monday March 14, 2011 continued 4:15-4:35pm Break / Exhibits Track 1: Thought Leader - Golden Gate B Track 2: Ensembles; Fraud Detection - Salon 5 & 6 s Case Study: PayPal/eBay l Making Technologies Intelligent: Ensembles for Online Analytic Scoring Engine 4:35-5:20pm Data, Patterns, Access, and Architecture Michael Murff, PayPal Astro Teller, Ph.D., Google Hui Wang, PayPal Track 1: Product Recommendations - Golden Gate B Track 2: Uplift Modeling (True-Lift) - Salon 5 & 6

l Case Study: TXU Energy Message Optimization Using Discrete Choice 5:25-5:45pm Analysis in the Energy Sector Dustin Cannon, TXU Energy; John Colias, Decision Analyst, Inc. s True-Lift Modeling: Mining for the Most Truly Responsive Customers & Prospects Track 1: High Technology Retail - Golden Gate B Kathleen Kane & Jane Zheng, Fidelity l Case Study: Hewlett-Packard Customer Repurchase Analysis - B2B Context: 5:50-6:10pm A Bayesian Framework Jerry Shan, Hewlett-Packard 6:10-7:30pm Reception / Exhibits Bay Area SAS Users Group Meeting useR Group Meeting David Bell, State of Calif, Dept of Industrial Relations 7:30-10:00pm David Smith, Revolution Analytics, William Jackman, Kaiser Permanente Byron Ellis, adBrite - Golden Gate B - Salon 5 & 6

DAY 2: Tuesday March 15, 2011 • Exhibit Hall Open - 9:45am-4:30pm 8:00-9:00am Registration & Breakfast Keynote: The New Quantitative Era: Creating Successful Business Change with Analytics 9:00-9:45am Thomas Davenport, President’s Distinguished Professor, Babson College, Author, Competing on Analytics: The New Science of Winning - Salon 7

9:45-10:15am Break / Exhibits • Exhibit Hall Open 9:45am-4:30pm Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream Panelists: Colin Shearer, WW Industry Solutions Leader, IBM 10:15-11:00am Wayne Thompson, Analytics Product Manager, SAS Andreas Weigend, Ph.D., weigend.com, Former Chief Scientist, Amazon.com Panel moderator: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World - Golden Gate B Exclusive Announcement 11:00-11:10am The $3 million Heritage Health Prize: New Announcements on Contest Structure Robert O’Keefe, Corporate Senior VP, Heritage Provider Network - Golden Gate B

11:10-11:20am Gold Sponsor Presentations: Lightning Round - Golden Gate B

s Lab Session: Live Topical Demo s Lab Session: Live Topical Demo Bank On It! Use All Your Data to Making Predictive Models Count Maximize Customer Satisfaction, Loyalty And Retention 11:25-12:10pm Kaiser Fung, Senior Director of Strategic Analytics, Sirius Steven Ramirez, CEO, Beyond the Arc XM Radio - Golden Gate B Robert Parolin, Technical Manager, IBM Business Analytics - Salon 5 & 6

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. DAY 2: Tuesday March 15, 2011 continued 12:10-1:30pm Birds of a Feather Lunch / Exhibits - Golden Gate Sponsored by

Special Plenary Session: The State of the Social Data Revolution 1:30-2:15pm Andreas Weigend, Ph.D., weigend.com, Former Chief Scientist, Amazon.com - Golden Gate B 2:15-2:30pm 2 Minute Sponsor Presentations: Lightning Round - Golden Gate B Track 1: Financial Services & Segmenting - Golden Gate B Track 2: Predictive Analytics and Search - Salon 5 & 6

l Case Study: Bank of the West Effectively Incorporating Internet Data in Customer 2:35-2:55pm Value Models s Case Study: Orbitz Joel Kleinman, Bank of the West Enhance Business Performance by Applying Analytics Track 1: Market Forecasting - Golden Gate B to Search Engine Marketing (SEM) Sameer Chopra, Orbitz l Case Study: Eventbrite 3:00-3:20pm Market Penetration: Is Your Growth on Track? Tilmann Bruckhaus, Ph.D., Eventbrite 3:20-3:55pm Break / Exhibits Track 1: Financial Service and Social Data - Golden Gate B Track 2: Software Lab - Salon 5 & 6 Lab Session: Live Topical Demo Large Scale Predictive Models for l Case Study: Bank of America Chronic Illness using Fuzzy Logix’s In-Database 3:55-4:15pm How Do Peers Evaluate Peer-to-Peer Lending? Analytics Technology on Netezza Aaron Lai, Bank of America Christopher Hane, Ph.D., Ingenix Partha Sen, Fuzzy Logix Bill Zanine, Netezza, An IBM Company Track 1: Retail and Insurance - Golden Gate B Track 2: Social Data: Advanced Methods - Salon 5 & 6 l Case Study: CA State Automobile Assn. Addressing Analytics Challenges in the Retail and s Social (Media) Network Analysis with NodeXL 4:20-4:40pm Insurance Industries Marc Smith, Ph.D., Connected Action Noe Tuason, CA State Automobile Assn.

Track 1: Deployment: Insurance & Financial - Golden Gate B Track 2: Social Data & Telecom - Salon 5 & 6

s Case Study: Major N. American Telecom 4:45-5:05pm Social Networking Data for Churn Analysis David Katz, Dataspora l Case Studies: Fiserv and Varolii Track 2: Social Gaming and Retention - Salon 5 & 6 Deploying Analytics with a Rules-Based Infrastructure James Taylor, Decision Management Solutions s Case Study: Gaia Decoding Customer Attention & Retention Behavior 5:10pm-5:30pm Using Predictive Analytics Tim Lopez, Gaia Interactive Post-Conference Workshops Wed., Mar. 16 • 9:00am-4:30pm Thu., Mar. 17 • 9:00am-4:30pm Fri. Mar. 18 • 1:30pm-5:00pm s Full-day Workshop - The Best and Sat. Mar. 19 • 9:00am-4:30pm the Worst of Predictive Analytics: s Full-day Workshop - Hands-On s Predictive Modeling Methods and Predictive Analytics 1½ - day Workshop - Net Lift Models: Common Data Mining Mistakes Dean Abbott, Abbott Analytics Optimizing the Impact of Your Marketing John Elder, Ph.D., Elder Research - Foothill F Kim Larsen, Market Share Partners - - Golden Gate B A & B Executive Conference Center, Room 203

Sunday, March 13, 2011 • Full-Day Workshop Time: 9:00am - 4:30pm | Room: Salon 3 Time: 9:00am - 4:30pm | Room: Salon 4 Driving Enterprise Decisions R for Predictive Modeling: with Business Analytics A Hands-On Introduction Instructor: James Taylor, Instructor: Max Kuhn, CEO, Dir. Nonclinical Statistics, Decision Management Solutions Pfizer Intended Audience: Intended Audience: • Managers: Project leaders, directors, CXOs, vice Practitioners who wish to learn how to execute on presidents, investors and decision makers of any kind predictive analytics by way of the R language; anyone who responsible for working with analytics or interested in using wants “to turn ideas into software, quickly and faithfully.” analytics to improve their business. Knowledge Level: Either hands-on experience with predictive modeling • Technical Managers: Analysts, BI directors, developers, (without R) or hands-on familiarity with any programming DBAs, data warehouse specialists, architects, and consultants language (other than R) is sufficient background and who wish to build systems that make better decisions. preparation to participate in this workshop. Attendees receive a free copy of the instructor’s book, “Smart Workshop Description (Enough) Systems,” a course materials book, and an official This one-day session provides a hands-on introduction to R, certificate of completion at the conclusion of the workshop. the well-known open-source platform for data analysis. Real Workshop Description examples are employed in order to methodically expose Putting business analytics to work is top of mind for attendees to best practices driving R and its rich set of organizations like yours. Business agility and operational predictive modeling packages, providing hands-on experience responsiveness are more important than ever. There is a real and know-how. R is compared to other data analysis platforms, opportunity to use analytics - especially predictive analytics and common pitfalls in using R are addressed. - to seek out increasingly small margins and understand The instructor, a leading R developer and the creator of your customers, products, channels, partners and more. But CARET, a core R package that streamlines the process for predictive analytics is only part of the solution - you must creating predictive models, will guide attendees on hands- put these analytic insights to work making better decisions on execution with R, covering: every day. Business rules offer the agile, business-centric platform you need to manage decisions and effectively • A working knowledge of the R system deploy predictive analytics. Putting them together requires a • The strengths and limitations of the R language new conceptual framework - Decision Management. • Preparing data with R, including splitting, resampling and variable creation This workshop covers the principles of Decision • Developing predictive models with R, including decision Management, its application to critical business processes, trees, support vector machines and ensemble methods and the appropriate use of available technology. We show • Visualization: Exploratory Data Analysis (EDA), and tools you how to identify and prioritize the operational decisions that persuade that drive your organization’s success, introduce business • Evaluating predictive models, including viewing lift rules as a foundation to automate these decisions, link curves, variable importance and avoiding overfitting these decisions to data mining and predictive analytics and discuss how to ensure continuous improvement and Hardware: Bring Your Own Laptop - Each workshop partici- competitive advantage using adaptive control. pant is required to bring their own laptop running Windows or OS X. The software used during this training program, R, is free Schedule and readily available at www.cran.r-project.org • Workshop starts at 9:00am • Morning Coffee Break at 10:30am - 11:00am Attendees receive an electronic copy of the course materials • Lunch provided at 12:30pm - 1:15pm and related R code at the conclusion of the workshop. • Afternoon Coffee Break at 2:30pm - 3:00pm Schedule • End of the Workshop: 4:30pm • Workshop starts at 9:00am • Morning Coffee Break at 10:30am - 11:00am • Lunch provided at 12:30pm - 1:15pm • Afternoon Coffee Break at 2:30pm - 3:00pm • End of the Workshop: 4:30pm © 2011 Rising Media, Inc. 6 www.predictiveanalyticsworld.com/sanfrancisco/2011

Session Descriptions

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. Monday, March 14, 2011 and fraud. Banks, insurance companies, and government entities are all seeing an increase in both the number and sophistication of fraudulent activities. Exhibit Hall Open 10:00am - 7:30pm To fight fraud effectively, organizations must continually 7:30-9:00am improve the monitoring of customer behavior across Registration & Breakfast multiple accounts, systems, and agencies. They must develop a framework of components that support fraud 9:00-9:45am • Room: Golden Gate B detection, alert generation and management, and case Keynote: Five Ways Predictive Analytics Cuts management. Using a hybrid approach for fraud detection, Enterprise Risk the SAS Fraud Framework can include industry-specific Speaker: Eric Siegel, Ph.D., Program Chair, business rules, anomaly detection, predictive models, Predictive Analytics World and social network analysis. It can offer both top-down and bottom-up functionality for making hidden and risky All business is an exercise in risk management. All networks visible to investigators. organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance This approach provides more actionable fraud detection, companies do. greater insight into suspicious activity report (SAR) management responsibilities, and improved operational Predictive analytics does the trick, one customer at a time. efficiency, all while decreasing overall fraud spending This technology is a data-driven means to compute the risk by the organizations. Examples from both banking and each customer will defect, not respond to an expensive childcare government support will be presented. mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for 10:05-10:15am • Room: Golden Gate B a telephone solicitation that would have landed a sale, Platinum Sponsor Presentation commit fraud, or become a “loss customer” such as a bad Case Study: Using Predictive Analytics To debtor or an insurance policy-holder with high claims. Reduce Credit Risk In Auto Dealer Financing In this keynote session, Dr. Eric Siegel will reveal: Speaker: Rado Kotorov Ph.D., Director, SP Management & Competitive Strategy, Information Builders • Five ways predictive analytics evolves your enterprise to reduce risk This case study describes the deployment of predictive • Hidden sources of risk across operational functions analytics solution to non-statistical field operations users in • What every business should learn from insurance order to improve their assessment of business risk in making companies lending decisions to second-hand car dealerships. The • How advancements have reversed the very meaning of solution integrates various ERP and ODS systems to gather fraud data and score in real time inventory financing applications. • Why “man + machine” teams are greater than the sum It helped the company decrease write offs by 10%. of their parts for enterprise decision support 10:15-10:35am 9:45-10:05am • Room: Golden Gate B Break / Exhibits

Diamond Sponsor Presentation 10:35-11:20am • Room: Golden Gate B The Hefty Toll of Fraud: How to Leverage l Track 1: e-Commerce; Thought Leadership Predictive Analytics and Social Network Case Study: PayPal / eBay Analysis Putting Predictive Analytics into Context: Speaker: John C. Brocklebank, Ph.D., VP, SAS Solutions The Analytics Value Chain OnDemand Speaker: Piyanka Jain, Senior Manager, NA Business Development and Analytics, PayPal/eBay As stocks and housing prices rise and fall and consumer confidence is very volatile in today’s economic turmoil, In a product/services company, analytics generates its one area of continuous growth has been - sadly - crime

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. greatest value when a certain line-up of best practices other factor analysis and regression-based approaches to is performed, ranging from gross intelligence to a more survey analysis that we used initially. The predictive models detailed understanding. This is achieved with a “three described are currently in use and resulted in both greater pillar” analytical approach: [Measurement Framework, understanding of employee attitudes, and a revised “short- Portfolio Analysis, and Customer Analysis]. Within each of form” survey with fewer key questions identified by the these components, we move from a simpler “20,000 foot” decision trees as the most important predictors. view analysis, to deeper, more comprehensive analytics. In this talk, Piyanka Jain will cover these components in 11:25-12:10pm • Room: Golden Gate B detail, along with the tools and techniques required and Special Plenary Session gotchas to look out for. Auxiliary intelligence such as Multiple Case Studies: Anheuser-Busch, VOC (Voice of the Customer) and Competitor/Industry/ Economic landscape analysis, which delivers an [outside-in] the SSA, Netflix view of the business, will also be covered. Data Mining Lessons Learned - Technical & Business - From Applied Projects What you will walk away with is: Speaker: John Elder, Ph.D., Chief Scientist, 1. An understanding of the [analytics value chain], which Elder Research, Inc. sets predictive analytics into an impactful context 2. Analytics your organization needs, to better understand In the recounting of analytics projects, my favorite part is your business “the reveal”: where the idea that turned things around 3. Tools and methodologies best suited for the [three is disclosed. Often disarmingly simple (in retrospect) it is pillars] of analysis virtually always preceded by waves of failure. Yet failure, or 4. Challenges to prepare for, as you embark on these at least an environment shockingly tolerant of it, may be analyses essential to the emergence of such breakthroughs. 5. Organizational support needed for analytics execution. I will tell tales of some favorite “reveals” that led to 10:35-11:20am • Room: Salon 5 & 6 technical successes. But, a true win must also be a business s Track 2: Survey Analysis success. This requires dealing well with idiosyncratic Case Study: YMCA carbon-based life forms. So we’ll also discuss the (painfully Turning Member Satisfaction Surveys into an acquired) lessons in the parallel universe of business. Actionable Narrative 12:10-12:20pm • Room: Golden Gate B Speakers: Dean Abbott, President, Abbott Analytics & Bill Lazarus, President & CEO, Seer Analytics, LLC Gold Sponsor Presentations: Lightning Round Survey analysis often involves hand-tuned analysis Enhancing Rules-Based Approaches requiring weeks of labor to decipher the key relationships to Fraud, Corruption, and Bribery in survey responses. Proper coding of responses, colinearity, and missing data plague analysts in their Detection with Predictive Analytics pursuit of clear explanations of responder intent in the Anthony F. DeSantis, CFE, Sr Manager, Deloitte surveys. Additionally, while traditional statistical analyses, Financial Advisory Services LLP such as linear and logistic regression, can be used effectively in modeling survey responses, these models do As companies and investigators wrestle with the not resonate with the business community in the same way implementation and usage of comprehensive fraud, they do with statisticians. corruption, or bribery detection platforms and approaches, there is at least one clear trend that is emerging: best Employees are a key constituency at the Y and previous practices around comprehensive detection of this kind analysis has shown that their attitudes have a direct bearing going forward will focus not on purely rules-based on Member Satisfaction. This session will describe a approaches, nor will they focus on purely predictive successful approach for the analysis of YMCA employee methodologies, but will instead incorporate a hybrid surveys. Decision trees are built and examined in depth approach of the two. to identify key questions in describing key employee satisfaction metrics, including several interesting groupings of employee attitudes. Our approach will be contrasted with

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. 2:15-2:35pm • Room: Golden Gate B ParAccel: The Power & Promise Diamond Sponsor Presentation of Big Data Analytics Speaker: Dipesh Patel, Senior Manager, Product The Analytical Revolution Speaker: Colin Shearer, WW Industry Marketing, ParAccel Solutions Leader, IBM At this session, we’ll do a quick overview of the evolution The algorithms at the heart of predictive analytics have of the database and analytics marketplace starting been around for years - in some cases for decades. with the 1980’s to the present. In addition we’ll quickly But now, as we see predictive analytics move to the highlight how today’s challenges are forcing a fundamental mainstream and become a competitive necessity for rethink that undermines the status quo - many customers organizations in all industries, the most crucial challenges are discovering that ever more powerful hardware are to ensure that results can be delivered to where they can’t ultimately solve their most challenging analytic can make a direct impact on outcomes and business challenges. Finally we’ll cover several new technologies performance, and that the application of analytics can be and approaches that are emerging in the face of the Big scaled to the most demanding enterprise requirements. Analytics challenge. This session will look at the obstacles to successfully 12:20-1:30pm • Room: Golden Gate applying analysis at the enterprise level, and how today’s Birds of a Feather Lunch / Exhibits approaches and technologies can enable the true Sponsored by “industrialization” of predictive analytics. Lunch topics: Operationalizing Analytics 2:40-3:25pm • Room: Golden Gate B In-Database Analytics l Track 1: Crowdsourcing Data Mining Structured and Unstructured Data Analysis Case Study: University of Melbourne, Visual Discovery Chessmetrics HPN Crowdsourcing Data Prediction 1:30-2:15pm • Room: Golden Gate B Speaker: Anthony Goldbloom, Keynote: Lessons Learned in Chief Executive Officer, Kaggle Predictive Modeling for Ad Targeting Speaker: Sugato Basu, Ph.D., Data prediction competitions facilitate a step change Senior Research Scientist, Google in the evolution of analytics outsourcing. They offer companies a cost effective to harness the “cognitive AdWords Quality at Google executes across a rich set surplus” of researchers and analysts who are hungry for of prediction applications in order to meets its business real-world data and motivated to excel whatever the prize. objectives, which include providing a top user experience, Competitions are particularly effective because there achieving a strong ROI for advertisers, and securing are any number of techniques that can be applied to a revenue for Google. This talk will cover several of these modeling problem, but we can’t know in advance which prediction problems, including: will be most effective. By exposing the problem to a wide audience, competitions are a cost effective way to reach • Predicting creative quality and landing page quality the frontier of what is possible from a given dataset. In just • Estimating ad bounce rate a few months, competitions hosted by Kaggle have helped • Estimating ad relevance and examination probability further the state of the art in HIV research, chess ratings and have outperformed sports betting markets. Some of these must apply predictive modeling at a very large scale, involving billions of features and millions of users. This talk will discuss some of the practical lessons learned by the speaker while working on these problems at Google.

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. 2:40-3:25pm • Room: Salon 5 & 6 3:55-4:15pm • Room: Golden Gate B s Track 2: Black Box Trading l Track 1: Thought Leader Case Study: Cerebellum Capital Case Study: Health Insurance Black Box Trading: Analytics in the Analytics by Industry/Role Land of the Vampire Squid Speaker: Jack Phillips, CEO, International Speaker: Dave Andre, Ph.D., Chief Executive Officer Institute for Analytics and Chief Technology Officer, Cerebellum Capital Why are certain industries and roles being so heavily The field of quantitative finance, while it has attracted impacted by the use of advanced analytics and others criticism over the past few years, is an enormously rich yet not? The International Institute for Analytic’s CEO will first mine-laden domain for machine learning and statistics - full share with attendees the list of industries and functional of unsolved problems that require both new science and roles that his firm sees being most impacted this year and innovative engineering. Cerebellum Capital was founded why, and then present a specific case study from a leading to create a system capable of autonomously finding, health insurer. testing, refining, launching, improving, and when necessary decommissioning novel trading strategies. This talk will 3:55-4:15pm • Room: Salon 5 & 6 give a tour of the challenges and opportunities we have s Track 2: Software Lab found as a group of outsiders approaching this domain as a computer science problem. Lab Session: Live Topical Demo Institutionalizing Analytics: What got 3:30-3:50pm • Room: Golden Gate B you here won’t get you there l Track 1: e-Commerce and HR Speaker: Srikanth Velamakanni, CEO, Fractal Analytics Case Study: Monster.com A Holistic Predictive Analytics Methodology Industry leaders are very swiftly coming to an agreement Speaker: Yangling Zhang, Director of Business that analytics will be a critical component of the Intelligence, Monster Worldwide competitive strategy in the 21st century. To that end the early adopters and the followers have started leveraging Understanding online behavior is key to driving customers analytics in different ways. However to generate true to your online and offline store. By capturing “surfing” benefit companies need to institutionalize Analytics while data in log files, and tracing customers psychographics a whole host of companies are stopping at just leveraging via surveys, we are able to predict the most profitable Analytics to generate insights or to solve a given problem. customers for acquisition and retention. This session will In this presentation we will talk about what it means and also discuss targeting to present “the right offer to the what it takes to institutionalize analytics, and what habits right audience at the right moment.” organizations need to change to institutionalize Analytics.

3:30-3:50pm • Room: Salon 5 & 6 Attendees Will Leave With: s Track 2: Fraud Detection Case Study: U.S. Postal Service, Office of • how to convert an Analytics solution into an integral part Inspector General of business processes Fighting the Good Fraud Fight • how to drive decision making using Analytics Speaker: Antonia de Medinaceli, Senior Business • how to generate organizational buy-in to make Analytics Analyst, Elder Research, Inc. the front and center of business strategy

Fraud is a costly problem for many businesses, and the 4:15-4:35pm efforts required to protect against it further compound the Break / Exhibits price. We will discuss the cultural and business hazards of addressing versus ignoring fraud, as well as the enormous ROI possible when adaptable quantitative tools are used to detect ever-changing anomalous behavior. Case studies from some of our consulting engagements will highlight lessons learned about what makes a potential fraud detection project ripe for success.

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. 4:35-5:20pm • Room: Golden Gate B Dustin and John will present an approach that scores a l Track 1: Thought Leader prospect database with derived preference estimates Making Technologies Intelligent: Data, for individual electricity pricing plan features. Customers Patterns, Access, and Architecture make purchase decisions within hypothetical purchasing Speaker: Astro Teller, Ph.D., scenarios in a survey. Bayesian choice models predict the Director of New Projects, Google customer’s purchase probability and estimates the lift in demand (utilities) for different levels of features tested. Using examples from his past companies and projects, Predictive analytics is then used with database variables Astro will give a tour through four key elements required to to score every record in a prospect database with likely make products and services more intelligently automated. preference for individual features. Marketing decisions are The talk will cover each of these four elements (data, greatly enhanced by knowing which product features are patterns, access, and architecture), how each functions most appealing to individual prospects and messaging is separately and how they function together to allow for optimized. Lift in customer response will be presented automated products and services to be more intelligent and for them to be constantly improving in the automated 5:50-6:10pm • Room: Golden Gate B l Track 1: High Technology Retail intelligence they offer their users. The talk will also touch on feedback from users and user-interfaces for that Case Study: Hewlett-Packard feedback and how this plays into the intelligent technology Customer Repurchase Analysis - B2B Context: ecosystem and lifecycle. A Bayesian Framework Speaker: Jerry Shan, Principal Scientist, Hewlett-Packard 4:35-5:20pm • Room: Salon 5 & 6 s Track 2: Ensembles; Fraud Detection One of the primary objectives in database marketing is Case Study: PayPal / eBay computing the likelihood a customer will buy/transact Ensembles for Online within a time span (viz. next six months/one year). Various Analytic Scoring Engine techniques such as Logistic Regression, successfully Speakers: Michael Murff, Manager, Risk Decision employed in a B2C context, have been found less Management, PayPal efficient in a B2B context. In this session we describe a Hui Wang, PayPal methodology using a Bayesian analysis framework to assign each customer a probability score that they will There is a heavy reliance on logistic regression in many purchase within a time span. This provides the basis Risk applications in industry at large. Such ubiquity results by which customers may be ranked and targeted more in part from ease of interpretability and for implementation efficiently for marketing purposes. parsimony including fast execution times. However, many other algorithmic options exist such as CART, which can 5:25-6:10pm • Room: Salon 5 & 6 s Track 2: Uplift Modeling (True-Lift) produce many suites of trees that are aggregated for a common decision. We conduct some applied research to True-Lift Modeling: Mining for the Most Truly explore the efficacy of decision tree ensembles for PayPal Responsive Customers and Prospects fraud prediction. Both computational cost and predictive Speakers: Kathleen Kane, Principal Decision Scientist, effectiveness are taken into account. We find that bagging Fidelity Investments trees are a suitable contender against other methods, and Jane Zheng, Principal Decision Scientist, with some advantages. Fidelity Investments

5:25-5:45pm • Room: Golden Gate B Stop spending direct marketing dollars on customers who l Track 1: Product Recommendations would purchase anyway! Case Study: TXU Energy Message Optimization Using Discrete Choice True-lift modeling can identify: Analysis in the Energy Sector • which customers will purchase without receiving a Speakers: Dustin Cannon, Manager of Advanced marketing contact Analytics, TXU Energy • which customers need a direct marketing nudge to John Colias, Senior Vice President, make a purchase Decision Analyst, Inc.

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. • which customers have a negative reaction to marketing participants over a 6 month time period. Standardized (and purchase less if contacted) psychological tests were used to measure progress which This discussion will describe: leveraged reliability; however, meaningful analysis of individual and group progress was completed given the • the basic requirements needed to succeed with true-lift limitations of time and treatment participants. modeling • scenarios where this modeling method is most William Jackman: Proc Glimmix – An Overview applicable The GLIMMIX procedure is a new procedure in SAS/ • the pros and cons of various approaches to true-lift STAT software. It was an add-on product in SAS 9.1 on modeling the Windows platform. but now in SAS 9.2 is a production procedure. This presentation is intended for those with 6:10-7:30pm a general statistical background who want to learn what Reception / Exhibits PROC GLIMMIX is and what it does. It is not intended for those already using PROC GLIMMIX who want to learn 7:30-10:00pm • Room: Golden Gate B more details about how to use it. useR Group Meeting Speakers: David Smith, Vice President Tuesday, March 15, 2011 of Marketing for Revolution Analytics Byron Ellis, adBrite Exhibit Hall Open 9:45am - 4:30pm David Smith, Vice President of Marketing for Revolution 8:00-9:00am Analytics, relentless R Blogger co-author (with Bill Venables) of the tutorial manual An Introduction to R and Registration & Breakfast long-time R coder will provide a brief introduction to the R language. Anthony Goldbloom founder and CEO of 9:00-9:45am • Room: Salon 7 Kaggle will talk about the use of R in crowdsourcing data Keynote: The New Quantitative Era: Creating science. Predictive modeling competitions are shaping up Successful Business Change with Analytics to be the biggest thing in data science in 2011 - there are Thomas Davenport, President’s Distinguished Professor, multi-million dollar prizes on the way and vital scientific Babson College and Author, Competing on Analytics projects are being thrown open to competition for the first time. Approximately 25 percent of the models submitted The progenitor and connoisseur of “competing on in Kaggle competitions so far have been based on R, analytics” illustrates what it takes to create an analytics- the open source statistical modeling and programming driven business. Hint: it’s not about the data and it’s not language. Anthony Goldbloom, Kaggle’s founder and about the math. Tom is the authority on building broad CEO will describe the ideas underlying the competitions capabilities for enterprise-level business intelligence and and discuss the use of R in building predictive models. he’s back by popular demand. In this talk Tom addresses Anthony will also discuss an active competition to build a the organizational culture and business leadership required recommendation engine for R libraries. to make the most of the science of analysis, and shares stories of people who have made this transition and the 7:30-10:00pm • Room: Salon 5 & 6 resulting competitive edge their organizations exploit. Bay Area SAS Users Group Meeting Learn how to reap the rewards of business analytics from Speakers: David Bell, State of Calif, the man who laid the groundwork and wrote the book. Dept of Industrial Relations William Jackman, Kaiser Permanente 9:45-10:15 Break / Exhibits Real Data, Real Headache? Using Proc Mixed and Maximum Entropy Correlated Equilibria to Longitudinally Analyze Small Sample Data.

This presentation will demonstrate the power of mixed longitudinal hierarchical linear models (i.e., Proc Mixed) to measure progress of a treatment program of 10

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. 10:15-11:00am • Room: Golden Gate B That’s right, fleas! Fleas learn habits that place artificial limits Expert Panel: Kaboom! Predictive Analytics on themselves. Unfortunately, many analytic professionals Hits the Mainstream fall into this same trap. While there are options available Panelists: Colin Shearer, WW Industry today that can tremendously improve the efficiency and Solutions Leader, IBM scalability of an analytic environment, many companies are Wayne Thompson, Analytics Product Manager, SAS stuck in the old way of doing things and are failing to cash Andreas Weigend, Ph.D., weigend.com, Former Chief in on the benefits. Would you like to spend more time on Scientist, Amazon.com analysis and less time fighting to get your data together? If Panel moderator: Eric Siegel, Ph.D., Program Chair, so, then listen to this discussion on how to make sure your Predictive Analytics World organization has not fallen into a flea-like trap.

Predictive analytics has taken off, across industry sectors 11:25-12:10pm • Room: Golden Gate B and across applications in marketing, fraud detection, s Lab Session: Live Topical Demo credit scoring and beyond. Where exactly are we in Making Predictive Models Count the process of crossing the chasm toward pervasive Speaker: Kaiser Fung, Senior Director of Strategic deployment, and how can we ensure progress keeps up Analytics, Sirius XM Radio the pace and stays on target? By now, the value of marketing analytics is widely This expert panel will address: recognized among the business community. Widespread success has been reported using predictive models • How much of predictive analytics’ potential has been to complete tasks ranging from optimizing placement fully realized? of online advertisements to imputing people’s movie • Where are the outstanding opportunities with greatest preferences. What hasn’t been discussed much is that potential? building predictive models take valuable resources, that • What are the greatest challenges faced by the industry predictive models have modest accuracy, and that the in achieving wide scale adoption? business value of models is not fully considered. Without • How are these challenges best overcome? proper attention to these issues, analytics teams run the risk of over-promising, and under-delivering. 11:00-11:10am • Room: Golden Gate B Exclusive Announcement In this case study, we will outline the key steps of developing The $3 million Heritage Health Prize: predictive models that deliver true business value. This New Announcements on Contest Structure requires understanding not just the predictive performance Speaker: Robert O’Keefe, Corporate Senior VP, but also error rates, cost of errors, cost of investment, and Heritage Provider Network return on investment. The context is a real-world example of a predictive model used to target selected customers for Data competitions come of age: from movie more expensive marketing communication recommendations to life and death. Possibly the biggest news in predictive modeling in 2011 is Heritage Provider 11:25-12:10pm • Room: Salon 5 & 6 Network’s $3 million predictive modeling prize - the biggest s Lab Session: Live Topical Demo data mining competition ever. It requires data scientists to Bank On It! Use All Your Data to Maximize build algorithms that predict who will go to hospital in the next year, so that preventive action can be taken. We will Customer Satisfaction, Loyalty and Retention take this opportunity to release new information on the Speakers: Steven Ramirez, CEO, Beyond the Arc contest’s timeline and intermediate progress prizes. Robert Parolin, Technical Manager, IBM Business Analytics 11:10-11:20am • Room: Golden Gate B Gold Sponsor Presentations: Attend this session and learn how management Lightning Round consultants at Beyond the Arc helped one of the world’s largest banks build an effective Voice of the Customer Let’s Talk About Fleas! program, leading to increased customer satisfaction, Speaker: Bill Franks, Chief Analytics Officer, Global SAS loyalty and retention. Program, Teradata Corporation

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. Their secret? Integrating and then analyzing data from the discussion for five additional conference sessions across the enterprise - including unstructured text data. that address this topic, later the same day at Predictive Using IBM SPSS predictive analytic solutions, Beyond the Analytics World. Arc unlocked the value of data sources underutilized by the bank - such as survey comments, Twitter messages and 2:15-2:30pm • Room: Golden Gate B call center notes. Discover how predictive analytics enables Sponsor Presentations: Lightning Round you to use diverse feedback channels to attract and retain your most profitable customers, identify fast-moving 2:35-2:55pm • Room: Golden Gate B emerging issues, and improve the customer experience. l Track 1: Financial Services and Segmenting Case Study: Bank of the West Attend this session and learn: Effectively Incorporating Internet • How to integrate new data sources and customer touch Data in Customer Value Models points Speaker: Joel Kleinman, Vice President, Internet • How to prepare your data to maximize analytical Channel BI & Analysis, Bank of the West effectiveness Now that investments have been made in web analytics • How to collect, analyze and act upon diverse feedback infrastructure, how can one extract value from the vast channels in a holistic way amounts of data being collected and stored? A discussion of • How IBM SPSS modeling tools can help you easily successful methods used for converting internet data to value: analyze unstructured data • Developing an appropriate context for defining value 12:10-1:30pm • Room: Golden Gate for your business Birds of a Feather Lunch / Exhibits • Ensuring the right data is being collected and Sponsored by connected to other sources Lunch topics: • Interpreting (sometimes fuzzy) results • Creating business processes to communicate, integrate Operationalizing Analytics findings In-Database Analytics Structured and Unstructured Data Analysis 2:35-3:20pm • Room: Salon 5 & 6 Visual Discovery s Track 2: Predictive Analytics for SEM 1:30-2:15pm • Room: Golden Gate B Case Study: Orbitz Special Plenary Session Enhance Business Performance by applying The State of the Social Data Revolution Analytics to Search Engine Marketing (SEM) Speaker: Andreas Weigend, Ph.D., weigend.com, Speaker: Sameer Chopra, Vice President of Marketing Former Chief Scientist, Amazon.com Analytics, Orbitz Worldwide

With enterprises acting upon predictive models on a Increasing competition and more ads per keyword result second-by-second basis, the thirst for more powerful and in advertisers facing higher bid prices and an uphill task of relevant data is only growing. Quenching that thirst, there’s maximizing ROI. This drives a need for marketers to look no hotter emerging wealth of data than social data. In this for smarter ways to leverage their data and run their SEM session, Dr. Weigend will: campaigns. Getting smarter with the long tail of keywords is • Cover examples from industry verticals where he one key lever. projects social data will deliver the greatest impact, In this presentation, we will see examples of how data including insurance (should consumers be priced by mining can be used in SEM campaigns to improve their friends’ risky behavior?), retail (improving product efficiency: recommendations) and telecommunications. • Uncover the influence of companies in the very • Using Predictive Analytics to derive a value (revenue) business of collecting social data, including Google per click for each keyword -- leading to informed CPC and Facebook, as well as other companies that make bidding social data accessible to others. • Clustering the long tail to segments keywords and • Deliver the state of the social data revolution, framing inform keyword bidding.

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. 3:00-3:20pm • Room: Golden Gate B people at risk so as to delay or even prevent the onset of the l Track 1: Market Forecasting disease. We present a case study of developing the predictive Case Study: Eventbrite models for type 2 diabetes mellitus. The entire process Market Penetration: Is Your Growth on Track? starting with filtering raw claims data, building regression Speaker: Tilmann Bruckhaus, Director, Risk and models with diagnostics, solving for coefficients and Analytics Engineering, Eventbrite computing accuracy measures, is completed in 30 minutes for population sizes of over 2 million people with 2,000 predictors As our online ticketing business grows exponentially we on a Netezza TwinFin 12 platform running the In-Database model and track penetration in key markets at Eventbrite. Analytics library DB Lytix from Fuzzy Logix. This session shows how you can accomplish four key goals of understanding your growth: trace historical penetration 4:20-4:40pm • Room: Golden Gate B l Track 1: Retail and Insurance of the market, forecast future growth, predict market saturation, and determine which campaigns accelerate Case Study: California State market penetration. Automobile Association Addressing Analytics Challenges in 3:20-3:55pm the Retail and Insurance Industries Breaks /Exhibits Speaker: Noe Tuason, Research Manager, California State Automobile Association 3:55pm-4:15pm • Room: Golden Gate B l Track 1: Financial Service and Social Data This live demo will give you insight as to how Analytics Case Study: Bank of America Software is used to develop predictive models in the How Do Peers Evaluate Peer-to-Peer Lending? insurance industry. Noe Tuason, Customer Research Speaker: Aaron Lai, VP and Senior Quantitative Manager for Insurance at the California State Automobile Research Associate, Bank of America Association (CSAA) will speak to you about business challenges involving: Does peer-to-peer lending be different from the traditional bank-initiated lending? How do the customers/lenders • Modeling Automation in an Enterprise Software think and act? We used a comprehensive p2p data and Application found some interesting behavioral and analytical results on • Retaining High Profit while Minimizing Risk of Flight social interactions. • Pricing Models Using Claims Frequency and Severity Data.

3:55pm-4:15pm • Room: Salon 5 & 6 4:20-4:40pm • Room: Salon 5 & 6 s Track 2: Software Lab s Track 2: Social Data: Advanced Methods Lab Session: Live Topical Demo Social (Media) Network Analysis with NodeXL Large Scale Predictive Models for Speaker: Marc Smith, Ph.D., Chief Social Scientist, Connected Action Chronic Illness using Fuzzy Logix’s Networks are the common data structure that unify the In-Database Analytics Technology on Netezza otherwise diverse range of social media services. In this Speakers: Christopher Hane, Ph.D., Director, Ingenix session learn how to extract social networks from various social Innovation Lab media systems and analyze and visualize the structures found Partha Sen, President & CEO, Fuzzy Logix in collections of connections. Learn to use the free and open Bill Zanine, Business Solution Executive for Analytic NodeXL add-in for Excel 2007/2010 to analyze email, twitter, Solutions, Netezza facebook, youtube, www, flickr, and wiki networks.

Ingenix, an industry leader in healthcare information technology, has a data repository consisting of diagnosis, procedure, pharmacy, and lab claims covering 80 million lives over the last 17 years. In this presentation we show how this data is used to build statistical models which help in identifying individuals at risk of a certain disease, based on the similarity of their historical claims with others whose history and outcome is known. The objective is early intervention for

l Track 1 sessions are for all levels. s Track 2 sessions are expert/practitioner level. 4:45-5:30pm • Room: Golden Gate B 5:10-5:30pm • Room: Salon 5 & 6 l Track 1: Deployment: Insurance & Financial s Track 2: Social Gaming and Retention Case Studies: Fiserv and Varolii Case Study: Gaia Interactive Deploying Analytics with a Decoding Customer Attention and Retention Rules-Based Infrastructure Behavior Using Predictive Analytics Speaker: James Taylor, CEO, Decision Management Speaker: Tim Lopez, Vice President, Engineering, Solutions Gaia Interactive

An analytic model that is not in use has no value to an Before Facebook could command more attention than organization so analytic deployment is critical. And when television, millions of people spent multiple hours online analytic models must be applied to operational decisions through social gaming and virtual worlds, forming - micro decisions about a single customer, a single claim or environments prime for online customer behavior research, a single transaction - deploying analytics becomes more particularly around attention and retention. Predictive complex. This session will show how a business rules-based modeling technology has reached a level of sophistication infrastructure is ideal for deploying analytics into operational keen for forecasting in these environments, and leading systems. The power of rules to rapidly implement analytic online social gaming site, Gaia, extracts predictive behavior models and to turn those analytic models into decision- to direct its development process. In this session learn: making software components will be illustrated with real cases. • How to design the right model for decoding customer 4:45- 5:05pm • Room: Salon 5 & 6 behavior s Track 2: Social Data and Telecom • Common mistakes and assumptions to avoid in Case Study: Major North American Telecom behavior modeling Social Networking Data for Churn Analysis • How to leverage predictive analytics to drive customer Speaker: David Katz, Senior Analyst, Dataspora, and attention/retention President, David Katz Consulting

A North American Telecom found that it had a window into social contacts - who has been calling whom on its network. This data proved to be predictive of churn. Using SQL, and GAM in R, we explored how to use this data to improve the identification of likely churners. We will present many dimensions of the lessons learned on this engagement.

Wednesday, March 16, 2011 • Full-Day Workshops Time: 9:00am - 4:30pm | Room: Golden Gate B A & B The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes

Instructor: John Elder, Ph.D., Chief Scientist, Elder Research, Inc.

Intended Audience: Dr. Elder will share his (often humorous) stories from real- Interested in the true nuts and bolts of predictive analytics. world applications, highlighting the Top 10 common, but deadly, mistakes. Come learn how to avoid these pitfalls by A free copy of John Elder’s book, “Statistical Analysis and laughing (or gasping) at stories of barely averted disaster. Data Mining Applications” is included. If you’d like to become a practitioner of predictive analytics – or if you already are, and would like to hone Knowledge Level: your knowledge across methods and best practices, this Familiar with the basics of predictive modeling. workshop is for you!

Attendees will receive an electronic copy of the course What you will learn: notes via USB drive. • The tremendous value of learning from data • How to create valuable predictive models for your Workshop Description business Predictive analytics has proven capable of enormous • Best Practices by seeing their flip side: Worst Practices returns across industries – but, with so many core methods for predictive modeling, there are some tough questions Schedule that need answering: • Workshop starts at 9.00am • First AM Break from 10:00 - 10:15 • How do you pick the right one to deliver the greatest • Second AM Break from 11:15 - 11:30 impact for your business, as applied over your data? • Lunch from 12:30 - 1:15pm • What are the best practices along the way? • First PM Break: 2:00 - 2:15 • And how do you avoid the most treacherous pitfalls? • Second PM Break: 3:15 - 3:30 • Workshops ends at 4:30 This one-day session surveys standard and advanced methods for predictive modeling.

Dr. Elder will describe the key inner workings of leading algorithms, demonstrate their performance with business case studies, compare their merits, and show you how to pick the method and tool best suited to each predictive analytics project. Methods covered include classical regression, decision trees, neural networks, ensemble methods, uplift modeling and more.

The key to successfully leveraging these methods is to avoid “worst practices”. It’s all too easy to go too far in one’s analysis and “torture the data until it confesses” or otherwise doom predictive models to fail where they really matter: on new situations.

Thursday, March 17, 2011 • Full-Day Workshops Thursday, March 17, 2011 Data Preparation: Participants will clean fields in the data as necessary, and Time: 9:00am - 4:30pm | Room: Foothill F will derive new attributes as candidate inputs to predictive models ModelingParticipants will determine which Hands-On Predictive Analytics predictive modeling methods to use, and will build several models and assess them as prescribed in the Business Instructor: Dean Abbott, Understanding phase. The best model from the competing President, candidates will be selected to evaluate and deploy. Abbott Analytics Evaluation: Intended Audience: A final evaluation of the model(s) will be made, and the • Practitioners: Analysts who would like a tangible expected financial benefit of the model(s) will be forecast introduction to predictive analytics or who would like to and graphed. As time permits, an “ensemble” model experience analytics using a state-of-the-art data mining composed of all the models built by participants will be software tool. created to compare with the best individual models - we’ll • Technical Managers: Project leaders, and managers often find that the big “uber-model” is the best model of all. who are responsible for developing predictive analytics solutions, who want to understand the process. Deployment: Strategies for real-world model deployment will be Knowledge Level: assessed, including the application of the predictive model Familiar with the basics of predictive modeling. for its intended purpose - to produce scores that predict Workshop Description “tomorrow” across today’s customer data. Once you know the basics of predictive analytics, there’s no Participant background: better way to dive in than operating real predictive modeling Participants are expected to know the principles of software yourself - hands-on. “Get your hands dirty” by trying predictive analytics. This hands-on workshop requires out state-of-the art modeling methods on real data. Working all participants to be involved actively in the model to solve a specific business problem, you will design and building process, and therefore must be prepared to work execute on a core analytical approach. Prep the data, set up independently or in a small team throughout the day. The the modeling, push “go” and check out the results. instructor will help participants understand the application of predictive analytics principles, and will help participants “Hands-on Predictive Analytics” puts predictive analytics overcome software issues throughout the day. into action. This one-day workshop leads participants through the industry standard data mining process, from Software: Business Understanding through Model Deployment, While the vast majority of concepts covered during this approaching each stage of this process by driving a state-of- workshop apply to all predictive analytics projects - regardless the-art data mining software product. In this way, attendees of the particular software employed - this workshop’s hands-on gain direct experience applying this “best practices” experience is achieved via SAS Enterprise Miner. A license will process, and ramp up on an industry-leading tool to boot. be made available to participants for use on that day (included with workshop registration). Key process stages covered during the workshop include: Hardware: Training Computers Are Included Business Understanding: Each workshop participant will have access to a computer Participants will review a problem description from a with SAS Enterprise Miner installed for the duration of the business perspective, and design one or more solutions to workshop. that problem using predictive analytics. The solution will include one or more predictive models as determined by Attendees receive a course materials book and an official the participants. These models will be assessed according certificate of completion at the conclusion of the workshop. to the business objective(s) already defined. Schedule Data Understanding: • Workshop starts at 9:00am From a given data set (supplied), participants will examine • Morning Coffee Break at 10:30am - 11:00am characteristics of the data (a “data audit”) and identify • Lunch provided at 12:30pm - 1:15pm potentially problematic issues. Fields with insufficient • Afternoon Coffee Break at 2:30pm - 3:00pm information will be discarded. • End of the Workshop: 4:30pm

Friday, March 18, 2011 and Saturday, March 19, 2011

1½ - days | Room: Executive Conference Center Room 203 Times: Friday, March 18: afternoon Saturday, March 19: full day

Net Lift Models: Optimizing the Impact of Your Marketing

Instructor: Kim Larsen, Market Share Partners You will learn how to: Intended Audience: • Build net lift models that maximize the difference in Statisticians, business analysts, and market researchers response rates between the clients who receive the who build predictive models for marketing and retention offer and those that do not (the control group) campaigns • Identify good incremental lift predictive variables • Build net lift models using a variety of techniques Workshop Description • Evaluate and deploy net models The true effectiveness of a marketing campaign isn’t response rate, it is the incremental impact - that is, Specific topics covered include: additional revenue directly attributable to the campaign • Net lift models versus propensity models that would not otherwise have been generated. Yet • Example net lift models in action traditional targeting criteria are often designed to find • Comparison of net lift modeling approaches, including clients that are interested in the product, but would regression- and non-regression-based methods, and have bought it anyway, whether or not they received the Generalized Naive Bayes Classifier a promotion. In such cases, the incremental impact is insignificant and the marketing dollars could have been Access to working code and real examples. In order to spent elsewhere. illustrate net lift modeling in action and provide options for “take-home” usage, the instructor will provide 1) example Net Lift Models are designed to maximize incremental datasets and 2) examples of code implementing incremental impact by targeting the undecided clients that can be lift modeling methods, including the following SAS macros: motivated by marketing. These “swing customers” are INCREMENTAL, INFORMATION, GNBCREG, NWOE (net akin to the swing states of a political election; data miners weight of evidence), and NIV (net information value). could learn a lot from political campaigns. While very advanced attendees may optionally bring their Beyond targeted marketing, Net Lift methodology delivers own laptop and software to try out net lift modeling during tremendous performance improvements for deployed the workshop, this concentrate topics course does not churn models - retaining “savables” while avoiding the include enough time for guided hands-on instruction; it is adverse “reverse” affects retention outreach triggers for not designed or intended as a “hands-on” training program. some customers - as well as other innovative business applications of this advanced analytical method. This workshop is offered in cooperation and special arrangement with SAS Institute. This workshop demonstrates how to build Net Lift Models that optimize the incremental impact of marketing Schedule campaigns, covering the pros and cons of various core Day 1: 1:30pm - 5:00pm analytical approaches. Day 2: 9:00am - 4:30pm Coffee breaks are included both days, and a 12:30pm lunch is included on Day 2.

San Francisco • March 14-18, 2011 Munich • April 5-6, 2011 Sydney • April 13-14, 2011 Toronto • April 26-29, 2011 Paris • June 6-7, 2011 Stockholm • September, 2011 New York • October 17-21, 2011 Melbourne • November, 2011 London • November 30-December 1, 2011

San Francisco • March 14-15, 2011 New York • October 17-21, 2011 London • November 30-December 1, 2011

SMX Munich • April 5-6, 2011 SMX Toronto • April 26-29, 2011 SMX London Advanced • May 16-17, 2011 SMX Paris • June 6-7, 2011 SMX Stockholm • September, 2011

New York • October 19-20, 2011 Hamburg • November 7-8, 2011 London • November 30-December 1, 2011

San Francisco • March 17-18, 2011 New York • October 17-18, 2011

Vancouver • September, 2011 Helsinki • October, 2011 New York • October 19-21, 2011

Fort Lauderdale, October 30-November 3, 2011

Munich • June 8-9, 2011

Berlin • May 25-26, 2011 London • November 14-15, 2011

Denver • September, 2011

London • September 26-27, 2011 www.risingmedia.com Thank you to our sponsors

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Sugato Basu, Ph.D., Eric Siegel, Ph.D., Program Chair, Senior Research Scientist, Google Predictive Analytics World Sugato Basu’s areas of research expertise Eric Siegel, Ph.D., President of Prediction include machine learning, data mining, Impact, Inc., is an expert in predictive analytics predictive modeling and optimization, with special and data mining and a former computer science professor emphasis on scalable algorithm design for text and social at Columbia University, where he won the engineering network analysis. He did his Ph.D. in machine learning school’s award for teaching, including graduate-level from UT Austin, and worked at SRI International on the courses in machine learning and intelligent systems - the CALO project before joining Google. He has written academic terms for predictive analytics. After Columbia, Dr. multiple papers, book chapters, and encyclopedia articles Siegel co-founded two software companies for customer on clustering, semi-supervised learning, record linkage, profiling and data mining, and then started Prediction social search / routing, rule mining, and optimization, and Impact in 2003, providing predictive analytics services and has won best paper awards at the KDD, ICML and SDM training to mid-tier through Fortune 100 companies. conferences. He continues to serve on multiple conference / journal committees and NSF panels on machine learning Dr. Siegel is the instructor of the acclaimed training and data mining. program, Predictive Analytics for Business, Marketing and Web, and the online version, Predictive Analytics Applied. Keynote: Lessons Learned in Predictive Modeling He has published over 20 papers and articles in data mining for Ad Targeting research and computer science education, has served on 10 conference program committees, has chaired a AAAI Thomas Davenport, President’s Symposium held at MIT, and is the founding chair of Predictive Analytics World. Distinguished Professor, Babson College; Author, Competing on Keynote: Five Ways Predictive Analytics Cuts Enterprise Risk Analytics; Co-Founder, International Institute for Analytics Thomas Davenport is currently the President’s Distinguished Professor of Information Technology and Management at Babson College. He is the former director of research centers at SAS, Ernst & Young, and McKinsey, and has taught at Harvard Business School, Dartmouth’s Tuck School of Business, and the University of Texas at Austin.

Thomas is a frequent contributor to Harvard Business Review and other leading journals. His recent article, “Competing on Analytics,” was Harvard Business Review’s most requested article reprint of 2006. He is the author or co-author of twelve books, including the bestsellers, “Working Knowledge: How Organizations Manage What They Know and Process Innovation: Reengineering Work through Information Technology.” His latest book, “Competing on Analytics: The New Science of Winning,” has become a best-seller and is being translated into 10 languages.

Keynote: The New Quantitative Era: Creating Successful Business Change with Analytics

Dean Abbott workforce management software company that was sold President, to Witness Systems in 2005. He invented the scheduling Abbott Analytics engines utilized in the company’s products that took Dean Abbott is President of Abbott Analytics in San Diego, historical time-series of call data of different types and California. Mr. Abbott has over 21 years of experience human resource constraints and created schedules to applying advanced data mining, data preparation, and maximize performance using innovative successive data visualization methods in real-world data intensive approximation and simulation techniques. Dr. Andre also problems, including fraud detection, risk modeling, text co-founded and is a partner in Just Passing Through, LLC, mining, response modeling, survey analysis, planned a company that creates, runs, and films team-based puzzle giving, and predictive toxicology. In addition, Mr. Abbott races. Dr. Andre earned B.S. and B.A. degrees in Symbolic serves as chief technology officer and mentor for start-up Systems and Psychology from Stanford University and a companies focused on applying advanced analytics in their PhD in Artificial Intelligence from U.C. Berkeley, where he consulting practices. was awarded a Hertz Fellowship. In addition to holding numerous patents for his inventions, he is the author of Mr. Abbott is a seasoned instructor, having taught a wide more than 60 peer-reviewed publications in the areas range of data mining tutorials and seminars for a decade of statistical machine learning, robotics, reinforcement to audiences of up to 400, including PAW, KDD, AAAI, learning, evolutionary computation, and parallel IEEE and several data mining software users conferences. processing, as well as a book on automatic circuit design. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by Case Study: Black Box Trading: Analytics in the Land of a wide range of audiences, including analytics novices, the Vampire Squid data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses John C. Brocklebank, Ph.D., for major software vendors, including SPSS-IBM Modeler Vice President, (formerly Clementine), Unica PredictiveInsight (formerly SAS Solutions OnDemand Affinium Model), Enterprise Miner (SAS), Model 1 (Group1 John Brocklebank, VP of SAS Solutions OnDemand, Software), and hands-on courses using Statistica (Statsoft), oversees SAS’ growing menu of hosted applications, Tibco Spotfire Miner (formerly Insightful Miner), and CART directing product research and development, marketing, (Salford Systems). sales, program management, IT, quality assurance, and documentation. Current products and services address Case Study: Turning Member Satisfaction Surveys into critical issues in marketing, fraud detection, drug an Actionable Narrative development and education, as well as Advanced Analytics Full-day Workshop: Hands-On Predictive Analytics Lab and Education Value Added Assessment System (EVAAS) ASP educational testing (K-12). Dave Andre, Ph.D., Chief Executive Officer and Chief Technology Previously a Senior Research and Development Director Officer, Cerebellum Capital for SAS’ Analytic ASP, John joined SAS in 1981. He earned a Ph.D. in statistics from NC State University, where David co-founded Cerebellum Capital, Inc, where he is he was adjunct professor from 1999-2000. John holds presently the Chief Executive Officer and Chief Technology six US patents and has published some 30 papers and Officer. He is also a research advisor to BodyMedia, Inc. presentations. Since 2006, Dr. Andre has been principal investigator on a multi-million dollar research grant to non-invasively predict Sponsor Presentation: The Hefty Toll of Fraud: How blood glucose from a multi-sensor stream of physiological to Leverage Predictive Analytics and Social Network data. From 2002 until 2007, Dr. Andre was Director Analysis of Informatics at BodyMedia, where he and his team collected and analyzed clinical and user data and created state of the art statistical machine learning models that map sensor data to detailed and specific statements about human physiology and activity, such as energy expenditure and sleep state.

In 1996, Dr. Andre co-founded Blue Pumpkin Software, a

Tilmann Bruckhaus, Ph.D., Dustin Cannon, Director, Risk and Analytics Engineering, Manager of Advanced Analytics, Eventbrite TXU Energy Tilmann BruckhausHis areas of research expertise include Dustin Cannon is currently a Manager Market Research machine learning, data mining, predictive modeling and at TXU Energy in Irving, TX. His primary role on the TXU optimization, with special emphasis on scalable algorithm Energy market research team is to provide support for design for text and social network analysis. He did his advanced analytics. Prior to joining TXU Energy Dustin was Ph.D. in machine learning from UT Austin, and worked the Director of Advanced Analytics at M/A/R/C Research at SRI International on the CALO project before joining for 12 years. Google. He has written multiple papers, book chapters, and encyclopedia articles on clustering, semi-supervised Case Study: Message Optimization using Discrete learning, record linkage, social search / routing, rule Choice Analysis in the Energy Sector mining, and optimization, and has won best paper awards at the KDD, ICML and SDM conferences. He continues to serve on multiple conference / journal committees and NSF panels on machine learning and data mining.

Case Study: Market Penetration: Is Your Growth on Track?

Sameer Chopra, for the firm, resulting in record ROI for eBay’s Internet Vice President of Marketing Analytics, Marketing group. He led the foundational analytics effort Orbitz Worldwide, Inc. in building Triton, eBay’s automated SEM platform. Sameer Sameer Chopra is VP of Marketing Analytics at Orbitz was also a core member of the Trust & Safety Group in the Worldwide, a leading online global travel company. He formative years, where he applied predictive models to has 15 years of experience in applying data mining and detect fraud proactively. predictive analytics across various business domains at both Fortune 500 firms and startups -- saving companies Sameer also ran eBay Inc’s Global Experimentation millions of dollars and boosting their top line. Sameer Analytics Group -- the testing program was highlighted has led Analytical teams in areas including Revenue in the Feb 2009 issue (“How to Design Smart Business Management, Fraud Detection, Web Analytics, CRM, Experiments”) of the Harvard Business Review (HBR). Internet Marketing, and Experimentation (A/B & Multivariate Testing). Sameer holds a Masters degree in Operations Research from the Massachusetts Institute of Technology (MIT). Prior to Orbitz, Sameer was in the leadership team at He holds an undergraduate degree (Summa Cum Laude) Intuit’s Small Business Group (SBG), where he was leading in Mathematics with a minor in Computer Science from Marketing Analytics and Web Testing. He instilled a Allegheny College, where he graduated valedictorian. culture of test & learn and successfully helped SBG drive double-digit millions in annual incremental revenue from LinkedIn: http://www.linkedin.com/pub/sameer- leveraging Test Analytics. chopra/1/410/267

Sameer is an eBay Inc. veteran where he was Director of Case Study: Enhance Business Performance by Applying Analytics. He led Search Engine Marketing (SEM) Analytics Analytics to Search Engine Marketing (SEM)

John Colias, John F. Elder IV, Ph.D, Senior Vice President, Chief Scientist, Decision Analyst, Inc. Elder Research, Inc. John leads the Advanced Analytics Group at Decision Dr. John Elder heads a data mining consulting team Analyst. In this capacity he designs, implements, and with offices in Charlottesville Virginia, Washington DC, interprets custom quantitative research. His work is focused Mountain View California, and Manhasset New York (www. on applying advanced metrics and models in the areas of datamininglab.com). Founded in 1995, Elder Research, product optimization, market segmentation, competitive Inc. focuses on investment, commercial and security analysis, brand development, pricing strategies, predictive applications of advanced analytics, including text mining, modeling, forecasting, and panel data econometrics. forecasting, stock selection, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit John has more than 25 years of experience with scoring, market timing, and fraud detection. econometric and simulation models. He has designed marketing research in a broad spectrum of industry John obtained a BS and MEE in Electrical Engineering categories, including prescription drug, packaged goods, from Rice University, and a PhD in Systems Engineering high-tech and computer, telecommunications, insurance, from the University of Virginia, where he’s an adjunct restaurants, automobile, and retail gasoline. John holds professor teaching Optimization or Data Mining. a Ph.D. in Economics from The University of Texas at Prior to 15 years at ERI, he spent 5 years in aerospace Austin with specializations in econometrics and modeling defense consulting, 4 heading research at an investment methods. management firm, and 2 in Rice University’s Computational & Applied Mathematics department. Dr. Elder has Case Study: Message Optimization using Discrete authored innovative data mining tools, is a frequent Choice Analysis in the Energy Sector keynote speaker, and was co-chair of the 2009 Knowledge Discovery and Data Mining conference, in Paris.

Anthony F. DeSantis, John’s courses on analysis techniques -- taught at dozens CFE, Senior Manager, of universities, companies, and government labs -- are Deloitte Financial Advisory Services LLP noted for their clarity and effectiveness. Dr. Elder was Mr. DeSantis is a Senior Manager in the Data Analytics honored to serve for 5 years on a panel appointed by practice within Deloitte Financial Advisory Services LLP the President to guide technology for National Security. with more than 12 years experience specializing in the His book with Bob Nisbet and Gary Miner, ”Handbook forensic analysis of electronic data. His technical experience of Statistical Analysis & Data Mining Applications“, won includes the analysis of complex structured and unstructured the PROSE award for Mathematics in 2009. His book data, management and implementation of small-scale with Giovanni Seni, Ensemble Methods in Data Mining: information management systems, the design and operation Improving Accuracy through Combining Predictions, was of relational databases in investigations, litigations, claims published in February 2010. J processing and settlement administration environments and the use of databases and forecasting methodologies Case Study: Data Mining - Lessons Learned for identifying indicators of fraud. Mr. DeSantis is a Certified Full-day Workshop: The Best and the Worst of Fraud Examiner and has presented on International Predictive Analytics: Predictive Modeling Methods and eDiscovery Issues, Investigative Data Considerations, Common Data Mining Mistakes Technology to Detect Potential Violations of the U.S. Foreign Corrupt Practices Act, and Healthcare Data Mining.

Gold Sponsor Presentation: Enhancing Rules-Based Approaches to Fraud, Corruption, and Bribery Detection with Predictive Analytics

Bill Franks, Kaiser Fung, Chief Analytics Officer, Global SAS Program, Senior Director of Strategic Analytics, Teradata Corporation Sirius XM Radio Bill is Chief Analytics Officer for Teradata’s global SAS Kaiser Fung is Senior Director of Strategic Analytics at Sirius program. In this role, he provides thought leadership to XM Radio, and author of ”Numbers Rule Your World: the clients on trends in the Advanced Analytics space, how hidden influence of probability and statistics on everything clients can leverage those trends, and how Teradata and you do“ (McGraw-Hill, 2010). At Sirius XM Radio, his team SAS can support those efforts. Bill also oversees the Business is responsible for applying advanced statistical techniques Analytic Innovation Center, which is an analytics think tank to solve business problems, such as multivariate testing jointly sponsored by Teradata and SAS. Bill has extensive and predictive modeling. Mr. Fung has over 15 years of background helping clients derive value through the use of experience building predictive models for the media, data analysis and modeling, with a focus on making results Internet and banking companies. He holds an MBA from accessible to the business community. His work has spanned Harvard Business School, and statistics and operations clients in a variety of industries ranging in size from Fortune research degrees from Cambridge and Princeton. He 100 companies to small non-profit organizations. Bill is also a teaches statistics for management at New York University. faculty member of the International Institute for Analytics. Lab Session: Making Predictive Models Count Gold Sponsor Presentation: Let’s Talk About Fleas!

Anthony Goldbloom, Best product” for merchants to identifying the factors Chief Executive Officer, that indicates to an event happening in future, her role Kaggle at PayPal is to lead the organization into learning more Anthony is the Founder and CEO of Kaggle Pty Ltd, a global about their products and customers through rigorous platform for data prediction competitions. Anthony assists understanding and mining of data. companies with framing modeling tasks as data prediction competitions, ensuring competitions reflect real-life projects, Prior to this, Piyanka provided database driven analytical and that the results can be integrated into their day-to-day services to Adobe Product Marketing and Campaign operations. Marketing. At Adobe, Piyanka designed and analyzed programs, used program learnings to drive planning Before founding Kaggle, Anthony worked in the for direct marketing communication programs, created macroeconomic modeling areas of the Reserve Bank segmentation and targeting strategy, mined data for of Australia and before that the Australian Treasury. In predictors of campaign performance, built statistical these roles, Anthony was responsible for building and models to identify top converters. maintaining macroeconomic models of the Australian economy. He used these models to generate economic Prior to this, Piyanka founded Out of Box Media in 2003. The forecasts and to simulate the impact of changes in interest company’s first Ad Campaign started with Chinese boxes and rates and fiscal policy on the Australian economy. then grew to incorporate other types of containers including Pizza boxes, cake boxes etc. Her experience in advertising at Anthony holds a first class honours degree in economics Google, and her science and engineering degrees from Texas and econometrics from the University of Melbourne A&M and University of Minnesota enabled her to reach out to and has published in The Economist magazine and the a wide variety of customers. Australian Economic Review. Case Study: Putting Predictive Analytics into Context: Case Study: Crowdsourcing Data Prediction The Analytics Value Chain

Christopher Hane, Ph.D., Kathleen Kane, Director, Principal Decision Scientist, Ingenix Innovation Lab Fidelity Investments Christopher Hane, Ph.D. is Senior Scientist and Director in Kathleen Kane is a Principal Decision Scientist with the Ingenix Innovation Lab. He has 20 years of experience the Modeling and Analytics Strategy team at Fidelity designing and delivering decision support applications in Investments in Smithfield, RI. The Modeling and supply chain and health care markets. As a Director in the Analytics Strategy team works with partners in marketing, Ingenix Innovation Lab, Dr. Hane is responsible for design and distribution and product to answer business questions construction of leading edge analytic applications and services. using statistical data analysis. Kathleen has more than ten years of data mining experience in the financial services Lab Session: Large Scale Predictive Models for Chronic industry. Kathleen received her BA in engineering from Illness using Fuzzy Logix’s In-Database Analytics Dartmouth College, and her MS from the MIT Sloan School Technology on Netezza of Management.

Piyanka Jain, True-Lift Modeling: Mining for the Most Truly Senior Manager, NA Business Responsive Customers and Prospects Development and Analytics, PayPal Piyanka’s strength and interest lies in deriving actionable David Katz, insights from data to enable informed trade-offs and decision Senior Analyst, Dataspora, and President, making. She never stops asking “Why” questions as customer David Katz Consulting. behavior, action and attitude continuously intrigue her. David Katz has been in the forefront of applying statistical models and database technology to marketing problems Currently, she is engaged in leading strategic analytics since 1980. He holds a Master’s Degree in Mathematics for PayPal that enables fact-based decisions on fine- from the University of California, Berkeley. He is one of the tuning customer experience and product development. founders of Abacus Direct Marketing and was previously the From building a recommendation engine for “Next Director of Database Development for Williams-Sonoma.

He is the founder and President of David Katz Consulting, developing analytical solutions and marketing strategy. specializing in sophisticated statistical services for a Mr. Kleinman received his M.B.A. from UC Davis in variety of applications, with a special focus on the 2008 and a B.A. in Psychology from San Francisco State Direct Marketing Industry. David Katz has an extensive University in 1998. background that includes experience in all aspects of direct marketing from data mining, to strategy, to test Case Study: Effectively Incorporating Internet in design and implementation. In addition, he consults on Customer Value Models a variety of data mining and statistical applications from public health to collections analysis. He has partnered Rado Kotorov Ph.D., with consulting firms such as Ernst and Young, Prediction Director, SP Management & Competitive Strategy, Impact, and most recently on this project with Dataspora. Information Builders Dr. Rado Kotorov, technical director of Strategic Product Case Study: Social Networking Data for Churn Analysis Management for Information Builders, is responsible for emerging reporting, analytic, and visualization Joel Kleinman, technologies. He is driving the adoption of RIA, AJAX, Vice President, Internet Channel BI & Analysis, search, and other Web 2.0 and mobile technologies to Bank of the West make BI and enterprise analytics more accessible, intuitive, Mr. Kleinman specializes in internet marketing analytics and collaborative. Active Reports, Magnify, and Power and finance. In his current role, he manages business Painter are just a few of the applications created through intelligence and financial planning for the internet channel his efforts. at Bank of the West. Prior to joining Information Builders he was chief financial Between 2002 and 2008, Mr. Kleinman managed internet officer with responsibilities in IMS and IT at DeBacker research and analytics for Washington Mutual Bank. Prior to Management LLC. He was also BI director at CMI that, he spent several years at internet marketing agencies Marketing, where he managed the implementation of BI

and financial reporting solutions, data warehouses, and Kim Larsen, custom applications. Dr. Kotorov has developed analytic VP of Analytical Insights, models and applications for the pharmaceutical, retail, Market Share Partners CPG, financial, and automotive industries. He has a Ph.D. Kim Larsen is a VP of Analytical Insights at Market Share in decision and game theory, and economics from Bowling Partners, a leading marketing science company based in Los Green State University. He has also published on business Angeles. Prior to Market Share Partners, he worked in the processes, emerging technologies, CRM, KM, innovation, advanced analytics department of a Fortune 500 financial and entrepreneurship. services institution. Kim has worked in the area of data mining and statistical modeling industry since 2001 and Case Study:Using Predictive Analytics To Reduce Credit programmed in SAS since 1995. Throughout his professional Risk In Auto Dealer Financing career, he has worked on and managed a wide array of data mining and analytical problems including price optimization, Max Kuhn, media mix optimization, demand forecasting, customer Director, Nonclinical Statistics, segmentation, and predictive modeling. Pfizer Max Kuhn is a Director of Nonclinical Statistics at Pfizer Kim frequently speaks at data mining conferences around Global R&D in Connecticut. He has been apply models in the world in the areas of segmentation and predictive the pharmaceutical industries for over 15 years. modeling and has been an acclaimed keynote speaker at Predictive Analytics World and other conferences. His main He is a leading R developer and the author of several R areas of research include additive non-linear modeling and packages including the CARET package that provides net lift models (incremental lift models). a simple and consistent interface to over 100 predictive models available in R. Kim holds a B.S. in mathematics and economics and an M.S. in statistics. Mr. Kuhn has taught courses on modeling within Pfizer and externally, including a class for the India Ministry of Workshop: Net Lift Models: Optimizing the Impact of Information Technology. Your Marketing

Full-day Workshop: R for Predictive Modeling: A Hands- Bill Lazarus, On Introduction President and CEO, Seer Analytics, LLC Aaron Lai, Bill Lazarus is President and CEO of Seer Analytics, LLC, a Prior VP and Senior Quantitative Research technology-based research and analytics company in Tampa Associate, Bank of America Florida. Seer produces actionable intelligence to help clients Aaron Lai, CFA, is the former VP and Senior Quantitative make smarter decision and drive business performance. Research Associate for the Innovative Delivery Solutions team of the Bank of America in San Francisco. Aaron is Bill has worked in the non-profit and corporate sectors. He now Senior Manager of Marketing Analytics for Blue Shield joined the Dun & Bradstreet Corporation in 1984 where he of California. He has over 10 years experience in consumer held positions of increasing responsibility at three divisions analytics and database marketing for financial institutions and the corporate office. and has patents currently pending. He has published in academic journals and has presented in international In 1994, Bill founded Lazarus Associates, a consulting data-mining conferences. He also served on the CFA firm specializing in the use of data and analytical tools to examination curriculum committee and the CFA Magazine support strategy development. In January 2001 Bill and Advisory committee. He received a B.Sc.(Hons) in Finance his partners formed Seer Analytics to provide consumer (City University of Hong Kong), a B.Sc.(Econ)(Hons) in research products and services requiring a substantial Management Studies (University of London), a MBA technological infrastructure. (Purdue University), and a M.Sc. in Sociology (University of Oxford). Since its formation Seer has developed proprietary software and processes to automate complex data Case Study: How Do Peers Evaluate Peer-to-Peer collection, analysis and reporting. Seer reports embed Lending? sophisticated analytics yet are designed to be accessible

and meaningful to a non-technical audience. They Antonia de Medinaceli, have been used at every level of the organization, from Senior Business Analyst, frontline operations to boardroom planning. Bill received Elder Research, Inc. his BA from the University of Wisconsin, his MA from Antonia de Medinaceli has extensive experience in all the University of Toronto, and his SM and PhD from the aspects of the data mining process, and has solved Massachusetts Institute of Technology. challenges in many industries, including financial, crime analysis, and customer relationship management (CRM) Case Study: Turning Member Satisfaction Surveys into industries. Her consulting experience is both domestic an Actionable Narrative and international. Antonia is experienced with most of the leading statistical software packages. In addition to her Tim Lopez, consulting experience, she has taught data mining short Vice President, courses with the Elder Research team. She has degrees Engineering, Gaia Interactive in Computer Science and Systems Engineering from the Tim Lopez is VP of Engineering at Gaia Interactive, a social University of Virginia. site that delivers over a billion page views per month, Case Study: Fighting the Good Fraud Fight processes over 100,000 transactions and gets more than 7 million unique monthly users. Prior to that he worked in a number of different fields, including consumer databases and Java development tools.

Case Study: Decoding Customer Attention and Retention Behavior Using Predictive Analytics

Michael Murff, Jack Phillips Manager, Risk Decision Management, CEO, PayPal International Institute for Analytics Michael Murff is Manager, Decision Management, and Web Description: http://iianalytics.com/wp-content/ Statistician working for eBay analytics groups, and recently uploads/2010/03/jackleadership.pngIIA marks the sixth with PayPal focused on automated fraud detection for successful start-up venture for Jack Phillips over the past 20 PayPal Risk Management. Mike holds advanced degrees years. Jack specializes in spotting emerging job functions, specializing in Applied Econometrics and research methods; and building successful information publishing and research he enjoys statistical computing on big data. He is expert in firms to help those professionals make better decisions. SAS macro language and has since utilized many other data mining tools against massive datasets. Most recently his Prior to joining IIA, Jack held operating and founding roles work on Rapid Model Refresh was featured at M2010. at INFONXX (now kgb USA), ISI Emerging Markets (now Euromoney, PLC), CCBN (now Thomson/Reuters) and the Case Study: Ensembles for Online Analytic Scoring Institute for Applied Network Security (recent private sale). Engine Jack began his career as an investment banker at Morgan Robert O’Keefe, Stanley & Co. in New York, and at the Long-Term Credit Corporate Senior Vice President, Bank of Japan in Tokyo, and held senior operating Heritage Provider Network positions at various McGraw-Hill business units in 1994 before becoming an entrepreneur. Jack is a graduate of Robert O’Keefe is a Corporate Senior Vice President Harvard Business School and Williams College. for HPN. Mr. O’Keefe is also the CEO of HPN’s affiliates Bakersfield Family Medical Center and Coastal Case Study: Health Insurance Communities Physician Network. Through the years, he Analytics by Industry/Role has helped implement and been the executive in charge of many of the HPN’s Chronic Disease Management and Utilization Management programs. Partha Sen, President & CEO, Exclusive Announcement: The $3 million Heritage Fuzzy Logix Health Prize: New Announcements on Contest Structure Partha Sen Partha Sen is the Co-founder and Chief Executive Officer of Fuzzy Logix. Partha has a passion for Dipesh Patel, solving complex business problems using quantitative Senior Manager, Product Marketing, methods, data mining and pattern recognition. Since ParAccel 1995, Partha has pursued this passion and has developed numerous high-performance quantitative algorithms. Dipesh Patel joined ParAccel in 2010 after a varied career Today, these algorithms and models are the basis for the at IBM, Intel, NetApp and CommVault. He is an enthusiastic products being brought to market by Fuzzy Logix. champion of new and emerging technologies that can be put into solving real-world problems today and tomorrow. Before founding Fuzzy Logix, Partha worked at Bank of From Big Analytics, to Linux, to Data Deduplication, he’s America where he held senior management positions in able to highlight connections across multiple technologies the commercial and investment bank and in the portfolio and solution stacks to understand what approaches yield the strategies group. Prior to working at Bank of America, most value at the lowest level of risk. Partha held managerial positions at Ernst and Young and Tata Consultancy Services. He has a Bachelor of Gold Sponsor Presentation: The Power & Promise of Big Engineering degree from the Indian Institute of Technology Data Analytics and an MBA from Wake Forest University.

Lab Session: Large Scale Predictive Models for Chronic Illness using Fuzzy Logix’s In-Database Analytics Technology on Netezza

Jerry Shan, Ph.D., tackled numerous successful data mining applications in Principal Scientist, areas including finance, broadcasting, market research and Hewlett-Packard defense, and from 1996 to 1998 he jointly let the international Jerry is a Principal Scientist in the Business Optimization initiative to create and publish the CRISP-DM methodology. Lab, focused on developing software and services based In 1998 SPSS acquired ISL, and Shearer became responsible on decision sciences and data mining that will bring for a worldwide team of data mining consultants and for SPSS personalized experiences to individuals and operational Advanced Data Mining Group. He subsequently held various efficiencies to the enterprise. Jerry earned a PhD in positions at SPSS including global head of Product Marketing Statistics from Stanford University in 1995. Jerry has a and Senior Vice President for Market Strategy. SPSS was successful track record of leading technology creation and acquired by IBM in October 2009, and Shearer moved to his transferring solutions to businesses. Jerry has presented current position in January 2010. numerous papers at, Joint Statistical Meetings, International Symposium on Forecasting, Forecasting Summit, CPG Diamond Sponsor Presentation: The Analytical Revolution (Consumer Packaged Goods) Forecasting, and INFORMS. Expert Panel: Kaboom! Predictive Analytics Hits the Jerry has numerous publications in IEEE journals and Mainstream International Journal of Forecasters. Jerry has 12 granted patents and more than 20 open applications on predictive Marc Smith, Ph.D., analytics, change-point detection, and econometric analysis Chief Social Scientist, and modeling. Connected Action Case Study: Customer Repurchase Analysis - B2B Marc Smith is a sociologist specializing in the social Context: A Bayesian Framework organization of online communities and computer mediated interaction. Smith leads the Connected Action consulting group and lives and works in Silicon Valley, California. Smith Colin Shearer, co-founded the Social Media Research Foundation (www. WW Industry Solutions Leader, smrfoundation.org), a non-profit devoted to open tools, IBM data, and scholarship related to social media research. Colin Shearer is Worldwide Industry Solutions Leader for the SPSS brand at IBM. With a background in Computer Science Smith is the co-editor with Peter Kollock of Communities in and Artificial Intelligence at the University of Aberdeen, Cyberspace (Routledge), a collection of essays exploring the specializing in machine learning, he has been involved since ways identity; interaction and social order develop in online 1984 in applying advanced software solutions to business groups. Along with Derek Hansen and Ben Shneiderman, problems. Previously with SD-Scicon and Quintec Systems, he is the co-author and editor of, ”Analyzing Social Media he was one of the founders of Integral Solutions Ltd. (ISL) in Networks with NodeXL: Insights from a connected world“, 1989, and was a pioneer of data mining in the early 1990s. He from Morgan-Kaufmann which is a guide to mapping was creator and architect of ISL’s award-winning Clementine connections created through computer-mediated interactions. system (now IBM SPSS Modeler) which introduced the visual workbench approach for data mining. Shearer’s team at ISL Smith received a B.S. in International Area Studies from

Drexel University in Philadelphia in 1988, an M.Phil. in social management firm whose investments are continuously theory from Cambridge University in 1990, and a Ph.D. in designed, executed, and improved by a software system Sociology from UCLA in 2001. He is an affiliate faculty at the based on techniques from statistical machine learning. Astro Department of Sociology at the University of Washington is also co-founder and a current Director of BodyMedia, and the College of Information Studies at the University of Inc, a leading wearable body monitoring company. From Maryland. Smith is also a Distinguished Visiting Scholar at 2007 to 2010, Astro was the founding CEO of Cerebellum the Media-X Program at Stanford University. Capital, Inc. From 1999 to 2007, Dr. Teller was the founding CEO of BodyMedia, Inc. Prior to starting BodyMedia, Dr. Social (Media) Network Analysis with NodeXL Teller was co-founder, Chairman, and CEO of Sandbox Advanced Development, an advanced development James Taylor, technology company. Before his tenure as a business CEO, executive, Dr. Teller taught at Stanford University and was Decision Management Solutions an engineer and researcher for Phoenix Laser Technologies, James is the CEO and Principal Consultant of Decision Stanford’s Center for Integrated Systems, and The Carnegie Management Solutions. James is the leading expert in Group Incorporated. Dr. Teller holds a Bachelor of Science decision management and decisioning technologies. in computer science from Stanford University, Masters of James is passionate about using decisioning technologies Science in symbolic and heuristic computation, also from like business rules and predictive analytics to help Stanford University, and a Ph.D. in artificial intelligence from companies improve decision making and develop smarter Carnegie Mellon University, where he was a recipient of the and more agile processes and systems. James has over prestigious Hertz fellowship. 20 years developing software and solutions for clients and has led Decision Management efforts for leading As a respected scientist and seasoned entrepreneur, companies in insurance, banking, health management and Teller has successfully created and grown five companies telecommunications. He is also an active speaker, blogger and holds numerous U.S. patents related to his work in (primarily at JTonEDM) and author. hardware and software technology. Dr. Teller’s work in science, literature, art, and business has appeared in James delivers webinars, workshops and sales training for international media from the New York Times to CNN clients and vendors. He is a keynote speaker at conferences to NPR’s “All Things Considered.” Teller regularly gives such as the Business Rules Forum, Predictive Analytics invited talks for national and international technology, World and IBM’s Business Analytics Forum. James was co- government, and business forums on the subject of the author of “Smart (Enough) Systems” (Prentice Hall, 2007) future of intelligent technology. with Neil Raden, and has contributed chapters on decision management and business rules to multiple books including Making Technologies Intelligent: Data, Patterns, Access, “Applying Real-World BPM in an SAP Environment”, and Architecture “The Decision Model”, “The Business Rules Revolution: Doing Business The Right Way” and “Business Intelligence Wayne Thompson, Implementation: Issues and Perspectives”. James is a faculty Analytics Product Manager, member of the International Institute for Analytics and has SAS experience at FICO, PeopleSoft R&D, and Ernst & Young. Wayne Thompson is an Analytics Product Manager at SAS. His primary efforts are centered on bringing relevant Full-day Workshop: Driving Enterprise Decisions with product/solution feedback from customers to the SAS Business Analytics data mining development teams to extend SAS Institute’s Case Study: Deploying Analytics with a Rules-Based leadership position in the data mining market. SAS products Infrastructure that Wayne brought to market include SAS Text Miner, SAS Model Manager and SAS Scoring Accelerator for Teradata. Astro Teller, Ph.D., Current focus initiatives include easy to use self-service Director of New Projects, data mining tools for business analysts and in-database Google analytics. He has been employed at SAS since 1992. During Dr. Astro Teller is currently Director of New Projects for his tenure at SAS, Wayne also served as a Statistical Services Google, working to help the company explore new specialist for the Education Division in which he developed potential business areas. Astro is also a co-founder and and taught applied statistical courses as well as collaborated a current Director of Cerebellum Capital, a hedge fund on several data analysis projects for clients from many

industries. Wayne received his Ph.D. and M.S from the University of Tennessee in 1992 and 1987, respectively. During his PhD program, he was also a visiting scientist at the Institut Superieur d’Agriculture de Lille, Lille, France.

Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream

Noe Tuason, Research Manager, California State Automobile Association Noe is currently the Customer Research Manager for insurance at the California State Automobile Association (CSAA). Prior to joining CSAA, Noe worked for Deloitte Consulting as a project lead at the Advanced Quantitative Services Group and for the Allstate Research and Planning Center data mining and modeling Groups as a senior researcher. He has published papers on research methods in various journals and presented in conferences. Related to the current topic, Noe presented a paper at the Advanced Research Techniques Forum comparing the use of logistic regression, decision trees, and hybrid combination of the two on predicting short-term defection. He also taught research methods at the Cal State University East Bay and was a research co-investigator at the UCSF Comprehensive Cancer Center.

Case Study: Addressing Analytics Challenges in the Retail and Insurance Industries Andreas S. Weigend, Ph.D., Hui Wang, weigend.com, Former Chief Scientist, Director of Decision Management, Amazon.com PayPal Andreas S. Weigend, Ph.D. was Chief Scientist at Amazon. Description: Hui WangHui Wang is Director of Decision com, where he specialized in understanding how people Management at PayPal for last 7 years. Prior to that she behave online, helping Amazon build its customer-centric, worked for FICO as an Analytics Lead. Her education measurement-focused culture. includes advanced degrees in Statistics and Computer Science from UC Berkeley. Dr. Wang leads the fraud He now works as an independent consultant with firms modeling team at PayPal, and is expert in many analytical including Alibaba, Lufthansa, MySpace, and Nokia, helping technologies; additionally, she plays an designer role in some of the most brilliant executives around the world architecting many next generation analytic products to leverage user data to produce innovative products and enable Ensemble modeling for real time fraud systems. business models. He also teaches at Stanford and UC Berkeley, and is often invited to speak at international events. Case Study: Ensembles for Online Analytic Scoring Engine The State of the Social Data Revolution Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream

Bill Zanine, Ms. Zhang previously provided ad agency Hill Holliday Business Solution Executive for analytics and strategies for clients CVS, State Farm, Analytic Solutions, Netezza Dunkin’ Donuts, Bank of America, Dell, Verizon, etc. Bill Zanine is the Business Solution Executive for Analytic Before that, she contributed her effort at CRM leader Solutions at Netezza, an IBM Company. Throughout his Siebel Systems, Healthcare leader CareGroup with risk career he has focused on designing and developing adjustment models. innovative technology solutions for complex business problems in the areas of defense, manufacturing, financial Ms. Zhang holds an MBA in Management Information services and healthcare and life sciences. Today, he uses Systems and MS in Economics. that experience to help customers leverage Netezza’s advanced database and analytic technologies to solve their She was a faculty member teaching at Central University problems in ways they never thought possible. of Finance and Economics in China before she came to United States. Lab Session: Large Scale Predictive Models for Chronic Illness using Fuzzy Logix’s In-Database Analytics Case Study: A Holistic Predictive Analytics Methodology Technology on Netezza Jane Zheng, Yangling Zhang, Principal Decision Scientist, Director of Business Intelligence, Fidelity Investments Monster Worldwide Jane Zheng is a Principal Decision Scientist with the Ms. Yangling Zhang is Director of Business Intelligence at Modeling and Analytics Strategy team at Fidelity. Jane has Monster Worldwide. Ms. Zhang is responsible for providing more than 15 years experience in modeling, data mining data mining, statistical analyses, measurement, and and market research in financial services and consulting. experimental design, predictive modeling and methodology advice as well as strategy recommendations to support Jane received her BA in engineering and her MS in Monster’s prospects acquisition, customer retention, profit mathmatical statistics. maximization, product development and site optimization. True-Lift Modeling: Mining for the Most Truly Prior to joining Monster, Ms. Zhang was leading the Responsive Customers and Prospects analytics efforts to provide digital strategies for a consulting firm Molecular. She had worked with an impressive list of clients worldwide including: Monster Worldwide, BrownCo, PCconnections, VistaPrint, UPS, Liberty Mutual, Philips, KeyBank, Talbots, PerkinElmer, Analog Devices, Nikon, Lenox, Monster, RH Donally, Project Management Institute, Harvard Business School Publishing, TripAdvisor, Adidas, Upromise, Susquehanna, Solidworks, Synovate and Arrow electronics.

© 2011 Rising Media, Inc. 40 www.predictiveanalyticsworld.com/sanfrancisco/2011 Registration special for Predictive Analytics World attendees: $250 o ! Email [email protected] to claim your discount code.

of attendees rated % the instructor Training Seminar: 98 Excellent or Very Good Predictive Analytics for Business, Marketing and Web April 14-15, 2011: New York, NY Participant Comments October 27-28, 2011: San Francisco, CA Source: businessprediction.com On-site: Customized On-demand: Online course “An excellent overview on how to start using predictive analytics in any organization! In just two weeks I already have buy-in from upper manage- The official ainingtr program of Predictive Analytics World ment to explore ways to use predictive analytics to improve up-sells, cross sells and to determine what lifestyle imagery to display to our users. I'm super excited about these projects.” Predictive Analytics for Business, Marketing and Web is a concentrated training program that includes interactive Jennifer Boland breakout sessions and a brief hands-on exercise. In two Onsite Marketing Analyst days we cover: Sierra Trading Post

• The techniques, tips and pointers you need in order to run a “At Intuit we're already using data as an asset on the web, but this course successful predictive analytics and data mining initiative makes it very concrete how we can take it to the next level.”

• How to strategically position and tactically deploy predictive Jared Waxman analytics and data mining at your company Web Analytics Leader Intuit • How to bridge the prevalent gap between technical understanding and practical use “The best part of this training program is the clear correlation to practical applications in everyday business.” • How a predictive model works, how it's created and how much revenue it generates Reto Matter VP Business Intelligence • Several detailed case studies that demonstrate predictive PlanetOut Inc. analytics in action and make the concepts concrete “Eric is an A+++ instructor with a great sense of humor.” In order to meet the unique training needs of business decision makers and analytics practitioners, this training Ali Maleki program is: Project Manager • Business-focused. Unlike other training programs that also Computer Tech. Consultants cover scienti c, engineering and medical applications of data mining and analytics, this seminar focuses squarely on solving “A very insightful and interesting seminar. I plan to put data mining and business and marketing problems with these methods. predictive analytics to work for us right away thanks to your ability to make this an approachable subject.” • Comprehensive across business needs. Within this realm, however, we step beyond the standard application of Rob Ford response modeling for direct marketing to solve the wider Director Pricing range of business problems listed below. Getty Images

• Vendor-neutral and method-neutral. This training program, for more testimonials, see businessprediction.com which is not run by an analytics software vendor, provides a balanced view across analytics tools and methods.

Instructor: Eric Siegel, Ph.D. For a detailed program description and registration The Program Chair of Predictive Analytics information, visit: businessprediction.com World, Dr. Siegel is a seasoned consultant in predictive analytics, an acclaimed industry Produced by: instructor, and a former award-winning Prediction Impact, Inc. professor at Columbia University. www.predictionimpact.com