Keynote Speaker – Predictive Analytics World Talk: “New Challenges in Predictive Analytics: Social Networking, Direct-Response Marketing, and Understanding Customer Behavior,” San Francisco, CA, February 18-19, 2009.

Predictive Analytics World is the business­ focused event for predictive analytics professionals, managers and commercial practitioners. This conference delivers case studies, expertise and resources to achieve:

• Bigger wins: Strengthen the impact of predictive analytics deployment
• Broader capabilities: Establish new opportunities with predictive analytics

The Top Experts

PAW’s October 2009 program is packed with the top predictive analytics experts, practitioners, authors and business thought leaders, including keynote speakers:

Usama Fayyad, Ph.D.
CEO, Open Insights
Former Chief Data Officer,
Yahoo!

Stephen L. Baker
author of The Numerati
Senior writer at
BusinessWeek

Eric Siegel, Ph.D.
Conference Chair
Predictive Analytics World
See Eric’s Video Intro

Predictive Analytics World focuses on concrete examples of deployed predictive analytics. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling, and what kind of business impact it delivers.

PAW October 2009 includes over 20 speakers with case studies from leading enterprises such as:

• Aflac
• Amway
• The Coca­Cola Company
• Citizens Bank
• Financial Times
• Infinity Insurance
• Lifeline Screening
• The National Rifle Association
• The New York Times
• Optus (Australian telecom)
• PREMIER Bankcard
• Reed Elsevier
• Sprint­Nextel
• Sunrise Communications (Switzerland)
• Target
• US Bank
• Walden University
• Zurich
• plus special examples from Anheuser­Busch, Disney, Hewlett­Packard, HSBC,
  Pfizer, Social Security Administration, WestWind Foundation and others.

Cross­Industry Applications

Predictive Analytics World is the only conference of its kind, delivering vendor­neutral sessions across verticals such as banking, financial services, e­commerce, entertainment, government, healthcare, high technology, insurance, non­profits, publishing, and retail.

And PAW covers the gamut of commercial applications of predictive analytics, including response modeling, customer retention with churn modeling, product recommendations, online marketing optimization, behavior­based advertising, email targeting, insurance pricing and credit scoring.

Why bring together such a wide range of endeavors? No matter how you use predictive analytics, the story is the same: Predictively scoring customers optimizes business performance. Predictive analytics initiatives across industries leverage the same core predictive modeling technology, share similar project overhead and data requirements, and face common process challenges and analytical hurdles.

People Who Need People

Vendors:

• Meet the vendors and learn about their solutions, software and services
• Discover the best predictive analytics vendors available to serve your needs
• Learn what they do and see how they compare.

Colleagues:

• Mingle, network and hang out with your best and brightest colleagues
• Exchange experiences over lunch, breaks and the conference reception, connecting with those professionals who face the same challenges as you.

Get Started

If you’re new to predictive analytics, kicking off a new initiative, or exploring new ways to position it at your organization, there’s no better place to get your bearings than Predictive Analytics World. See what other companies are doing, witness vendor demos, participate in discussions with the experts, network with your colleagues and weigh your options!

Speakers

Dean Abbott, President, Abbott Analytics

Dean Abbott is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real­world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. In addition, Mr. Abbott serves as chief technology officer and mentor for start­up companies focused on applying advanced analytics in their consulting practices.

Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including PAW, KDD, AAAI, IEEE and several data mining software users conferences. He is the instructor of well­ regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including Clementine (SPSS), Affinium Model (Unica Corporation), Enterprise Miner (SAS), Model 1 (Group1 Software), and hands­on courses using Tibco Spotfire Miner (formerly Insightful Miner), and CART (Salford Systems).

Session: How to Improve Customer Acquisition Models with Ensembles
Workshop: Hands­On Predictive Analytics

Joel Appelbaum, Chief Analytics Officer, Zurich

Joel A. Appelbaum is chief analytics officer of Zurich North America Commercial’s Programs and Direct Markets business unit. Mr. Appelbaum specializes in providing predictive modeling solutions for the business unit and Underwriting transformation initiatives for Zurich.

Mr. Appelbaum has more than 20 years of insurance industry experience. Before joining Zurich, he was a construction risk officer for CNA Insurance. He currently serves on the CPCU Society board of Governors’, and was an instructor for the Insurance School of Chicago for more than 15 years.

Mr. Appelbaum holds a Bachelor of Science degree from DePaul University and a Master of Business Administration with honors in international business from Lake Forest Graduate School of Management. He is a designated Charter Property Casualty Underwriter (CPCU), holds an Associate in Risk Management (ARM) designation, and an Associates in Insurance Services designation (AIS).

Session: Top 10 Ways to be Successful in Implementing Predictive Modeling in Insurance Commercial Markets

Heather Avery, Manager, Business Analytics, Aflac

Heather Avery is manager of Business Analytics within the Shared Services division at Aflac. Heather joined Aflac in 2001 and has held analyst positions in Policy Service, Change Management, Strategy & Planning, and Marketing within Aflac; most recently serving as a business process consultant in Shared Services. Heather holds a master’s degree in computer science and a bachelor’s degree in psychology from Columbus State University, and is currently pursuing a master’s degree in business administration from Auburn University.

In her current role, Heather manages the daily operations of the Business Analytics team. This team partners with the Administrative business areas to lead select strategic initiatives, provide actionable, knowledge­based analytics, and build foundational capabilities that support management of operational efficiency and service delivery.

Session: Establishing a Customer Retention Analytics Framework

Stephen L. Baker, Senior writer, BusinessWeek

Stephen L. Baker, author of The Numerati, is a senior writer at BusinessWeek, covering technology. Previously he was a Paris correspondent. Baker joined BusinessWeek in March, 1987, as manager of the Mexico City bureau, where he was responsible for covering Mexico and Latin America. He was named Pittsburgh bureau manager in 1992. Before BusinessWeek, Baker was a reporter for the El Paso Herald­Post. Prior to that, he was chief economic reporter for The Daily Journal in Caracas, Venezuela. Baker holds a bachelor’s degree from the University of Wisconsin and a master’s from the Columbia University Graduate School of Journalism. He blogs at TheNumerati.net and Blogspotting.net, and can be found on Twitter at @stevebaker.

Keynote: Opportunities and Pitfalls: What the World Does and Doesn’t Want from Predictive Analytics
Panel: Predictive Analytics and Consumer Privacy

Michael Berry, Founder and Principal, Data Miners, Inc.

Michael Berry is author of some of the most widely­read and respected books on data mining. He is an active, hands­on practitioner with over 20 years in the field, for the past 12 years with Data Miners, a consultancy he founded.

 

Session: Predicting Future Subscriber Levels

Erick Brethenoux, Vice President, Corporate Development, SPSS Inc.

Erick Brethenoux’s responsibilities within SPSS include mergers and acquisitions, strategic planning, strategic partnering and future scenarios analysis. Brethenoux also plays a major role in the industry analyst activities and various operational missions within the company.

Prior to joining SPSS, Erick Brethenoux was VP of Software Equity Research at Lazard Fréres, New York, from 1997 to 2004. Brethenoux had a threefold mission: providing institutional investors with equity research and recommendations, analyzing startup companies for potential venture capital funding or initial public offering and forecasting and monitoring trends and market dynamics in merger and acquisition activities.

Brethenoux has published extensively in the domains of artificial intelligence systems, system sciences, applied mathematics, complex systems and cybernetics. He has held various academic positions at the University of Delaware and the Polytechnic School of Africa in Gabon.

Brethenoux received his third year Manager of Information Systems degree from France and M.S. from West Chester University of Pennsylvania. His research as a Ph.D. candidate at University of Delaware was aimed at knowledge engineering, computational linguistics and connectionism modeling within a cognitive sciences multidisciplinary program.

Session: The Perfect Storm: The Rise of Predictive Analytics

 

Srivatsava Daruru, Research Assistant, Intelligent Data Exploration and Analysis Laboratory, Dept. of Electrical and Computer Engineering, The University of Texas at Austin

Based in Austin, Texas, Srivatsava Daruru leads the parallel data mining research efforts at the IDEAL Laboratory, UT Austin. Srivatsava Daruru’s research has focused on three areas: association rule mining, search engine and multi­document relevancy ranking, and building massively parallel data mining applications. He has led industry research projects involving very large Netflix, YahooMovies, and France Telecom datasets. He presented his most recent work titled “Pervasive Parallelism in Data Mining: Dataflow solution to Co­clustering Large and Sparse Netflix Data” at the Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, July 2009. He is currently working on a terascale density­based clustering algorithm for the Texas Advanced Computing Center.

Mr. Daruru’s degrees include Bachelor of Technology in Computer Science and Engineering, International Institute of Information Technology, Hyderabad and Master of Science in Computer Science, University of Texas at Austin, TX.

Session: Churn, Baby, Churn: Fast Scoring on Large Telecom Dataset

Ozgur Dogan, Vice President, Merkle

Ozgur has 12 years of experience in building and implementing analytics solutions for many different clients from credit card, consumer lending, insurance, media, CPG, pharma and business to business verticals.

Ozgur’s Database Marketing experience stretches across many functional areas such as strategy, analytics, infrastructure and data content for many Fortune 500 clients. Ozgur is the recipient of multiple awards within Merkle, including the Exceptional Client, Operational Excellence, Database Marketing Excellence and the Chairman’s Award, which is the highest recognition within Merkle. Ozgur’s Team also won the NCDM Excellence Award in the Modeling and Analytics Area in 2006.

Prior to joining Merkle, he held Senior Analyst, Manager and Director Positions with leading direct marketing firms such as FedEx, NextCard and Wells Fargo Financial. Ozgur holds a BS Degree in Industrial Engineering and a technical MBA degree with focus on Marketing and Management Information Systems from The University of Georgia.

Session: Segmented Modeling Applications in Health Care Industry

John F. Elder, Ph.D., CEO and Founder, Elder Research, Inc.

Dr. John F. Elder heads a data mining consulting team with offices in Charlottesville, Virginia and Washington DC. Founded in 1995, Elder Research, Inc. focuses on scientific and commercial applications of pattern discovery and optimization, including stock selection, image recognition, text mining, biometrics, drug efficacy, credit scoring, cross-selling, investment timing, and fraud detection.

John obtained a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he’s an adjunct professor, teaching Optimization or Data Mining. Prior to 13 years leading ERI, he spent 5 years in aerospace defense consulting, 4 heading research at an investment management firm, and 2 in Rice’s Computational & Applied Mathematics department.

Dr. Elder has authored innovative data mining tools, is active on Statistics, Engineering, and Finance conferences and boards, is a frequent keynote conference speaker, and is General Chair of the 2009 Knowledge Discovery and Data Mining conference in Paris. John’s courses on data analysis techniques – taught at dozens of universities, companies, and government labs – are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security. His book on Practical Data Mining, with Bob Nisbet and Gary Minor, will appear in May 2009.

Session: The High ROI of Data Mining for Innovative Organizations
Workshop: The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes

Usama Fayyad, Ph.D., CEO, Open Insights

Dr. Usama Fayyad is CEO of Open Insights, a data strategy, technology and consulting firm he founded to help enterprises understand data strategy and deploy data­driven solutions that effectively and dramatically grow revenue and competitive advantages. Up until September 2008, he was Yahoo!’s chief data officer and executive vice president of Research & Strategic Data Solutions. Fayyad was the industry’s first chief data officer, responsible for Yahoo!’s global data strategy, architecting Yahoo!’s data policies and systems, prioritizing data investments, and managing the Company’s data analytics and data processing infrastructure. Fayyad also founded and managed the Yahoo! Research organization with offices around the world. Yahoo! Research is building the premier scientific research organization to develop the new sciences of the Internet, on­line marketing, and innovative interactive applications.

Prior to joining Yahoo!, Fayyad co­founded and led the DMX Group, a data mining and data strategy consulting and technology company that was acquired by Yahoo! in 2004. DMX Group International still operates in the Middle East and is headquartered in Dubai. In early 2000, he co­founded and served as CEO of digiMine Inc. (Revenue Science, Inc.), a data analysis and data mining company that built, operated and hosted data warehouses and web analytics for some of the world’s largest enterprises in online publishing, retail, manufacturing, telecommunications and financial services. The company today specializes in Behavioral Targeting and advertising networks. Fayyad’s professional experience also includes five years spent leading the data mining and exploration group at Microsoft Research and building the data mining products for Microsoft’s server division. From 1989 to 1996 Fayyad held a leadership role at NASA’s Jet Propulsion Laboratory (JPL), where his work in the analysis and exploration of scientific databases gathered from observatories, remote­sensing platforms and spacecraft garnered him the top research excellence award that Caltech awards to JPL scientists, as well as a U.S. Government medal from NASA.

Fayyad earned his Ph.D. in engineering from the University of Michigan, Ann Arbor (1991), and also holds BSE’s in both electrical and computer engineering (1984); MSE in computer science and engineering (1986); and M.Sc. in mathematics (1989). He has published over 100 technical articles in the fields of data mining and Artificial Intelligence, is a Fellow of the AAAI (association for Advancement of Artificial Intelligence) and a Fellow of the ACM (association of Computing Machinery), has edited two influential books on the data mining and launched and served as editor­in­chief of both the primary scientific journal in the field of data mining (Data Mining and Knowledge Discovery) and the primary newsletter in the technical community published by the ACM: SIGKDD Explorations. He is active in the academic community and holds adjunct professor positions in Australia and Hong Kong. For more details on Usama’s background, see his personal web site at: www.fayyad.com/usama.
Keynote: Predictive Analytics over On­line and Social Network Data

Sherry Bennett­Flatt, Group Leader, Business Intelligence, Laureate Higher Education Group

Sherry Bennett­Flatt, is Group Leader for the Business Intelligence department at Laureate Higher Education Group, a for­profit education company that own and operates both campus based and on­line universities. Her team is responsible for creating analytical business solutions for universities such as Walden, Kendall College, University of Liverpool and more. Sherry Bennett­Flatt has over 15 years of experience applying advanced modeling and data mining techniques to business opportunities, including among others, risk modeling, response modeling, survey analysis, planned giving, segmentation, etc. In addition, Sherry Bennett­Flatt has extensive experience in the design and development of database and data warehousing solutions for both for­profit and non­profit companies.

Session: The Use of Lead Scoring Solutions in the For­Profit Education Industry

Seth Grimes, Principal Consultant, Alta Plana Corporation

Seth Grimes is an analytics strategist with Washington DC based Alta Plana Corporation. He consults on data management and analysis systems for public and private­sector clients internationally. He is also contributing editor and Breakthrough Analysis columnist for Intelligent Enterprise magazine, founding chair of the Text Analytics Summit, and a leading industry analyst covering text analytics. He writes and speaks on business intelligence, data management and analysis systems, text mining, visualization, and related topics.

Session: Predictive Text Analytics

Michael Grundhoefer, Marketing Analytics, Market Information & Research, US Bank

Mike Grundhoefer is Assistant Vice President of Marketing Analytics at U.S. Bank, the sixth largest bank in the United States, where he leads a team of analysts responsible for all direct marketing models including response, attrition, usage, balance, and uplift models. These models are used in campaigns that produce over six million pieces of mail annually. Mike has over 15 years experience in model development. He holds a Masters in Applied Economics from Marquette University.

Session: Raising the Bar in Cross­Sell Marketing With Uplift Modeling

Mikael Hagström, Executive Vice President, EMEA and Asia Pacific, SAS 

As Executive Vice President, Mikael Hagström is passionate about providing a culture where innovation can flourish, resulting in market leadership for the organization and its customers.

Hagström leads a growing global team of more than 4,000 professionals in over 50 countries throughout Europe, Middle East, Africa and Asia Pacific. With a more than 20­year track record of leading high-performance organizations, Hagström is responsible for delivering consecutive revenue growth, ensuring profit, harnessing the potential in the current market and preparing the organization for the future.

From 1998 to 2000, as country manager of SAS Norway, Hagström restructured the office and led SAS Norway to record growth, doubling new sales each year for three consecutive years. Over the next few years, additional geographies and P&Ls were consistently added to his growing level of responsibilities. Hagström, who joined SAS Sweden in 1989, moved to the company’s European headquarters in Heidelberg in 1993 where he was promoted to vice president of Sales for EMEA. He currently works from SAS Worldwide Headquarters in Cary, North Carolina.

Hagström is vice­chair of the American Chamber of Commerce to the European Union (AmCham EU) Executive Council, a member of the Executive Committee of the United States Council for International Business (USCIB) and is a frequent speaker on the multinational business climate at the World Economic Forum and OECD in particular. He is a board member, head officer or chairman of more than 30 SAS subsidiaries. He holds a Master of Science degree in Industrial Automation Engineering and
Administration.

Panel: Predictive Analytics and Consumer Privacy

Mike Kinlaw, Manager/Lead Statistician, CMI Customer Analytics, Alticor (Amway)

Mike Kinlaw has masters in statistics and has worked at Amway for 7 years providing statistical support for multiple divisions. In his current capacity, he has lead several research projects including the development of a global segmentation model and a distributor life time value model.

Session: Establishing a Performance­Based Culture with Predictive Analytics

Eric A. King, Founder and President, The Modeling Agency (TMA)

Eric has represented machine learning technology, productized data mining solutions, and managed predictive analytics projects and teams for over 19 years. In its 10th year and with a bench of 18 senior­level data miners, TMA enables those who are data­rich yet information­poor to establish and maintain their own internal predictive modeling practice.

Moderator

Ram Krishnamurthy, Group Director, Marketing Strategy & Insights, The Coca­Cola Company 

Ram Krishnamurthy is the Group Director, Marketing Strategy & Insights at The Coca­Cola Company. Ram currently overseeing projects to gather and deploy insights relating to marketing productivity and investment allocation for Coke worldwide. He is a market generalist market researcher with quantitative leanings and an abiding interest in understanding why it is such a struggle to get at accurate predictions with the data we have and the applications available. To that end, he has been a driver for one of the world’s largest CPG companies is improving their ability to accurately forecast sales and drive efficiencies and the marketing spends to do so.

Session: A Predictive Approach to Marketing Mix Modeling

Russell Mandelik, Regional Sales Manager, NICE Systems

Russell Mandelik is a Regional Sales Manager at NICE Systems. He has been providing enterprise software solutions to Financial, Insurance, Healthcare, and Telecommunication organizations for over 10 years. During the last 2 years, Russell has been part of the NICE Interaction Business Applications business unit. The group is responsible for speech analytics based solutions including customer churn prediction, customer experience, sales and marketing effectiveness, and operational efficiency

Session: Leveraging Speech Analytics to Gain a Competitive Edge

Tim Manns, Senior Data Mining Analyst, Consumer Marketing, Optus

Tim Manns works as a data mining analyst for Optus SingTel. He manages Optus’s Data Mining Innovations’ team, and often spends his time analysing transactional call detail records (cdr) data which are stored in the Teradata data warehouse. He has conducted awesome analysis to tackle common marketing problems such as; customer retention (churn), acquistion, fraud, up­sell, cross­sell, product targeting, mobile rateplan features etc.

Session: Know Your Customers by Knowing Who They Know, and Who They Don’t

John McConnell, Director, Analytical People

John McConnell is the founder of Analytical People. He has been delivering Analytical Consulting Services in a broad range of business and research areas for over 20 years. The type of projects he is involved in range from ad­hoc analyses through to multi­user high­end, automated, analytical solutions delivery with Statistical, Data Mining and Predictive Analytics methods and technologies.

Through the ’90s he worked for SPSS in a variety of international Professional Services delivery and management roles. Since 2000 John has been involved in a number of ventures which have applied advanced analytical methodologies. In 2004 he co­founded Applied Insights which specialised in the application of Advanced Digital Analytics. Applied Insights was acquired by Foviance in November 2008 and Analytical People was launched.

John has a BSc in Mathematics, Statistics and Operational Research from the University of Manchester Institute of Science and Technology, UK

Session: Where Do We Go from Here ­ So the First Model Worked. What About the Next 6?

Antonia de Medinaceli, Senior Business Analyst, Elder Research, Inc.

Antonia de Medinaceli has extensive experience in all aspects of the data mining process, and has solved challenges in many industries, including financial, crime analysis, and customer relationship management (CRM) industries. Her consulting experience is both domestic and international. Antonia is experienced with most of the leading statistical software packages. In addition to her consulting experience, she has taught data mining short courses with the Elder Research team. She has degrees in Computer Science and Systems Engineering from the University of Virginia.

Session: Keep Winning the Eternal Fraud Battles

Anne Milley, Senior Director of Technology Product Marketing, SAS

As Senior Director of SAS Technology Product Marketing, Anne Milley oversees the marketing of SAS(r) technologies. Her ties to SAS began with her thesis on bank failure prediction models and the term structure of interest rates, which she completed at The Federal Home Loan Bank of Dallas. She has also served as a senior business consultant at 7­Eleven Inc., where she performed sales analysis and designed and conducted tests to aid in strategic decision making.

Milley has authored various papers, articles and an award­winning report for the 1999 KDD Contest. She co­chaired the SAS data mining technology conferences, M2001 and M2002, as well as SAS’ inaugural forecasting conference, F2006. She has served on Web mining committees for KDD and the Society for Industrial and Applied Mathematics, and on the Scientific Advisory Committee for Data Mining 2002. In 2008, she completed a five­month working sabbatical at a major financial services company in the United Kingdom.

Session: Strength in Numbers: ACE!

Anish Nanavaty, CEO, Research & Analytics, WNS Global Services 

Anish Nanavaty is CEO, Research & Analytics at WNS Global Services, Inc (NYSE: WNS). The Research & Analytics business unit that Anish leads provides specialized services for the Consumer Packaged Goods, Pharmaceutical, Retail, and Financial Services and Professional Services industries. With approximately 1,500 staff, the division provides some of the world’s largest companies with complex analytical solutions in functional areas such as marketing, operations, supply chain and risk.

As one of WNS’ founding executives, Anish previously led sales and business development
efforts for the company in North America. As Executive Vice President, Anish had particular
responsibility for building the Travel Services practice from its infancy. In this role, he was

active in sales, strategy setting, business development, marketing, and alliances.
Anish has a Bachelors Degree in Science and Economics from Wharton Business School.
Session: A Predictive Approach to Marketing Mix Modeling

Istvan Pilaszy, Prize Team, Gravity R&D

Istvan Pilaszy is a co­founder of the Netflix Prize team, Gravity, which lead the Netflix Prize competition from Jan 2007 to Apr 2007, and participated very successfully during the lifetime of the competition. Gravity served as a core component of team “The Ensemble,” which came in an extremely close second place at the conclusion of this ground­breaking competition by tying, according to Netflix, with the winning team, who had submitted their entry just 20 minutes earlier. The team members founded a startup, Gravity R&D, which specializes in high quality recommendation systems. The company won the Strands $100k Call last year, and is also a winner of the Red Herring 100 Europe award.

Istvan is working on his PhD thesis about recommendation algorithms, which is expected to be finished at the end of this year.

Session: Lessons That We Learned from the Netflix Prize

Andrew Pole, Senior Manager, Media and Database Marketing, Target 

Andrew Pole’s work experience includes Marketing research and CRM analytics as a Lead Consumer Analyst at Hallmark Cards, Analytics manager at Hallmark Loyalty Marketing Group consulting on CRM activities for clients ranging from Nestle­Purina, Cendant Mortgage, and Schwans Foods, and, currently, Sr. Manager at Target, managing a team of 40 people in the US and India supporting the analytics, campaign design/execution, and operational/performance reporting for Target stores and Target.com marketing campaigns. Mr. Pole’s degrees include BA Math and Music (Augustana College), MS Statistics (Kansas State University), MA Economics (University of Missouri­Kansas City).

Session: Challenges of Incremental Sales Modeling in Direct Marketing

Jules Polonetsky, Co­Chair and Director, Future of Privacy Forum

Jules serves as Co­chair and Director of the Future of Privacy Forum. As AOL’s former Chief Privacy Officer and SVP for Consumer Advocacy, Jules was responsible for ensuring that AOL’s users could trust the company with their information and for educating employees about best practices for advertising, content, and product development. Jules also served for four years as Vice President, Integrity Assurance, at America Online Inc.

From 2000 to 2002, Jules was Chief Privacy Officer and Special Counsel at DoubleClick, the advertising and marketing technology company that at the time was the largest internet company in New York City. From 1998 until 2000, Jules served as the NYC Consumer Affairs Commissioner for Mayor Rudolph Giuliani. Jules served as an elected member of the New York State Assembly from 1994 to 1997. From November 1992 through 1993, Jules was a legislative aide to Congressman Charles Schumer and was a District Representative for Congressman Steve Solarz from 1990 to 1992.

A graduate of New York University School of Law and Yeshiva University, Jules has served on the boards of a number of privacy and consumer protection organizations. In 2001, Crain’s NY Business magazine named Jules one of the top technology leaders in New York City.

Jules is a regular speaker at privacy and marketing industry events and has testified or presented as an industry expert before Congressional committees and the Federal Trade Commission.

Panel: Predictive Analytics and Consumer Privacy

Rex Pruitt, Senior Level Business Analyst, PREMIER Bankcard, LLC

Rex Pruitt is currently a Senior Level Business Analyst, who works for PREMIER Bankcard, LLC. He is responsible for portfolio data mining and predictive modeling, among other project leadership roles. Rex has a degree in Marketing Management and his career has been focused on the study of corporate data and how it can be turned into a revenue source. He has been in the Analytics profession since 1986 supporting the Financial Services and Insurance industries. Rex has traveled the world performing accounting process audits and recommending business process improvements using his analytic talents.

Session: The Development of a “Good Customer Score” for Use in Customer Acquisition, Rewards, Retention and Recovery

Karl Rexer, PhD, President, Rexer Analytics

Karl Rexer is President of Rexer Analytics, a small Boston­based analytic consulting firm. Karl has broad experience in analytic consulting that includes analytic strategy & leadership, predictive modeling, statistics, data mining, direct marketing, CRM, and market research. Before founding Rexer Analytics in 2002, Karl held leadership and consulting positions at several consulting firms and two multi­national banks. Karl and his teams have delivered analytic solutions to dozens of companies, including fraud detection, customer attrition analysis and prediction, customer segmentation, sales forecasting, direct mail targeting, market basket analysis and survey research. Karl is a leader in the field of applied data mining. He is a frequent invited speaker at MBA programs and conferences, and he has been on the review and organizing committees of several international data mining conferences, including KDD­2006, 2007, 2008, & 2010.

Karl serves on the Board of Directors of Oracle’s Business Intelligence, Warehousing, and Analytics (BIWA) Special Interest Group, and is on SPSS’s customer advisory board. Each year Rexer Analytics conducts and publishes the widely read Annual Data Miner Survey. In the Spring of 2009 over 700 data miners from around the globe participated in the third annual survey. Karl holds a BA from The Ohio State University and a PhD in Experimental Psychology from the University of Connecticut.

Moderator

Chris Scandlen, Senior Manager, Analytics, Laureate Education

Chris has been with Laureate Education for one and a half years where he manages the Marketing Analytics team. The Marketing Analytics team was instrumental in deploying the lead scoring solution for Laureate Education’s flagship online university. Prior to Laureate Education he was a member of the Economics and Statistics Group at PricewaterhouseCoopers, where he employed rigorous empirical analyses to address business challenges for organizations in the health care, financial services and Consumer Packaged Goods industries.

Before joining PricewaterhouseCoopers, Chris worked for Time Warner in the AOL Member Services group leading quantitative modeling efforts to support operational and strategic execution in AOL’s call centers. He has a BA in Mathematics and a BA in Economics from Macalester College and an MSc in Operations Research from George Mason University. Chris was also a PhD candidate in Economics at the University of Maryland specializing in Game Theory.

Session: The Use of Lead Scoring Solutions in the For­Profit Education Industry

Eric Siegel, Ph.D., Conference Chair

The president of Prediction Impact, Inc., Eric Siegel is an expert in predictive analytics and data mining and a former computer science professor at Columbia University, where he won awards for teaching, including graduate ­level courses in machine learning and intelligent systems ­ the academic terms for predictive analytics. After Columbia, Dr. Siegel co­founded two software companies for customer profiling and data mining, and then started Prediction Impact in 2003, providing predictive analytics services and training to mid­tier through Fortune 100 companies.

Dr. Siegel is the instructor of the acclaimed training program, Predictive Analytics for Business, Marketing and Web, and the online version, Predictive Analytics Applied. He has published 13 papers in data mining research and computer science education, has served on 10 conference program committees, and has chaired a AAAI Symposium held at MIT.

Session: Five Ways to Lower Costs with Predictive Analytics 

Stamatis Stefanakos, Senior Consultant, D1 Solutions AG

Stamatis Stefanakos received a Ph.D. in Computer Science from the Swiss Federal Institute of Technology in Zurich (ETH) in 2004. In 2005 he was a researcher at the University of Rome “La Sapienza” and the University of Padova in Italy. Afterwards, he worked as an analyst in the telecommunications industry in Zurich before joining D1 Solutions AG. He is a senior consultant with experience in business intelligence and data mining projects in telecommunications, banking, and retail.

Session: Cost Reduction in Bill­Insert Campaigns With Predictive Analytics

James Taylor, CEO, Decision Management Solutions

Taylor was previously a Vice President at Fair Isaac Corporation where he developed and refined the concept of enterprise decision management or EDM. The best known proponent of the approach, Taylor is a passionate advocate of decision management. He has 20 years experience in all aspects of the design, development, marketing and use of advanced technology including CASE tools, project planning and methodology tools as well as platform development in PeopleSoft’s R&D team and consulting with Ernst and Young. He develops approaches, tools and platforms that others can use to build more effective information systems. He is an experienced speaker and author, with his columns and articles appearing regularly in industry magazines.

Workshop: Putting Predictive Analytics to Work
Session: Putting Predictive Analytics to Work

Steve VanDee, PMP, AVP of Underwriting Transformation, Zurich Programs and Direct Markets

Steve VanDee is an Assistant Vice President of Zurich North America Commercial’s Programs and Direct Markets business unit. Mr. VanDee is dedicated to the elements of organizational transformation and change management in the implementation of predictive analytics solutions within our business unit.
Mr. VanDee has 10 years of insurance industry experience. Before joining Zurich, he was a Senior Project Manager with Sprint. Mr. VanDee holds a Bachelor of Arts degree in psychology from Conception College and a degree in philosophy from the Gregorian University in Rome. He is a designated Project Management Professional and is currently pursuing a Master of Business Administration from Georgetown University’s McDonough School of Business.

Session: Top 10 Ways to be Successful in Implementing Predictive Modeling in Insurance Commercial Markets

Jay Zhou, President, Business Data Miners, LLC

Dr. Jay Zhou is President of Business Data Miners, LLC. He has over 11 years of experience applying data mining technologies in solving business problems, including credit risk modeling, fraud detection in financial and telecommunication industries, life time value modeling, and direct mailing. Tens of millions of consumers have benefited from the predictive models that he has built. Dr. Zhou has developed many innovative data mining approaches. He co­authored the award wining paper, “Using genetic learning artificial neural networks for spatial decision making in GIS” (Nov., 1996, PE & RS).

He obtained a MS in Beijing University, and a PhD in plant science from University of Connecticut. Dr. Zhou can be reached at jzhou@businessdataminers.com and followed on twitter.

Session: Building In­Database Predictive Scoring Model: Check Fraud Detection Case Study

Agenda

Monday October 19, 2009

Full-day Workshop
Room: Poplar
Putting Predictive Analytics to Work

• Workshop starts at 9:00am
• Morning Coffee Break at 10:30am – 11:00am
• Lunch provided at 12:30 – 1:15pm
• Afternoon Coffee Break at 2:30pm – 3:00pm
• End of the Workshop: 4:30pm

Speaker: James Taylor, CEO, Decision Management Solutions

Full-day Workshop
Room: Walnut B
Hands-On Predictive Analytics

• Workshop starts at 9:00am
• Morning Coffee Break at 10:30am – 11:00am
• Lunch provided at 12:30 – 1:15pm
• Afternoon Coffee Break at 2:30pm – 3:00pm
• End of the Workshop: 4:30pm

Speaker: Dean Abbott, President, Abbott Analytics

Tuesday October 20, 2009

8:00am-9:00am
Registration & Continental Breakfast

9:00am-9:50am
Keynote 
Room: Magnolia
Five Ways to Lower Costs with Predictive Analytics

Question: How does predictive analytics actively deliver increased returns? Answer: By driving operational decisions with predictive scores – one score assigned to each customer. In this way, an enterprise optimizes on what customers WILL do.

But, in tough times, our attention turns away from increasing returns, and towards decreasing costs. On top of boosting us up the hill, can predictive analytics pull us out of a hole? Heck, yes. Marketing more optimally means you can market less. Filtering high risk prospects means you will spend less. And, by retaining customers more efficiently, well, a customer saved is a customer earned – and one you need not acquire.

In this keynote, Eric will demonstrate five ways predictive analytics can lower costs without decreasing business, thus transforming your enterprise into a Lean, Mean Analytical Machine. You’ll want to run back home and break the news: We can’t afford not to do this.

Speaker: Eric Siegel, Ph.D., Conference Chair

9:50am-10:10am
Platinum Sponsor Presentation
Room: Magnolia
The Perfect Storm: The Rise of Predictive Analytics

In today’s world, a unique combination of trends and factors is driving the uptake of predictive analytics in organizations across all sectors. The explosion of data volumes and the availability of advanced analytical technology coincide with an unprecedented focus on generating return on investments (ROI) in business systems and processes.

This presentation will address issues such as:

• How mathematics is becoming a trusted and popular science
• How the amount of data available is exponential exploding
• How the new sources of data are contributing to that explosion
• How C-level executives are pushing predictive analytics to the forefront
• How predictive analytics techniques are redefining the role of IT
• How organizations justify their investments in predictive analytics

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Erick Brethenoux, VP Corporate Development, SPSS Inc.

10:10am-10:30am
Morning Coffee Break
Upper Foyer / Exhibit Hall

Exhibit Hall Open from 10:10am to 7:30pm

Delegates may choose to attend either session at this time.

10:30am-11:20am
Track 1: Thought Leader 
Room: Magnolia
Case Study: Infinity Insurance & PREMIER Bankcard
Putting Predictive Analytics to Work

Seeking out increasingly small margins has brought analytics into vogue. Organizations realize that they can gain analytic insights about customers, products, channels, partners and much more. But some companies are already finding that analytics is only a part of the process – the intelligent application of the findings of these new insights can only pay off if the decisions that are made are correct.

Translating analytics into better operational outcomes requires a new conceptual framework – decision management. By applying this framework and becoming more decision-centric, by using business rules to control those decisions and by leveraging predictive analytics, companies are truly putting predictive analytics to work.

You will learn:

• How better operational decisions deliver ROI
• What the challenges are in applying predictive analytics to these decisions
• What Decision Management is and how it addresses these challenges
• How companies have benefited from this approach

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: James Taylor, CEO, Decision Management Solutions

10:30am-11:20am
Track 2: Non-profit 
Room: Walnut A&B
Case Study: National Rifle Association
How to Improve Customer Acquisition Models with Ensembles

Model ensembles have been used increasingly by data mining practitioners to increase model accuracy and routinely appear as top performers in predictive modeling competitions. However, they also provide significant robustness advantages over single models, particularly when data is noisy and has significant levels of uncertainty.

Insightful Miner was used to create an ensemble of models to improve the ROI of a direct marketing campaign to members of the National Rifle Association. This session will highlight a systematic methodology to use in building bootstrapped ensembles of logistic regression models, similar to the Bagging approach first advocated by Leo Brieman. The model ensembles are more reliable and out perform single models by exploring the results of applying both modeling techniques to the NRA membership file.

Moderator: Karl Rexer, Rexer Analytics

Speaker: Dean Abbott, President, Abbott Analytics

11:20am-12:30pm
Multiple Case Studies: Anheuser-Busch, Disney, HSBC, Pfizer, and others 
Room: Magnolia
The High ROI of Data Mining for Innovative Organizations

Data mining and advanced analytics can enhance your bottom line in three basic ways, by 1) streamlining a process, 2) eliminating the bad, or 3) highlighting the good. In rare situations, a fourth way – creating something new – is possible. But modern organizations are so effective at their core tasks that data mining usually results in an iterative, rather than transformative, improvement. Still, the impact can be dramatic.

Dr. Elder will share the story (problem, solution, and effect) of nine projects conducted over the last decade for some of America’s most innovative agencies and corporations:

Streamline:

• Cross-selling for HSBC
• Image recognition for Anheuser-Busch
• Biometric identification for Lumidigm (for Disney)
• Optimal decisioning for a leading high-tech retailer
• Quick decisions for the Social Security Administration

Eliminate Bad:

• Tax fraud detection for the IRS
• Warranty Fraud detection for a leading high-tech retailer

Highlight Good:

• Sector trading for WestWind Foundation
• Drug efficacy discovery for Pharmacia & UpJohn (now Pfizer)

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: John Elder, Elder Research, Inc.

12:30pm-1:45pm
Birds of a Feather Lunch
Room: Pan Am Foyer

Find your clan of like-minded colleagues with whom to dine and discuss, comparing your organization’s stories and challenges.

Discussion topics:

• Response modeling
• Churn modeling
• Product recommendations

SAS Lunch Topics:

• Response modeling
• Churn modeling
• Interactive data visualization best practices

1:45pm-2:35pm
Keynote 
Room: Magnolia
Predictive Analytics over On-line and Social Network Data

The rise of the interactive media represented by web, social media, search and behavioral targeting have created new challenges and opportunities for predictive analytics. While these new media offer a better chance for approaching the holy grail in marketing and advertising – understanding the customer’s intent and utilizing this intent to produce relevant offers and advertising – the rich structure of this data, from social graph data to time-series from interactions to reputation and other behavioral traits, from video streams to unstructured text, expand the complexity of prediction in dimensions where we have little experience and a poor understanding of the terrain. We present examples of such applications as well as challenges, and relate some case-studies to illustrate the power of understanding and harnessing this data. However, the context will also be used to illustrate the stronger need to build up new sciences to help us better understand these new powerful dimensions and new areas of their application. Predictive analytics plays a key role in addressing many of these challenges.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Usama Fayyad, Ph.D., Open Insights – Former Chief Data Officer, Yahoo!

2:35pm-2:45pm
Leveraging Speech Analytics to Gain a Competitive Edge
Room: Magnolia

In this overview, learn how NICE has become the leading provider of Insight from Interactions solutions, powered by advanced analytics of unstructured multimedia content – from telephony, web, radio and video communications. NICE’s solutions address the needs of the enterprise market enabling organizations to operate in an insightful and proactive manner. Every conversation between your customers and staff speaks volumes about your company’s long-term success. With Interaction Business Analytics from NICE Systems, you can tune into the valuable data housed in every interaction to establish sound business strategies for delivering exemplary service and shaping future strategy.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Russell Mandelik, NICE Systems

2:45pm-2:55pm
Session Break
Upper Foyer / Exhibit Hall

Delegates may choose to attend either session at this time.

2:55pm-3:45pm
Track 1: Incremental Modeling (Uplift modeling) 
Room: Magnolia
Case Study: Target
Challenges of Incremental Sales Modeling in Direct Marketing

While Target has enjoyed profitable returns from their ongoing direct mail program, the Target analytics group was given the challenge to drive more guest (customer) incremental sales and profit in the future. The team determined that in order to do this, they needed to look beyond the common levers like guest segmentation, product category guest conversion modeling, and mail piece customization to tackle guest-level incremental sales modeling.

In this presentation, we will share our proposed methodologies, as well as results, based on using predictive models to identify guests who are likely to spend incrementally upon receiving a direct mail contact. Unfortunately, despite a year’s worth of effort and multiple engagements with statistical consulting firms, the team has been unable to produce consistent and dependable result to use in guest campaign selection. Given this, the Target team will share intermediate results and cover hypotheses as to why the solution has been so elusive to date.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Andrew Pole, Senior Manager, Media and Database Marketing, Target

2:55pm-3:45pm
Track 2: Telecommunications 
Room: Walnut A&B
Case Study: Sunrise Communications (Switzerland)
Cost Reduction in Bill-Insert Campaigns With Predictive Analytics

Sunrise Communications is the 2nd largest telecom provider in Switzerland. Its business intelligence team has successfully deployed churn modeling for several years. In this session, we describe a new data mining application within Sunrise’s CRM department.

Every month, CRM creates several print campaigns which are sent to the customers together with their invoices. To reduce costs, however, not every customer receives a monthly invoice. Whether the invoice is sent depends on a complex calculation based on the month’s revenues, any outstanding amounts, previous bills, etc.

The challenge arises from the fact that CRM has to calculate the volumes to be sent to the printer several weeks before the month’s revenues is known. Predicting the required print volumes has resulted in significant cost savings compared to the approach taken previously.

Moderator: Eric A. King, The Modeling Agency

Speaker: Stamatis Stefanakos, Senior Consultant, D1 Solutions AG

Delegates may choose to attend either session at this time.

3:45pm-4:30pm
Track 1: Incremental Modeling (Uplift modeling) 
Room: Magnolia
Case Study: US Bank
Raising the Bar in Cross-Sell Marketing With Uplift Modeling

Learn how US Bank is applying next generation Uplift analytic modeling to boost the ROI of their cross-sell marketing while simultaneously slashing program costs. As you will hear, Uplift modeling significantly exceeded the results achievable via traditional analytic approaches. Some of the many results observed include:

• Increase incremental cross-sell revenue by greater than 300%
• Reduce mailing costs by up to 40%
• Isolate and eliminate the negative effects of marketing
• Achieve a 5-fold increase in campaign ROI when compared with existing programs

In this session you will also receive practical tips from experts on how you can leverage this technique within your organization in order to optimize marketing performance by achieving greater business results from increasingly limited resources.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Michael Grundhoefer, Marketing Analytics, Market Info. & Research, US Bank

3:45pm-4:30pm
Track 2: Telecommunications 
Room: Walnut A&B
Case Study: Optus (Australian telecom)
Know Your Customers by Knowing Who They Know, and Who They Don’t

In the highly saturated and ever competitive mobile (cell-phone) industry, telecommunication companies continually seek new ways to analyse and understand customer behaviour. Whilst the priority of data mining analysis in the telecommunications industry is often to retain customers, being able to successfully target customers for appropriate upgrades (for example, new handsets or higher priced plans) and additional value added services will greatly enhance customer value and revenue profit margins. One recent development in Optus SingTel’s strategy for better customer insights is social network analysis

Tim Manns will present a case study of an In-Database Social Networking Analysis solution developed and applied to the Optus SingTel consumer mobile customer base (approx 5 million mobile customers). This deployed data mining solution runs monthly, analysing every communication event made and received by mobile customers. The presentation will illustrate some of the practical data mining challenges of processing and transforming several billion rows of data warehouse transactional level data (call detail records) into summarised customer data within a practical timeframe. Business benefits and predictive modelling outcomes will also be presented.

Moderator: Eric A. King, The Modeling Agency

Speaker: Tim Manns, Data Miner, Optus (part of the SingTel group)

4:30pm-4:55pm
Afternoon Coffee Break
Upper Foyer / Exhibit Hall

Delegates may choose to attend either session at this time.

4:55pm-5:40pm
Track 1: Financial Services 
Room: Magnolia
Case Study: PREMIER Bankcard
The Development of a “Good Customer Score” for Use in Customer Acquisition, Rewards, Retention and Recovery

“Good Customer Score (GCS)” is a measurement of current customer value. The methodology used for development of GCS incorporates internal operational customer data focused on key customer performance measures applied through mathematical weighting to generate a ratio representative of a “Good Customer”.

The GCS accuracy is supported by its statistical correlation to Behavior Score (3rd party score), as well as other scores, when predicting those customers who will perform in the top 25% of the portfolio ranked by GCS. The strength of like scores is measured using Chi-Square correlation results in addition to Decision Tree and Logistic Regression prediction models.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Rex Pruitt, Senior Level Business Analyst, PREMIER Bankcard, LLC

4:55pm-5:40pm
Track 2: Telecommunications 
Room: Walnut A&B
KDDcup 2009 Competition Results: Orange Labs (France Telecom)
Churn, Baby, Churn: Fast Scoring on Large Telecom Dataset

Churn prediction and management is critical for companies in the fast and competitive telecommunication market. In this session, we introduce the dataflow computational model in the context of data and computationally intensive high performance parallel data mining. We present a highly scalable and robust model capable of scoring “propensity-to-churn” at the rate of 50,000 customers in a 1.6GB test set (Orange Labs France Telecom, KDD Cup) in 3 minutes on commodity 16-core CPUs. This is an effective scoring runtime of 3.6 milliseconds per customer, orders of magnitude faster than some systems. As a competitor in this year’s KDDcup data mining competition, this speed enables more iterations towards improved performance; while the research focus was speed, resulting predictive accuracy ranked higher than 70% of competitors.

Moderator: Eric A. King, The Modeling Agency

Speaker: Srivatsava Daruru, Research Assistant, The University of Texas at Austin

Delegates may choose to attend either session at this time.

5:40pm-6:30pm
Track 1: Financial Services 
Room: Magnolia
Case Study: Citizens Bank
Building In-Database Predictive Scoring Model: Check Fraud Detection Case Study

It is estimated that the nation’s banks experience over $10 billion per year in attempted check fraud. The daily challenge for a large bank is to identify a few thousand risky checks deposited out of hundreds of thousands of normal ones. To address this challenge, we build a predictive model that gives each check a risk score. All of the processes including data preparation, feature variable calculation, model training, model testing and final model deployment are executed within a single database environment. The in-database solution provides substantially increased security, productivity, manageability and scalability.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Jay Zhou, President, Business Data Miners, LLC

5:40pm-6:30pm
Track 2: Text Analytics 
Room: Walnut A&B
Predictive Text Analytics

The session will introduce predictive analytics applied to textual material and present several mini-application case studies. Predictive text analytics consists of information retrieval, information extraction, clustering, classification, and other techniques applied to make sense of ‘unstructured’ sources. Solutions apply statistical, linguistic, and machine learning algorithms in conjunction with BI, data mining, and visualization tools.

The technology has found broad applications in business, government, and research, in domains that range from intelligence and the life sciences to social-media analysis. The case studies will highlight challenges, techniques, and benefits in the application of predictive text analytics in a sampling of the many business domains where solutions are applied.

Moderator: Eric A. King, The Modeling Agency

Speaker: Seth Grimes, Principal Consultant, Alta Plana Corporation

6:30pm-7:30pm
Reception
Upper Foyer / Exhibit Hall

Sponsored by  

7:30pm-10:00pm
useR Meeting
Room: Magnolia – Sponsored by  

Please join the group at www.meetup.com/R-users-DC/

R is an open source programming language for statistical computing, data analysis, and graphical visualization. R has an estimated one million users worldwide, and its user base is growing. While most commonly used within academia, in fields such as computational biology and applied statistics, it is gaining currency in commercial areas such as quantitative finance and business intelligence.

Among R’s strengths as a language are its powerful built-in tools for inferential statistics, its compact modeling syntax, its data visualization capabilities, and its ease of connectivity with persistent data stores (from databases to flatfiles).

In addition, R is open source nature and extensible via add-on “packages” allowing it to keep up with the leading edge in academic research.

For all its strengths, though, R has an admittedly steep learning curve; the first steps towards learning and using R can be challenging.

This DC R Users Group is dedicated to bringing together area practitioners of R to exchange knowledge, inspire new users, and spur the adoption of R for innovative research and commercial applications.

Wednesday October 21, 2009

8:00am-9:00am
Registration & Continental Breakfast

9:00am-9:50am
Keynote 
Room: Magnolia
Opportunities and Pitfalls:
What the World Does and Doesn’t Want from Predictive Analytics

Mathematicians and statisticians are churning through mountains of data in their efforts to model and predict human behavior. The goal is to optimize every function possible, from sales and marketing to the enterprise itself. These Numerati are guided by the two dominant models of the late 20th century, the modeling of financial markets and of industrial systems. How do humans fit into these systems? And what will their response be when the analytic systems appear to misunderstand them or invade their privacy?

Stephen Baker joins PAW to directly address the Numerati. In his keynote presentation, Mr. Baker will guide us toward the untapped goldmines where predictive analytics will be embraced and thrive, and teach us to anticipate and maneuver around two central pitfalls: Consumer misperception of us, and our inadvertent mistreatment of them.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Stephen Baker, BusinessWeek – author, The Numerati

9:50am-10:10am
Platinum Sponsor Presentation
Room: Magnolia
Strength in Numbers: ACE!

As more organizations are beginning their analytical journey or reinvigorating their existing efforts, Analytic Centers of Excellence (ACEs) are helping them along the way. The interest in ACEs is growing across industries as organizations seek better ways to tap into their analytic infrastructure-most importantly, scarce high-end analytic expertise to improve results. We will highlight valuable best practices for achieving greater analytic bandwidth realizing more and better evidence-based decisions.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Anne Milley, Senior Director of Tech. Product Marketing, SAS

10:10am-10:30am
Morning Coffee Break
Upper Foyer / Exhibit Hall

Exhibit Hall Open from 10:10am to 6:00pm

Delegates may choose to attend either session at this time.

10:30am-11:20am
Track 1: Verticals 
Room: Magnolia
Case Study: Reed Elsevier
Where Do We Go from Here – So the First Model Worked. What About the Next 6?

At the inaugural PAW in February 2009 John presented a first case study in subscriber retention modelling. The emphasis of this project was to improve retention rates and profitability for one of Reed Business Information’s magazines, Caterer & Hotelkeeper, and to do this in a way which enabled Ed Garcia of RBI to prove the business value of the resultant model to senior management.

Since then we’ve moved on from the initial success and modelled a number of other RBI publications including New Scientist and Flight International. Our new case study presents the results from the next level of modelling.

Moderator: Karl Rexer, Rexer Analytics

Speaker: John McConnell, Director, Analytical People

10:30am-11:20am
Track 2: Forecasting 
Room: Walnut A&B
Case Study: The Financial Times, The New York Times, Sprint-Nextel
Predicting Future Subscriber Levels

Subscription-based businesses of all types–software as a service firms, wireless carriers, satellite and cable TV firms–all have a need to predict future subscriber levels. This session presents a customer-centric approach to prediction using survival analysis. A subscriber population is an ever-changing mix of customer segments, each with its own hazard probability that is a function of tenure and additional covariates such as market, product type, and credit class. A forecast based on these hazard probabilities automatically reacts to changes in the customer mix, allowing one to simulate alternate scenarios based on different assumptions about future customer acquisitions.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Michael Berry, Founder and Principal, Data Miners, Inc.

Delegates may choose to attend either session at this time.

11:20am-12:10pm
Track 1: Verticals 
Room: Magnolia
Case Study: Amway
Establishing a Performance-Based Culture with Predictive Analytics

Too often organizations manage their business based on what they know from the past. In Amway’s case, they use Predictive Analytics as a necessary and strategic business weapon to provide the vision to proactively make decisions that improve the future. Amway will discuss how Predictive Analytics has helped transform their organization into a performance-based culture. In support of this worldwide initiative, Amway developed key metrics and processes across numerous program, such as measuring the lifetime value of its Distributors.

By implementing Predictive Analytics, Amway was able to successfully execute this critical metric quickly by building, validating and deploying models more efficiently that benefited its marketing campaigns, segmentation, strategic planning and, most importantly, improved Distributor retention rates. Amway’s management team now has clear insight into the results of this analysis on an executive dashboard so they can manage and measure investment decisions and track program success. Amway will explain the process, techniques and lessons learned from this initiative, including an overview of the metrics, roll-out stages, aligning with IT, communicating with management and successes.

Moderator: Karl Rexer, Rexer Analytics

Speaker: Mike Kinlaw, Manager/Lead Statistician, CMI Customer Analytics, Amway

11:20am-12:10pm
Track 2: Forecasting 
Room: Walnut A&B
Case Study: Coke
A Predictive Approach to Marketing Mix Modeling

In this session we will review approaches to Marketing Mix Modeling with forecasting as a primary focus. We will review trade-offs in data granularity to achieve the accuracy desired to estimate the impact of various promotional activities on sales and then forecast the impact of future sets of promotional tactics with and eye towards different departments within the organization that are affected. We will address certain limitations such as reading the value of longer-term equity building programs and regional or targeted media impact. And finally, we will review the System Effort required to get worthwhile results in terms of action-ability.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Ram Krishnamurthy, Group Director, Marketing Strategy & Insights, Coca-Cola

Speaker: Anish Nanavaty, CEO, Research & Analytics, WNS Global Services, Inc

12:10pm-1:50pm
Birds of a Feather Lunch
Room: Pan Am Foyer

Find your clan of like-minded colleagues with whom to dine and discuss, comparing your organization’s stories and challenges.

Discussion topics:

• Project management and organizational process
• Data preparation
• Core predictive modeling methods

SAS Lunch Topics:

• Project management and organizational process
• Data access and preparation
• Core predictive modeling methods

1:50pm-2:40pm
Expert Panel: Predictive Analytics and Consumer Privacy
Room: Magnolia

When analytics delivers value to an enterprise, it often means benefits for the end consumer as well. More precise targeting means less junk in the mail (not to mention fewer trees cut down). More effective fraud detection allows for more competitive pricing. Spot-on personal movie, music and book recommendations certainly can’t hurt. What could possibly go wrong?

Pure and simple, personalization means personal data must be stored. Moreover, with predictive models in place, this personal data becomes alive as it is specifically acted upon. Today’s consumer displays a growing concern with her “cloud identity”. What’s known – and what’s thereby inferred – about a consumer seems to, at times, cut to the core of her sense of identity.

Our panel of experts digs in to answer the central questions. What happens when the shoppers and workers of the world perceive predictive analytics as irresponsibly applied – or, worse yet, what if they’re right? What are the minimally required conditions to ensure the consumer does not feel manipulated?

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Stephen Baker, BusinessWeek – author, The Numerati

Speaker: Jules Polonetsky, Co-Chair and Director, Future of Privacy Forum

Speaker: Mikael Hagström, Executive Vice President, EMEA and Asia Pacific, SAS

2:40pm-2:45pm
Session Break
Upper Foyer / Exhibit Hall

Delegates may choose to attend either session at this time.

2:45pm-3:35pm
Track 1: Health Care 
Room: Magnolia
Case Study: Lifeline Screening
Segmented Modeling Applications in Health Care Industry

A “segmented” modeling methodology was developed in order to identify most likely responders for Lifeline Screening’s Direct Mail program. The models are developed based on segments of the population that respond differently depending on the frequency and cadence of the communications. “Segment-specific” models were developed over different consumer segments . This approach produced significant lift over the traditional response modeling approach that is based on a single model, as established with a head-to-head evaluation.

Moderator: Karl Rexer, Rexer Analytics

Speaker: Ozgur Dogan, Vice President, Merkle

2:45pm-3:35pm
Track 2: Fraud Detection 
Room: Walnut A&B
Keep Winning the Eternal Fraud Battles

Fraud is pervasive and extraordinarily costly, and the effort required to prevent it is non-trivial. But, enormous ROI is possible when predictive analytics insights are harnessed to detect ever-changing anomalous behavior. We will describe case studies to highlight lessons learned about what leads to a successful fraud detection project. We’ll also address the cultural and business hazards of attacking versus ignoring fraud, summarize collateral benefits of analyzing customer behavior, and describe new forms of fraud only discoverable via analyzing social networks and links between accounts.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Antonia de Medinaceli, Senior Business Analyst, Elder Research, Inc.

3:35pm-3:55pm
Afternoon Coffee Break
Upper Foyer / Exhibit Hall

Delegates may choose to attend either session at this time.

3:55pm-4:45pm
Track 1: Insurance 
Room: Magnolia
Case Study: Zurich
Top 10 Ways to be Successful in Implementing Predictive Modeling in Insurance Commercial Markets

Zurich recently chalked up our 27th straight quarter of profitability, and with the help of predictive modeling we are on track to maintain that record. This presentation is an examination of our lessons learned as part of our industry leading implementation of predictive modeling within commercial lines. We will be relating some poignant change management opportunities and other issues particularly significant for commercial lines insurance companies and related service providers to be aware of as they begin their own forays into applying predictive analytics within their organizations.

Moderator: Karl Rexer, Rexer Analytics

Speaker: Joel Appelbaum, Chief Analytics Officer, Zurich

Speaker: Steve VanDee, PMP, AVP of Underwriting Transformation, Zurich

3:55pm-4:45pm
Track 2: Product Recommendations 
Room: Walnut A&B
Lessons That We Learned from the Netflix Prize

Netflix announced in Oct 2006 the 1 million dollar Netflix Prize competition to create a better recommender system, which just ended this summer. During the 3 years of the competition, many new approaches have been developed, and state of the art in the field of recommendation algorithms has been completely changed. In this session, we survey the most interesting algorithms of these 3 years, giving an insight into how new methods were developed by successively changing already existing ones.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Istvan Pilaszy, Prize Team, Gravity R&D

Delegates may choose to attend either session at this time.

4:45pm-5:30pm
Track 1: Insurance 
Room: Magnolia
Case Study: Aflac
Establishing a Customer Retention Analytics Framework

Now more than ever, customer retention is a key performance indicator for many organizations. Unfortunately, assessing customer retention in any industry can prove to be quite a challenge. In this session we will review key steps for establishing and executing a customer retention analytics solution. A customer retention analytics solution should seek to define key metrics, gauge an organization’s retention performance, identify/quantify sources of impact, and use predictive modeling to forecast future performance. Our modeling approach includes rigorous data-mining, regression analysis, and incorporation of additional assumptions for scenario modeling; each of which will be addressed. In addition, we will share our challenges and lessons learned along the way.

Moderator: Karl Rexer, Rexer Analytics

Speaker: Heather Avery, Manager, Business Analytics, Aflac

4:45pm-5:30pm
Track 2: Education 
Room: Walnut A&B
Case Study: Walden University, Kendall College, University of Liverpool
The Use of Lead Scoring Solutions in the For-Profit Education Industry

The implementation of a lead scoring solution has the potential to render both Sales and Marketing organizations a competitive advantage. The case study presented will examine the use of lead scoring to (1) structure the on-line media buying process and (2) potential to drive contact management strategy with prospective students.

Moderator: Eric Siegel, Program Chair, Predictive Analytics World

Speaker: Christopher Scandlen, Laureate Higher Education Group

Speaker: Sherry Bennett-Flatt, Business Intel., Laureate Higher Edu. Group

Thursday October 22, 2009

Full-day Workshop
Room: Hickory
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes

• Workshop starts at 9:00am
• First AM Break from 10:00 – 10:15
• Second AM Break from 11:15 – 11:30
• Lunch from 12:30 – 1:15pm
• First PM Break: 2:00 – 2:15
• Second PM Break: 3:15 – 3:30
• Workshops ends at 4:30

Speaker: John F. Elder, Ph.D., CEO and Founder, Elder Research, Inc.

Introduction PDF

Agenda PDF / Online

Speakers PDF