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Full Agenda – Washington, DC – October 19-20, 2010
Track 1 sessions are for all levels
Track 2 sessions are expert/practitioner level.

Monday October 18, 2010

Full-day Workshop
Room: Walnut
Driving Enterprise Decisions with Business Analytics and Business Rules
Click here for the detailed workshop description

  • Registration and breakfast starts at 8:00am
  • 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: Hickory
Hands-On Predictive Analytics
Click here for the detailed workshop description

  • Registration and breakfast starts at 8:00am
  • 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 19, 2010

Room: Pan Am Foyer

Registration & Continental Breakfast


Room: Magnolia
Five Ways Predictive Analytics Cuts Enterprise Risk

All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.

Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a "loss customer" such as a bad debtor or an insurance policy-holder with high claims.

In this keynote session, Dr. Eric Siegel will reveal:

  • Five ways predictive analytics evolves your enterprise to reduce risk
  • Hidden sources of risk across operational functions
  • What every business should learn from insurance companies
  • How advancements have reversed the very meaning of fraud
  • Why "man + machine" teams are greater than the sum of their parts for
  • enterprise decision support

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

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Room: Magnolia

Platinum Sponsor Presentation
Analytics: The Beauty of Diversity

Analytics contributes to, and draws from, multiple disciplines. The unifying theme of "making the world a better place" is bred from diversity. For instance, the same methods used in econometrics might be used in market research, psychometrics and other disciplines. In a similar way, diverse paradigms are needed to best solve problems, reveal opportunities and make better decisions. This is why we evolve capabilities to formulate and solve a wide range of problems through multiple integrated languages and interfaces. Extending that, we have provided integration with other languages so that users can draw on the disciplines and paradigms needed to best practice their craft.

Speaker: Anne H. Milley, Senior Director of Analytic Strategy, Worldwide Product
Marketing, SAS

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Room: Magnolia

Gold Sponsor Presentation
Predictive Analytics Accelerate Insight for Financial Services

Financial services organizations face immense hurdles in maintaining profitability and building competitive advantage. Financial services organizations must perform "what-if" scenario analysis, identify risks, and detect fraud patterns. The advanced analytic complexity required often makes such analysis slow and painful, if not impossible.

This presentation outlines the analytic challenges facing these organizations and provides a clear path to providing the accelerated insight needed to perform in today's complex business environment to reduce risk, stop fraud and increase profits.

  • The value of predictive analytics in Accelerating Insight

  • Financial Services Analytic Case Studies

  • Brief Overview of ParAccel Analytic Database

Speaker: Finbarr Deely, Director of Business Development, ParAccel

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Room: Pan Am Foyer

Break / Exhibits

Room: Magnolia
Track 1: Business Value
Case Study:
Creating Global Competitive Power with Predictive Analytics

Using Predictive analytics to gain a deeper understanding of customer behaviours, increase marketing ROI and drive growth

  • Creating global competitive power with business intelligence: Making the right decisions - at the right time
  • Avoiding common change management challenges in sales, marketing, customer service, and products
  • Developing a BI vision - and implementing it: successful business intelligence implementation models
  • Using predictive analytics as a business driver to stay on top of the competition
  • Following the Monster Worldwide global BI evolution: How Monster used BI to go from good to great

Speaker: Jean Paul Isson, Monster Worldwide

Room: Walnut
Track 2: Survey Analysis
Case Study: YMCA
Turning Member Satisfaction Surveys into an Actionable Narrative

Survey analysis often involves hand-tuned analysis requiring weeks of labor to decipher the key relationships in survey responses. Proper coding of responses, colinearity, and missing data plague analysts in their pursuit of clear explanations of responder intent in the surveys. Additionally, while traditional statistical analyses, such as linear and logistic regression, can be used effectively in modeling survey responses, these models do not resonate with the business community in the same way they do with statisticians.

Employees are a key constituency at the Y and previous analysis has shown that their attitudes have a direct bearing on Member Satisfaction. This session will describe a successful approach for the analysis of YMCA employee surveys. Decision trees are built and examined in depth to identify key questions in describing key employee satisfaction metrics, including several interesting groupings of employee attitudes. Our approach will be contrasted with other factor analysis and regression-based approaches to survey analysis that we used initially. The predictive models described are currently in use and resulted in both greater understanding of employee attitudes, and a revised "short-form" survey with fewer key questions identified by the decision trees as the most important predictors.

Speakers: Dean Abbott, President, Abbott Analytics & Bill Lazarus, Seer Analytics, LLC

Also see Mr. Abbott's full-day workshop

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Room: Magnolia
Industry Trends
2010 Data Miner Survey Results: Highlights

Do you want to know the views, actions, and opinions of the data mining community? Each year, Rexer Analytics conducts a global survey of data miners to find out. This year at PAW we unveil the results of our 4th Annual Data Miner Survey. This session will present the research highlights, such as:

  • Analytic goals & key challenges
  • Impact of the economy
  • Regional differences
  • Text mining trends

The full Summary Report will then be immediately available online to all PAW attendees.

Speaker: Karl Rexer, Ph.D., Rexer Analytics

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Room: Magnolia
Special Plenary Session
Multiple Case Studies: U.S. DoD, U.S. DHS, SSA
Text Mining: Lessons Learned

Text Mining is the "Wild West" of data mining and predictive analytics - the potential for gain is huge, the capability claims are often tall tales, and the "land rush" for leadership is very much a race.

In solving unstructured (text) analysis challenges, we found that principles from inductive modeling - learning relationships from labeled cases - has great power to enhance text mining. Dr. Elder will highlight key technical breakthroughs discovered while working on projects for leading government agencies, including:

  • Prioritizing searches for the Dept. of Homeland Security
  • Quick decisions for Social Security Admin. disability
  • Document discovery for the Dept. of Defense
  • Disease discovery for the Dept. of Homeland Security
  • Risk profiling for the Dept. of Defense

Dr. Elder will summarize, from these (and commercial) deployment experiences, the factors essential to a successful text mining project.

Speaker: John Elder, Ph.D., Elder Research, Inc.

Also see Dr. Elder's full-day workshop

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Room: Pan Am Foyer

Birds of a Feather Lunch / Exhibits

Watch this session online
Room: Magnolia
How Target Gets the Most out of Its Guest Data to Improve
Marketing ROI

Target Corp. is the fifth-largest retailer in America, providing a convenient one-stop shopping destination for guests seeking food, commodities, home furnishings, electronics, sporting goods, toys, apparel and more, both in-store and online. During these challenging economic times, Target is focused on delivering compelling reasons for guests to shop with us - and we continue to stand by our commitment to offering exceptional value, a broad assortment of outstanding merchandise and a superior store experience to each and every guest. To that end, Target is constantly examining how we can leverage innovative technology, analytics, and data to optimize our business model.

In this session, you'll learn how Target leverages its own internal guest data to optimize its direct marketing - with the ultimate goal of enhancing our guests' shopping experience and driving in-store and online performance. You will hear about what guest data is available at Target, how and where we collect it, and how it is used to improve the performance and relevance of direct marketing vehicles. Furthermore, we will discuss Target's development and usage of guest segmentation, response modeling, and optimization as means to suppress poor performers from mailings, determine relevant product categories and services for online targeted content, and optimally assign receipt marketing offers to our guests when offer quantities are limited.

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

Explore upcoming Predictive Analytics World conferences

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Room: Magnolia

Platinum Sponsor Presentation
Driving Analytics Into Decision Making

Organizations looking to dramatically improve their business outcomes are turning to decision management, a convergence of technology and business processes that is used to streamline and predict the outcome of daily decision-making. IBM SPSS Decision Management technology provides the critical link between analytical insight and recommended actions. In this session you'll learn how Decision Management software integrates analytics with business rules and business applications for front-line systems such as call center applications, insurance claim processing, and websites. See how you can improve every customer interaction, minimize operational risk, reduce fraud and optimize results.

Speaker: Jason Verlen, Director, SPSS Product Strategy & Management, IBM Software Group

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Room: Pan Am Foyer

Session Break


Room: Magnolia
Track 1: Data Infrastructure and Integration
Case Study: Macy's
The world is not flat (even though modeling software has to think it is).

Software for statistical modeling generally use flat files, where each record represents a unique case with all its variables. In contrast most large databases are relational, where data are distributed among various normalized tables for efficient storage. Variable creation and model scoring engines are necessary to bridge data mining and storage needs. Development datasets taken from a sampled history require snapshot management. Scoring datasets are taken from the present timeframe and the entire available universe. Organizations, with significant data, must decide when to store or calculate necessary data and understand the consequences for their modeling program.

Speaker: Paul Coleman, Director of Marketing Statistics, Macy's Inc.

Room: Walnut
Track 2: Customer Value
Case Study: SunTrust
When One Model Will Not Solve the Problem - Using Multiple
Models to Create One Solution

In 2007, SunTrust Bank developed a series of models to identify clients likely to have large changes in deposit balances. The models include three basic binary and two linear regression models.

Based on the models, 15% of SunTrust clients were targeted as those most likely to have large balance changes. These clients accounted for 65% of the absolute balance change and 60% of the large balance change clients. The targeted clients are grouped into a portfolio and assigned to individual SunTrust Retail Branch. Since 2008, the portfolio generated a 2.6% increase in balances over control.

Using the SunTrust example, this presentation will focus on:

  • Identifying situations requiring multiple models
  • Determining what types of models are needed
  • Combining the individual component models into one output

Speaker: Dudley Gwaltney, Group Vice President, Analytical Modeling, SunTrust Bank

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Room: Magnolia

Track 1: Response & Cross-Sell
Case Study: Paychex
Staying One Step Ahead of the Competition - Development of
a Predictive 401(k) Marketing and Sales Campaign

In-depth case study of Paychex, Inc. utilizing predictive modeling to turn the tides on competitive pressures within their own client base. Paychex, a leading provider of payroll and human resource solutions, will guide you through the development of a Predictive 401(k) Marketing and Sales model. Through the use of sophisticated data mining techniques and regression analysis the model derives the probability a client will add retirement services products with Paychex or with a competitor. Session will include roadblocks that could have ended development and ROI analysis.

Speaker: Frank Fiorille, Director of Enterprise Risk Management, Paychex

Speaker: Jason Fox, Risk Management Analyst, Paychex

Room: Walnut
Track 2: Segmentation
Practitioner: Canadian Imperial Bank of Commerce
Segmentation Do's and Don'ts

The concept of Segmentation is well accepted in business and has withstood the test of time. Even with the advent of new artificial intelligence and machine learning methods, this old war horse still has its place and is alive and well. Like all analytical methods, when used correctly it can lead to enhanced market positioning and competitive advantage, while improper application can have severe negative consequences.

This session will explore what are the elements of success, and what are the worse practices that lead to failure. The relationship between segmentation and predictive modeling will also be discussed to clarify when it is appropriate to use one versus the other, and how to use them together synergistically.

Speaker: Daymond Ling, Senior Director, Modelling & Analytics, Canadian Imperial Bank of Commerce

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Room: Pan Am Foyer

Break / Exhibits


Room: Magnolia
Track 1: Social Data
Thought Leadership
Social Network Analysis: Killer Application for Cloud Analytics

Social networks such as Twitter and Facebook are a potential goldmine of insights on what is truly going through customers´minds. Every company wants to know whether, how, how often, and by whom they´re being mentioned across the billowing new cloud of social media. Just as important, every company wants to influence those discussions in their favor, target new business, and harvest maximum revenue potential. In this session, Forrester analyst James Kobielus will identify fruitful applications of social network analysis in customer service, sales, marketing, and brand management. He will present a roadmap for enterprises to leverage their inline analytics initiatives and leverage high-performance data warehousing (DW) clouds and appliances in order to analyze shifting patterns of customer sentiment, influence, and propensity. Leveraging Forrester's ongoing research in advanced analytics and customer relationship management, Kobielus will discuss industry trends, commercial modeling tools, and emerging best practices in social network analysis, which represents a game-changing new discipline in predictive analytics.

Speaker: James Kobielus, Senior Analyst, Forrester Research

Room: Walnut
Track 2: Healthcare - International Targeting
Case Study: Life Line Screening
Taking CRM Global Through Predictive Analytics

While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK

Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc

Speaker: Trish Mathe, Director of Database Marketing, Life Line Screening

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Room: Magnolia
Track 1: Survey Analysis
Case Study: Forrester
Making Survey Insights Addressable and Scalable - The Case Study
of Forrester's Technographics® Benchmark Survey

Marketers use surveys to create enterprise wide applicable strategic insights to: (1) develop segmentation schemes, (2) summarize consumer behaviors and attitudes for the whole US population, and (3) use multiple surveys to draw unified views about their target audience. However, these insights are not directly addressable and scalable to the whole consumer universe which is very important when applying the power of survey intelligence to the one to one consumer marketing problems marketers routinely face. Acxiom partnered with Forrester Research, creating addressable and scalable applications of Forrester's Technographics Survey and applied it successfully to a number of industries and applications.

Speaker: Roxana Strohmenger, Senior Survey Manager, Operations and Analytics, Forrester Research

Speaker: Nethra Sambamoorthi, Ph.D | Team Leader, Consumer Dynamics & Analytics, Global Consulting, Acxiom Corporation

Room: Walnut
Track 2: Healthcare
Case Study: UPMC Health Plan
A Predictive Model for Hospital Readmissions

Hospital readmissions are a significant component of our nation's healthcare costs. Predicting who is likely to be readmitted is a challenging problem. Using a set of 123,951 hospital discharges spanning nearly three years, we developed a model that predicts an individual's 30-day readmission should they incur a hospital admission. The model uses an ensemble of boosted decision trees and prior medical claims and captures 64% of all 30-day readmits with a true positive rate of over 27%. Moreover, many of the 'false' positives are simply delayed true positives. 53% of the predicted 30-day readmissions are readmitted within 180 days.

Speaker: Scott Zasadil, Senior Scientist, UPMC Health Plan

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Room: Pan Am Foyer

Reception / Exhibits

Room: Walnut

DC useR Group Meeting

One of the most innovative ideas in data visualization in recent years is that graphical images can be described using a grammar. Just as a fluent speaker of a language can talk more precisely and clearly than someone using a tourist phrasebook, graphics based on a grammar can yield more insights than graphics based on a limited set of templates (bar chart, pie graph, etc.). There are at least two implementations of the Grammar of Graphics idea in R, of which the most popular is the ggplot2 package written by Prof. Hadley Wickham. Just as with natural languages, ggplot2 has a surface structure made up of R vocabulary elements, as well as a deep structure that mediates the link between the vocabulary and the "semantic" representation of the data shown on a computer screen. In this introductory presentation, the links among these levels of representation are demonstrated, so that new ggplot2 users can build the mental models necessary for fluent and creative visualization of their data.

Harlan D. Harris, PhD, is a statistical data scientist working for Kaplan Test Prep and Admissions in New York City. He has degrees from the University of Wisconsin-Madison and the University of Illinois at Urbana-Champaign. Prior to turning to the private sector, he worked as a researcher and lecturer in various areas of Artificial Intelligence and Cognitive Science at the University of Illinois, Columbia University, the University of Connecticut, and New York University.

Michael Milton is a Client Manager at Blue State Digital. When he's not saving the world by designing interactive marketing strategies that connect passionate users with causes and organizations, he writes about data and analytics. For O'Reilly Media, he wrote Head First Data Analysis and Head First Excel and has created the videos Great R: Level 1 and Getting the Most Out of Google Apps for Business.

Michael's talk is called "How to Save the World Using R." In this wide-ranging discussion, Michael will highlight individuals and organizations who are using R to help others as well as ways in which R can be used to promote good statistical thinking."

Visit DC useR Group Meeting

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Wednesday October 20, 2010

Room: Pan Am Foyer

Registration & Continental Breakfast

Room: Magnolia
Predictive Analytics and Business Performance

In this session, Bruno Aziza will discuss the challenges organizations face with Analytics and Performance. This participative session will provide first-hand accounts from Fortune 500 companies who are winning by building accountability, intelligence, and informed decision-making into their organizational DNA.

Speaker: Bruno Aziza, Director, Worldwide Strategy Lead, Business Intelligence, Microsoft

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Room: Magnolia

Gold Sponsor Presentation
Let's Talk About Fleas!

That's right, fleas! Fleas learn habits that place artificial limits on themselves. Unfortunately, many analytic professionals fall into this same trap. While there are options available today that can tremendously improve the efficiency and scalability of an analytic environment, many companies are stuck in the old way of doing things and are failing to cash in on the benefits. Would you like to spend more time on analysis and less time fighting to get your data together? If so, then listen to this discussion on how to make sure your organization has not fallen into a flea-like trap.

Speaker: Bill Franks, Chief Analytics Officer, Global SAS Program Teradata Corporation

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Room: Pan Am Foyer

Break / Exhibits

Room: Magnolia

Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream

Predictive analytics has taken off, across industry sectors and across applications in marketing, fraud detection, credit scoring and beyond. Where exactly are we in the process of crossing the chasm toward pervasive deployment, and how can we ensure progress keeps up the pace and stays on target?

This expert panel will address:

  • How much of predictive analytics' potential has been fully realized?
  • Where are the outstanding opportunities with greatest potential?
  • What are the greatest challenges faced by the industry in achieving wide scale adoption?
  • How are these challenges best overcome?

Panelist: David Duling, Research and Development Director, SAS

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

Panelist: Jason Verlen, Director, SPSS Product Strategy & Management, IBM Software Group

Panel moderator: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World

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Room: Magnolia

Gold Sponsor Session

Room: Magnolia

SASLab Session:Live Topical Demo
Applied Customer Segmentation - Knowing Your Customers

This lab workshop will begin with some very basic segmentation concepts such as RFM and then quickly move to how you can better understand your customer by understanding their purchase behavior and basic firmagraphic metrics using techniques such as Clustering and SOM Neural Networks. Product affinity scoring and methods to implement attitudinal segments to your customers obtained from surveys will be reviewed as well. If you need to understand your customers and how best to set up strategic plans for your customer base, then this workshop will show you the how-to methods using SAS Enterprise Miner and Enterprise Guide.

SAS Speaker: Randy Collica, Solutions Architect, SAS

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Room: Walnut

IBMLab Session: Live Topical Demo
Case Study: The American Public University System
Taking the "risk" out of "at-risk" - identifying students before they drop out

Eager to keep your most valuable customers? So are universities. Hear how APUS created models using IBM® SPSS®Modeler to better identify at-risk students - successfully minimizing the number of dropouts. Whether your concern is at-risk students, minimizing customer churn or keeping constituents satisfied, attend this session to hear how predictive analytics can help you attract and retain customers.

IBM Speaker: Tim Daciuk, Predictive Analytics Solutions Architect, IBM

Customer Speaker: Dave Becher, Director of Academic Information Analysis, American Public University System

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Room: Pan Am Foyer

Birds of a Feather Lunch

Room: Magnolia

Special Plenary Session
Case Study: Yahoo! and other large on-line e-businesses
Search Marketing and Predictive Analytics: SEM, SEO and
On-line Marketing Case Studies

Search Engine Marketing is a $15B industry in the U.S. growing to double that number over the next 3 years. Worldwide the SEM market was over $50B in 2010. Not only is this a fast growing area of marketing, but it is one that has significant implications for brand and direct marketing and is undergoing rapid change with emerging channels such as mobile and social. What is unique about this area of marketing is a singularly heavy dependence on analytics:

  • Large numbers of variables and options
  • Real-time auctions/bids and a need to adjust strategies in real-time
  • Difficult optimization problems on allocating spend across a huge number of keywords
  • Fast-changing competitive terrain and heavy competition on the obvious channels
  • Complicated interactions between various channels and a large choice of search keyword expansion possibilities
  • Profitability and ROI analysis that are complex and often challenging

The size of the industry, its growing importance in marketing, its upcoming role in Mobile Advertising, and its uniquely heavy reliance on analytics makes it particularly interesting as an area for predictive analytics applications. In this session, not only will hear about some of the latest strategies and techniques to optimize search, you will hear case studies that illustrate the important role of analytics from industry practitioners.

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

Speaker: Nick Besbeas, SVP of Marketing at Yahoo!

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Room: Magnolia

Gold Sponsor Presentation
Going beyond the numbers. Turning data into insights.

In this session, David Stewart, National Leader of Deloitte Canada's Analytic and Forensic Technology group will provide an overview of how advanced analytic techniques were used in executing one of the largest lottery investigations ever conducted. Using a combination of deductive and inductive techniques including advanced segmentation and identity resolution, the lottery corporation's potential risk exposure from inappropriate lottery play practices was quantified and trended. The ability to break down data silos to provide a holistic view of lottery play helped the corporation increase the effectiveness of its "player protection" initiatives. These analytic techniques also helped uncover highly valuable consumer insights. The outcome of this massive exercise now serves as the foundation for the corporation to build predictive models for enhancing social responsibility in gaming, and use its competitive intelligence to respond to emerging competition from alternative channels.

Speaker: David Stewart, Partner, Deloitte

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Room: Magnolia
Track 1: Sales Force Optimization
Case Study: Corporate Executive Board
Enhancing Sales Force Effectiveness with Predictive Analytics

Does Sales expense represent a large portion of your company's cost structure? Listen up! The Corporate Executive Board (CEB), a research and decision-support company counting over 85% of the Fortune 500 as customers, improves Sales effectiveness by utilizing analytic models that are typically employed for Marketing purposes.

Applications include territory design, quota-setting & sales-force productivity and have been built into the fabric of Sales Operations and the executive mindset at CEB. Since initial implementation 6 years ago, CEB has expanded the partnership between Sales and Analytics: models have evolved and improved over time and the application of models has broadened in scope. Tangible examples of tools will be shared and this presentation will have a strong focus on how to effectively apply analytics to real-world business problems.

Speaker: Nancy Hersh, Managing Director of BI, Corporate Executive Board

Room: Walnut
Track 2: Recommender Systems
Case Study: Oracle OpenWorld 2010
Conference Session Recommendation Engine for Oracle OpenWorld

With thousands of sessions at Oracle OpenWorld 2010, finding the right sessions to attend can be challenging. Recommending sessions to attendees can highlight sessions they might not otherwise find. In this case study, learn how data mining was employed in the conference's Schedule Builder session-enrollment application at Oracle OpenWorld and JavaOne/Oracle Develop 2010 to recommend sessions to attendees. Attendees will learn about the methodology applied to solve this problem, which includes building models using previous year's data and applying those models to current year session text and attendee profiles. We demonstrate data and text mining for k-means clustering and naive Bayes classification, while highlighting advanced text features including lexer definition, stopword list definition, and text index creation.

Speaker: Mark Hornick, Senior Manager, Oracle

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Room: Pan Am Foyer

Breaks /Exhibits


Room: Magnolia
Track 1: Privacy
Thought Leadership
Privacy by Design: The Future of Privacy, Standards and Tech Policy

Privacy and analytics are often publicly positioned as mortal enemies, but are they really?
A leading thinker on privacy and information policy, Ari Schwartz suggests that the two worlds may have some real differences, but can probably live a peaceful coexistence if they simply understand where the other is coming from. Schwartz will discuss how fair information practices can be helpful in building analytics tools that help improve data quality while satisfying basic privacy protections.

Speaker: Ari Schwartz, Senior Internet Policy Advisor, National Institute of Standards and Technology

Room: Walnut
Track 2: Text Mining & Fraud Detection
Case Study: MetLife
Fighting Fraud with Text Mining at MetLife Auto & Home

We will discuss the introduction of a predictive modeling solution for homeowner insurance claims fraud. Starting with a general overview of the business and technical challenges, we will progress through the modeling process, and conclude by presenting real world results. The use of text mining will be highlighted.

Speaker: David McMichael, Assistant Vice President, MetLife Auto & Home

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Room: Magnolia
Track 1: Catalog Retailer
Case Study: Miles Kimball
Miles Kimball Optimizes Marketing Decisions

Miles Kimball is a leader in the catalog business, offering cards, gifts, household items, and food products. We use leading-edge technologies to optimize our catalog circulation decisions across six titles, to each and every customer, each and every month. We reduce advertising costs with minimal impact on sales, and re-invest to improve reactivation. The solution utilizes our predictive models, our marketing plans, and our business objectives to optimize circulation decisions. A revolutionary approach leverages mathematical, statistical, and optimization technologies to generate seven-figure annual gains over a comparable control group. Marketing has broken new ground in contact optimization with predictive analytics.

Speaker: Ryan Hennig, Vice President of Catalog Marketing, Miles Kimball

Room: Walnut
Track 2: Text Mining
Case Study: Xerox PARC
Exploiting Unstructured Data Sources for Predictive Applications

A barrier to applying Predictive Analytics more broadly is that more than 95% of information is unstructured (documents, email, web pages, audio, images, etc.) making it difficult for analytic systems to identify relationships between data elements. However, some structure can be extracted using technologies such as natural language parsing, entity extraction, image matching, context-awareness, and behavior modeling. This session will cover some of the Contextual Intelligence work at the Palo Alto Research Center (PARC) in creating new business opportunities based on predictive analytics applied to unstructured data sources. We will demonstrate the use of such methods in a case study of a consumer service that personalizes recommendations by predicting a mobile user's future locations and leisure activity category. The service is in trials in Japan during Spring 2010.

Speaker: Bo Begole, Principal Scientist, Palo Alto Research Center, Inc. (a Xerox Company)
Speaker: Lawrence Lee, Director of Business Development, Palo Alto Research Center, Inc. (a Xerox Company)

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Thursday October 21, 2010

Room: Walnut

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

Click here for the detailed workshop description

  • Registration and breakfast starts at 8:00am
  • 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 Elder, Ph.D., CEO and Founder, Elder Research, Inc.

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