CONFERENCE PREVIEW - Predictive Analytics World

part of 

MARCH 4 - 10, 2012 ● SAN FRANCISCO, CA 

CONFERENCE PREVIEW 

www.predictiveanalyticsworld.com/sanfrancisco/2012 

KEYNOTE SPEAKERS 

Piyanka Jain 

CEO, Aryng.com 

Former PayPal Business Analytics Head 

Anne Robinson 

Director of Supply Chain Strategy & Analytics 

Verizon Wireless 

Eric Siegel 

Conference Chair 

Predictive Analytics World 

PRODUCED BY

An Invitation 

Dear Analytical Innovators and Practitioners, 

I invite you to join us this March in San Francisco for Predictive Analytics World, the business-focused event, loaded with 

predictive analytics case studies, expertise and resources. This conference brings professionals and experts together in 

order to keep predictive analytics deployment moving forward, strengthening the impact it delivers and establishing new 

opportunities. 

PAW’s San Francisco 2012 program features over 40 sessions across two tracks - an “All Audiences” and an “Expert/ 

Practitioner” track -- so you can witness how predictive analytics is applied at enterprises such as Altos Research, Alberta 

Motor Association, Broadspire, CompassLabs , Hewlett Packard, Interclick, Kelley Blue Book, LinkedIn, MLB, NFL, NBA, 

Pfizer, Sisters of Mercy Health Systems, Social Media Research Foundation, University of Phoenix, Volunteers of America, 

Wells Fargo, Yahoo!, YMCA, ZZAlpha, California General Underwriters Insurance Company, an IT support firm and a 

sales workforce, real-world examples in Financial Services, Emergency Response, true-to-life anecdotes based on misc. 

enterprise successes plus insights from projects for Anheuser-Busch, Dept. Homeland Security, and US Postal Service 

Office of Inspector General. 

PAW’s March agenda covers hot topics and advanced methods such as agile analytics, behavior-based advertising, 

blackbox trading, branch location assessment, churn modeling, clinical healthcare, cloud analytics, crowdsourcing predictive 

analytics, econometric indicators, education, enterprise-wide analytics, forecasting, HR analytics, insurance, list sourcing, 

non-profits, online marketing, real estate market scoring, risk management, social data, sports analytics, targeting direct 

marketing, uplift modeling, and other innovative applications that benefit organizations in new and creative ways.. 

Join PAW and access our premier keynotes, sessions, workshops, exposition, expert panel, live demos, networking coffee 

breaks, and reception. 

Eric Siegel Ph.D. 

Program Chair 


New to Predictive Analytics 

If you’re new to the field, 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! 

Access the free online Predictive Analytics Guide: www.pawcon.com/guide 

© 2012 Rising Media, Inc. 1 www.predictiveanalyticsworld.com/sanfrancisco/2012

Agenda Overview 

for all levels 

for expert/Practitioners 

Sunday, March 4, 2012 

9:00am-4:30pm 

Full-Day Workshop 

R for Predictive Modeling: A Hands-On Introduction s 

Max Kuhn, Director, Nonclinical Statistics, Pfizer 

DAY 1: Monday, March 5, 2012 

8:00-9:00am 

9:00-9:45am 

9:45-10:05am 

10:05-10:10am 

10:10-10:40am 

Registration & Breakfast 

Keynote 

Persuasion by the Numbers: Optimize Marketing Influence by Predicting It 

Eric Siegel, Program Chair, Predictive Analytics World 

Diamond Sponsor Presentation 

Gold Sponsor Presentation 

Breaks / Exhibits 

Track 1: All Levels 

Track 2: Expert/Practitioners 

Analytics in the Cloud 

10:40-11:00am 

11:05-11:25am 

Case Study: Five Growth Scenarios 

for Predictive Analytics in the Cloud l 

James Taylor, Decision Management Solutions 

Crowdsourcing Predictive Analytics 

Case Study: Wikipedia l 

Solving the Last Mile: 

Focusing Global Intelligence on Your Data 

Mehul Patel, Kaggle 

Case Study from 

s 

Dean Abbott, Abbott Analytics 

11:30am- 

12:15pm 

Special Plenary Session 

Case Studies: Anheuser-Busch, Dept. Homeland Security, 

& US Postal Service Office of Inspector General 

Becoming an Ace with a Robot as your Wingman 

John Elder, CEO & Founder, Elder Research, Inc. 

12:15-12:20pm 

12:20-12:40pm 

12:40-1:40pm 

1:40-2:25pm 

2:25-2:35pm 


Lightning Round of 2-Minute Sponsor Presentations 

Birds of a Feather Lunch / Exhibits 


Consumer Applications Identity Wars: The Battle to Control Personal Data 

Andreas Weigend, weigend.com, Former Chief Scientist, Amazon.com 

Platinum Sponsor Presentation 



2:35-2:45pm 


2:50-3:10pm 

3:15-3:35pm 


Thought Leadership 

Case Study: CA General Underwriters Insurance l 

Seize the Competitive Future through a 

Shared Vision for Value Creation, 

Quality Management & Collaboration 

Arnold Goodman, Collaborative Data Solutions 

& Stephanie Behnke, CA General 

Underwriters Insurance 

Insurance 

Case Study: Alberta Motor Association l 

Insurance Pricing Models using 

Predictive Analytics 

Richard Boire, Boire Filler Group 

Insurance 


Real Estate Market Scoring 

Case Study: Altos Research s 

There & Back Again: Model Interpretability 

in Real Estate Market Scoring 

Ben Gimpert, Altos Research 

Branch Location Assessment 

Case Study: YMCA s 

Using Probabilistic Computing 

to Optimize YMCA Branch Site Locations 

Bill Lazarus, SeerAnalytics 

Sponsored Lab 

3:40-4:00pm 

4:00-4:35pm 

4:35-4:55pm 

5:00-5:20pm 

5:25-5:45pm 

5:50-6:10pm 

6:10-7:30pm 

7:30-10:00pm 

Case Study: Broadspire l 

The First 24 Hours: Understanding New Claims 

Gary Anderberg, Broadspire 

& Sergo Grigalashvili, Crawford & Company 

Econometric Indicators 

Case Study: LinkedIn l 

Econometric Applications & Extracting 

Economic Insights from the LinkedIn Dataset 

Scott Nicholson, LinkedIn 

Forecasting 

Case Study: Wells Fargo Securities 

Macroeconomic Forecasting, Consensus & 

Individual Forecaster: A Real-Time Approach l 

Azhar Iqbal, Wells Fargo Securities 

Social Data 

Case Study: Social Media Research Foundation l 

Crowd Photography for Social Media 

Marc Smith, Social Media Research Foundation 

Social Data 

Case Study: Real-world examples in 

Financial Services, Emergency Response l 

Exploring Social Data: Use Cases for 

Real-World Application 

Chris Moody, Gnip 


Reception / Exhibits 

Bay Area SAS Users Group Meeting 

Lab Session: Live Topical Demo s 

Net Lift Modeling 

Case Study: Market Share Partners s 

Response Modeling is the Wrong Modeling: 

Maximize Impact With Uplift Modeling 

Kim Larsen, Market Share Partners 

Behavior-Based Advertising 

Case Study: Yahoo! s 

How to Get the Exact Same Online 

Display Advertising Results with 

Only 55% of the Spending 

Liwei Ma, Big Fish Games 


Case Study: CompassLabs s 

Prediction and Optimization Models 

for Online Display Advertising 

Mahesh Kumar, Tiger Analytics 


Case Study: Interclick Data s 

Data, Data Everywhere: Navigating the 

Digital Ecosystem with Predictive Analytics 

Yuan Ren, Interclick 



8:00-9:00am 

9:00-9:45am 

9:45-10:05am 

10:05-10:35am 

10:35-11:20am 

11:20-11:40am 

11:45am-12:30pm 

12:30-1:30pm 

1:30-2:15pm 

2:15-2:20pm 

2:20-2:25pm 

2:30-2:50pm 

DAY 2: Tuesday, March 6, 2012 


Keynote 

Influencers, Skeptics, and Data Geeks 

Anne Robinson, Director of Supply Chain Strategy & Analytics, Verizon Wireless 



Expert Panel 

Wise Enterprise: Best Practices for Managing Predictive Analytics 

Lightning Round of 2-Minute Sponsor Presentations 


Sponsored Lab 

Lab Session: Live Topical Demo l 


Keynote 

The Five Myths of Predictive Analytics 


Sponsored Lab 

Lab Session: Live Topical Demo s 

Piyanka Jain, CEO, Aryng.com, Former PayPal Business Analytics Head 

Sports Analytics 

Case Study: Major League Baseball l 

MLB Pitchers: A Look at the Numbers 

Bartev Vartanian, Dataspora 



Retaining Subscribers 

Case Study: True-to-Life Anecdotes Based on 

Misc. Enterprise Successes Subscription s 

Survival Modeling for Fun & Profit 

Jim Porzak, Viadeo 

2:55-3:15pm 


Case Study: NFL, MLB, & NBA l 

Competing & Winning with Sports Analytics 

Benjamin Alamar, Menlo College 

HR Analytics 

Case Study: An IT Support Firm & 

a Sales Workforce s 

Creating an Engaged Workforce 

using Statistical Learning 

Salman Taherian, Kasra Technologies 

3:15-3:55pm 




3:55-4:15pm 

4:20-4:40pm 

4:45-5:05pm 

5:10-5:30pm 


Enterprise-Wide Analytics 

Case Study: Hewlett Packard Global Analytics l 

Institutionalization of Enterprise Wide 

Analytics to Help Organizations Make Better 

Decisions, Faster 

Rohit Tandon, Hewlett Packard 

Education 

Case Study: University of Phoenix l 

Predicting Student Success 

Rebecca Barber, University of Phoenix 

Agile Analytics 

Case Study: Kelley Blue Book l 

Agile Analytics: Model Development in 

an Agile Environment 

Shawn Hushman, Kelley Blue Book 

Non-Profit 

Case Study: Volunteers of America, Chesapeake l 

Turning Operational Outcome Metrics into 

an Actionable Predictive Forecasting Model 

Shyam Desigan, Volunteers of America, Chesapeake 


Healthcare Analytics 

Case Study: Pfizer s 

Right Medicine Right Patient 

Max Kuhn, Pfizer 

Clinical Healthcare 

Case Study: Sisters of Mercy Health Systems s 

Framework for Detection of Clinical 

States & Disease Onset Using Electronic 

Health Record (EHR) Data 

Jeni Fan, Booz Allen Hamilton 

Fraud Detection 

TBA Session on Fraud Detection s 

Antonia de Medinaceli, Elder Research, Inc. 

Blackbox Trading 

Case Study: ZZAlpha s 

Effective Market Forecasting 

in 5 Key Dimensions 

Kevin Pratt, ZZAlpha 

Wednesday, March 7, 2012 


9:00am-4:30pm 

The Best & the Worst of Predictive Analytics: 

Predictive Modeling Methods & Common Data Mining Mistakes s 

John Elder, CEO & Founder, Elder Research, Inc 

Thursday, March 8, 2012 


9:00am–4:30pm 

Business Friendly Data Mining with Decision Management l 

James Taylor, CEO, Decision Management Solutions 

Friday, March 9 and Saturday, March 10, 2012 

9:00am-4:30pm 

Two-Day Workshop 

Net Lift Models: Optimizing the Impact of Your Marketing s 

Kim Larsen, Vice President of Analytical Insights, Market Shares Partners 


Full Agenda 

Monday, March 5, 2012 

9:45am-7:30pm 

Exhibit Hall Open 

8:00-9:00am 


9:00-9:45am 

Keynote 

Persuasion by the Numbers: 

Optimize Marketing Influence by 

Predicting It 

Data driven marketing decisions are meant to maximize 

impact - right Well, the only way to optimize marketing 

influence is to predict it. The analytical method to do this 

is called uplift modeling. This is a completely different 

animal from what most models predict: customer 

behavior. Instead, uplift models predict the influence on 

customer behavior gained by choosing one marketing 

action over another. The good news is case studies show 

ROI going where it has never gone before. The bad 

news You need a control set... But you should have 

been using one anyway! The crazy part is that “marketing 

influence” can never be observed for any one customer, 

since it literally involves the inner workings of the 

customer’s central nervous system. If influence can’t be 

observed, how can we possibly model and predict it 

Speaker: Eric Siegel, Program Chair, Predictive Analytics 

World 

9:45-10:05am 


10:05-10:10am 


10:15-10:40am 


for all levels 

for expert/Practitioners 

10:40-11:00am 

l Track 1: Analytics in the Cloud 

Case Study: Five Growth Scenarios 

for Predictive Analytics in the Cloud 

Predictive analytics and cloud technologies are hot topics 

individually, but how can you use them together In this 

session industry expert James Taylor will discuss the use 

cases for predictive analytics in the cloud. He will show 

how companies at every stage of analytic sophistication 

can use cloud-based predictive analytic approaches, 

discuss the value propositions of the different use cases, 

outline the pros and cons of predictive analytics in 

the cloud, and present results from a recent survey on 

people’s attitudes to these exciting technologies. 

Speaker: James Taylor, CEO, Decision Management 

Solutions 

10:40-11:25am 

s Track 2: Case Study 

Speaker: Dean Abbott, President, Abbott Analytics 

11:05-11:25am 

l Track 1: Crowdsourcing Predictive Analytics 

Case Study: Wikipedia 

Solving the Last Mile: Focusing 

Global Intelligence on Your Data 

As companies gather and collect more and more data 

across their organizations, the “last mile” of actionable 

insights based on that data has become increasingly 

crucial to success. Public contests provide a potent, 

rapidly expanded means to facilitate vastly better 

predictions for your companies, by accessing over 16,000 

leading data scientists around the world who compete 

to produce the best results for any given data problem. 

By correcting the existing mismatch between companies 

needing better predictions and data scientists wanting 

access to the most challenging problems and real world 

data, public competition proves over and over to be a 

“win-win-win”. 

Speaker: Mehul Patel, Chief Operating Officer, Kaggle 


Full Agenda 

11:30am-12:15pm 


Case Studies: Anheuser-Busch, Dept. 

Homeland Security, & US Postal 

Service Office of Inspector General 

Becoming an Ace with a Robot as 

your Wingman 

Humans and computers have strengths that are more 

complementary than alike – to the point where a 

sophisticated algorithm may be the best “2nd person” 

to put on a complex task. By contrasting natural and 

artificial intelligence we will explore how to optimize the 

man/machine partnership. 

Speaker: John Elder, CEO & Founder, Elder Research, Inc. 

12:15-12:20pm 


12:20-12:40pm 

Lightning Round of 2-Minute Sponsor 

Presentations 

12:40-1:40pm 


1:40-2:25pm 

Special Plenary Session - Consumer 

Applications 

Identity Wars: The Battle to Control 

Personal Data 

Personal data is the doppelganger of the consumer, 

her very identity as a commercial being. Open the 

floodgates, as most of the vast quantities of enterprise 

data generated each day is - one way or another – 

personal; data that’s transactional, social, local, mobile 

and on and on. 

As the leading players jockey for position, control is the 

name of the game, and the stakes couldn’t be higher. 

We are early in the process of establishing a consumer 

identity ecosystem, standing on the cusp of major 

developments. New paradigms will be established and 

astounding enterprise power stands to be gained. Does 

anyone own consumer identity data What precisely does 

“ownership” of personal data mean In any case, the 

objective is not to own the transaction but to control the 

data it generates. Facebook and Google are central, but 

dozens of established enterprises and innovative startups 

are in the game. 

In this special plenary session, Dr. Andreas Weigend will 

illuminate where identity data is, where it’s going, and 

how to leverage it. 

Speaker: Andreas Weigend, weigend.com, Former Chief 

Scientist, Amazon.com 

2:25-2:35pm 


2:35-2:45pm 


2:50-3:10pm 

l Track 1: Thought Leadership 

Case Study: CA General Underwriters 

Insurance 

Seize the Competitive Future through 

a Shared Vision for Value Creation, 

Quality Management & Collaboration 

Predictive Analytics facilitates “anticipating future needs 

so we can actively create the future” (Drucker). Enrich 

it and your organization by creating more value from 

predictions, managing prediction quality and genuine 

collaboration among stakeholder teams. Stakeholders 

include your prediction financiers, owners and users 

plus analytics developers, implementers and insurers. 

Explore “what to do and how to do it” for collaboration, 

quality management and value creation plus appreciate 

Drucker’s Philosphies, Deming’s Principles, Juran’s 

Processes and Ackoff’s Pitfalls. Tribal cultures and their 

immune systems are primary inhibitors of collaboration. 

To ensure competitive future success of Predictive 

Analytics, we need to selectively relax them. 

Speakers: Arnold Goodman Arnold Goodman, Founder 

& Principal, Collaborative Data Solutions & Stephanie 

Behnke, President, California General Underwriters 

Insurance Company 


Full Agenda 

s Track 2: Real Estate Market Scoring 

Case Study: Altos Research 

There & Back Again: Model 

Interpretability in Real Estate 

Market Scoring 

Seasoned predictive analytics practitioners understand 

that simple “accuracy” is the beginning of model 

validation not the end. Perfect accuracy on your own 

training data is trivial. How confident are we in our 

predictions during truly unprecedented scenarios The 

business builds confidence and optimizes “variance” by 

involving itself in the gritty modeling process. Black boxes 

are difficult for the business to interpret so improving 

robustness often means going back to more transparent 

models. Ben will present a case study in local residential 

real estate market scoring when non-parametric 

ensemble methods were left behind for marginally less 

accurate but interpretable linear models. 

Speaker: Ben Gimpert Director of Quantitative 

Analytics, Altos Research 

3:15-3:35pm 

l Track 1: Insurance 

Case Study: Alberta Motor Association 

Insurance Pricing Models using 

Predictive Analytics 

The use of predictive analytics solutions as a pricing 

tool for insurance is a very recent phenomenon 

amongst actuaries. This case study examines what 

tools were used in the past and what has led to the 

adoption of predictive analytics solutions within the 

actuarial discipline. Particular emphasis is devoted to 

the significant data challenges which are unique to the 

insurance pricing sector. At the same time, attendees 

will learn the process that was adopted in building these 

tools. More importantly, attendees will understand how 

to demonstrate the value or benefit of predictive analytics 

solutions over existing actuarial tools. 

Speaker: Richard Boire, Partner, Boire Filler Group 

s Track 2: Branch Location Assessment 

Case Study: YMCA 

Using Probabilistic Computing to 

Optimize YMCA Branch Site Locations 

The YMCA operates a network of 2000+ branches, 

each of which is a $10M+ special-purpose investment. 

Predicting viability of new branch sites and benchmarking 

performance potential of existing sites is a high-value 

challenge which Seer Analytics has worked closely with 

the YMCA to tackle. 

Seer recently evaluated probabilistic computing models 

for retail site location analysis. Seer’s probabilistic models 

were developed in a week and were as predictive as a 

dedicated suite of logistic regression models developed 

over 18 months for the YMCA. The probabilistic models 

also generated per-estimate confidence intervals, 

offering unique insight into the risk associated with each 

prediction. 

Speaker: Bill Lazarus, President & CEO, SeerAnalytics 

3:40-4:00pm 

l Track 1: Insurance 

Case Study: Broadspire 

The First 24 Hours: Understanding 

New Claims 

Broadspire, a claim administrator, gets thousands of new 

workers’ compensation claims every week. Our internal 

methodology uses an expert research database, an 

intelligent interview process, and a sophisticated scoring 

tool to predict outcomes and resource needs for these 

claims as we conduct our three point interviews within 24 

hours of the first report. We will present the high dollar 

business problem that this method addresses, examine 

the development of the system through five years of real 

world use, and show the ways in which it has improved 

the claim handling process and the bottom line for 

Broadspire and its clients. 

Speakers: Gary Anderberg, Practice Leader of Analytics 

& Outcomes, Broadspire & Sergo Grigalashvili, Vice 

President of Architecture, Analytics, Information & 

Communication Technology, Crawford & Company 


Full Agenda 

s Track 2: Sponsored Lab 

Lab Session: Live Topical Demo 

4:35-4:55pm 

l Track 1: Econometric Indicators 

Case Study: LinkedIn 

Econometric Applications & Extracting 

Economic Insights from the LinkedIn 

Dataset 

Using examples from the LinkedIn dataset, this talk 

will highlight how applied economic intuition can be a 

valuable tool in extracting value from big data. We will 

discuss how econometric techniques expand the toolkit 

of data scientists, especially when trying to pull causation 

out of correlation in observational data. For example, 

what site activities are predictive of future engagement 

The second part of the talk will show how big data helps 

us understand the effects of economic events, especially 

with regard to the recent financial crisis. We will discuss 

predicting economic indicators and uncover other insights 

related to the economy. 

Speaker: Scott Nicholson, Senior Data Scientist, 

LinkedIn 

s Track 2: Net Lift Modeling 

Case Study: Market Share Partners 

Response Modeling is the Wrong 

Modeling: Maximize Impact With 


The true effectiveness of a marketing campaign isn’t 

response rate! It’s the incremental impact - that is, 

additional revenue directly attributable to the campaign 

that would not otherwise have been generated. Yet 

traditional targeting criteria are often designed to find 

clients that are interested in the product, but would have 

bought it whether or not they received a promotion. In 

such cases, the incremental impact is insignificant and 

the marketing dollars could have been spent elsewhere. 

Net Lift Models are designed to maximize incremental 

impact by targeting the undecided clients that can be 

motivated by marketing. These “swing customers” are 

akin to the swing states of a presidential election; data 

miners could learn a lot from presidential campaigns. 

Beyond targeted marketing, Net Lift methodology delivers 

tremendous performance improvements for deployed 

churn models - retaining “savables” while avoiding the 

adverse “reverse” affects retention outreach triggers for 

some customers - as well as other innovative business 

applications of this advanced analytical method. 

This keynote will demonstrate how to build Net Lift 

Models (also referred to as Uplift or Incremental Lift) that 

optimize the incremental impact of marketing campaigns, 

discussing the pros and cons of multiple core analytical 

approaches. 

Speaker: Kim Larsen, Vice President of Analytical 

Insights, Market Share Partners 

4:00-4:35pm 

Break / Exhibits 

5:00-5:20pm 

l Track 1: Forecasting 

Case Study: Wells Fargo Securities 

Macroeconomic Forecasting, 

Consensus & Individual Forecaster: 

A Real-Time Approach 

This study provides a real-time short-term 

macroeconomic forecasting approach that offers several 

advantages over conventional short-term forecasting 

procedures. The approach produces more accurate 

real-time forecasts compared to those of the Bloomberg 

real-time consensus forecast, on average, for major 

macroeconomic variables. 

This study sheds light on five important areas of 

macroeconomic forecasting. 

Speaker: Azhar Iqbal, Vice President & Econometrician, 

Wells Fargo Securities 

s Track 2: Behavior-Based Advertising 

Case Study: CompassLabs 


for Online Display Advertising 

According to eMarketer, the internet’s share of total 

media spending will continue to rise, from 15% in 

2010 to 20% in 2014 and the growth in online display 


Full Agenda 

advertising will outpace total online ad spending through 

2014. The trend indicates greater online ad competition 

and the weaker and slowly improving economy 

means more spending pressure, so how to deliver the 

stronger ROI becomes a key success factor for all online 

marketers. Today I am introducing a strategy which will 

let you get the exact same online display advertising 

results with only 55% of the spending. 

Speaker: Liwei Ma, Senior Director or Analytics & 

Insights, Big Fish Games 

5:25-5:45pm 

l Track 1: Social Data 

Case Study: Social Media Research 

Foundation 

Crowd Photography for Social Media 

Crowds of people gather in social media around many 

products, services, businesses, and events but they can be 

difficult to see and understand. With new free and open 

tools, it is now possible to map and measure social media 

spaces, capturing the sub-groups and key people within 

and between them. Learn how to capture social media 

data and quickly generate a visual map of the crowd. 

With maps in hand, we will discuss ways they guide a 

journey to the key influencers and concepts in the crowd. 

Speaker: Marc Smith, Chief Social Scientist, Social Media 

Research Foundation 

s Track 2: Behavior-Based Advertising 

Case Study: CompassLabs 


for Online Display 

A challenging task in online advertising is to identify a 

combination of the right advertisement to right customer 

on right website at right time. In this talk, I will present 

a data-driven approach we developed to estimate the 

likelihood that a user would click an online display 

advertisement based on a set of user and webpage 

characteristics. We used this model to identify the optimal 

campaign and its bid price for an ad space available 

for purchase on a real-time ad-exchange. Our model is 

currently being used by a startup company in providing 

online advertising solutions to Fortune 500 companies. 

Speaker: Mahesh Kumar, CEO, Tiger Analytics 

5:50-6:10pm 

l Track 1: Social Data 

Case Study: Real-world Examples in 

Financial Services, Emergency Response 

Exploring Social Data: Use Cases for 

Real-World Application 

With billions of social activities passing through the evergrowing 

real-time social web each day, companies are 

beginning to harness the power of social data. However, in 

today’s social data economy, not all social data is created 

equal. While all social media data can be valuable, it is a 

matter of discovering which type of social data is best suited 

for each specific use case. Through this session, participants 

will learn from real-world case studies in Financial Services, 

Emergency Response, Brand Analytics and other industries 

about how businesses are applying social data to their 

operations to drive value. 

Speaker: Chris Moody, President & COO, Gnip 

s Track 2: Behavior-Based 

Advertising Case Study: Interclick 

Data, Data Everywhere: Navigating 

the Digital Ecosystem with Predictive 

Analytics 

In today’s digital ecosystem, the challenge marketer’s face 

isn’t access, but it is determining the application and value 

of data. This presentation will demonstrate how interclick 

has pioneered data-driven advertising technologies to find 

applicable online audiences to meet digital campaign goals. 

Our challenges are testing multiple predictive modeling 

techniques on Big Data to build effective audiences and 

making algorithms align with multiple goals. 

Utilizing statistical data mining techniques such as hypothesis 

testing, collaborative filtering, and model ensemble, 

we have developed cutting-edge analytics products that 

recommend effective audiences with higher response rates 

in shorter time. Real results will be presented. 

Speaker: Yuan Ren, Data Mining Scientist, Interclick 

6:10-7:30pm 

Reception / Exhibits 

7:30-10:00pm 

Bay Area SAS User Group Meeting 


Full Agenda 

Tuesday, March 6, 2012 

8:00-9:00am 


9:00-9:45am 

Keynote 

Influencers, Skeptics, and Data Geeks 

Speaker: Anne Robinson, Director of Supply Chain 

Strategy & Analytics, Verizon Wireless 

9:45-10:05am 


10:05-10:35am 


10:35-11:20am 

Expert Panel 

Wise Enterprise: Best Practices for 

Managing Predictive Analytics 

Expert Panelists: TBA 

11:20-11:40am 

Lightning Round of 2-Minute 

Sponsor Presentations 

11:00-11:20am 

Sponsored Lab Session 

11:45-12:30pm 

l Track 1: Sponsored Lab 


s Track 2: Sponsored Lab 


12:30-1:30pm 


1:30-2:15pm 

Keynote 

The Five Myths of Predictive Analytics 

Predictive Analytics is powerful, it can help you predict an 

event or a behavior at a an individual customer level. It 

can help you spot golden nuggets from the deep-widebig 

data ocean; But is also one of the techniques which is 

not very well understood. With all the recent buzz about 

Predictive Analytics, it does seems like a new technique 

in the tool box. Is that so In this keynote, we will ground 

ourselves in the reality of building and maintaining an 

impactful Predictive Model and explore questions like 

• Is Predictive Analytics new 

• Is it a crystal ball 

• Is it perfect 

• Can it be built quickly and cheaply 

• Is it going to solve all my business problems 

• Does it always work 

• Can anybody learn how to build a model 

Speaker: Piyanka Jain, CEO, Aryng.com, Former PayPal 

Business Analytics Head 

2:15-2:20pm 


2:20-2:25pm 


2:30-2:50pm 

l Track 1: Sports Analytics 

Case Study: Major League Baseball 

MLB Pitchers: A Look at the Numbers 

Baseball enthusiasts and statisticians have found common 

ground in the field of Sabermetrics – the application of 

statistics to baseball. Since the installation of the PITCHf/x 

tracking system in major league ballparks, data has been 

generated on the type, velocity, and displacement for 


Full Agenda 

every pitch thrown in the MLB. We present the findings 

from an investigation focused on MLB pitchers leveraging 

this treasure trove of information. 

In this presentation we investigate how pitchers generate 

value. We discuss methods that explore pitching speed 

and control data simultaneously. We also identify the 

factors that are most important in determining value 

generation. 

Speaker: Bartev Vartanian, Principal, Dataspora 

s Track 2: Retaining Subscribers 

Case Study: True-to-Life Anecdotes 

Based on Misc. Enterprise 

Successes Subscription Survival 

Modeling for Fun & Profit 

Survival analysis started in reliability engineering and 

medical research. More recently it has been used by 

marketers to better understand customers in subscription 

based businesses. While the basic math doesn’t 

change, there are practical differences when applied to 

marketing. First, N is huge. Secondly, there are generally 

many cohorts driven by marketing questions around 

product, offer, price, acquisition source, and various 

subscriber properties. In a series of case studies, you will 

learn the basic ideas behind subscription survival and 

how to calculate average lifetime, LTV, compare cohorts, 

and answer what-if questions. The data and code used 

are given to participants. 

Speaker: Jim Porzak, Senior Data Scientist, Viadeo 

2:55-3:15pm 

l Track 1: Sports Analytics 

Case Study: NFL, MLB, & NBA 

Competing & Winning with 


The field of sports analytics ties together the tools of 

data management, predictive modeling and information 

systems to provide sports organization a competitive 

advantage. The field is rapidly developing based on 

new and expanded data sources, greater recognition 

of the value, and past success of a variety of sports 

organizations. Teams in the NFL, MLB, NBA, as well as 

other organizations have found a competitive edge with 

the application of sports analytics. The future of sports 

analytics can be seen through drawing on these past 

successes and the developments of new tools. 

Speaker: Benjamin Alamar, Professor of Sports 

Management, Menlo College 

s Track 2: HR Analytics 

Case Study: An IT Support Firm 

and a Sales Workforce 

Creating an Engaged Workforce 

Using Statistical Learning 

Top-performing organizations consider employee 

engagement as a key performance driver. Interviews 

and questionnaires are widely used to measure this 

engagement, yet results are often unreliable, discrete, 

and non-actionable. In this talk, we demonstrate how 

statistical learning can be used to estimate engagement 

from individuals’ low-level activity data. Using prior 

knowledge and Bayesian frameworks, we have built 

a probabilistic model that incrementally learns every 

employee’s true level of engagement. Results have 

helped organizations (an IT support firm and a sales 

workforce to date) to form cohesive teams that are more 

productive and engaged at the workplace. 

Speaker: Salman Taherian, CFO & CIO, Kasra 

Technologies 

3:15-3:55pm 


3:55-4:15pm 

l Track 1: Enterprise-Wide Analytics 

Case Study: Hewlett Packard, 

Global Analytics 

Institutionalization of Enterprise 

Wide Analytics to Help Organizations 

Make Better Decisions, Faster 

The presentation will talk about how organizations can 

leverage the structured and unstructured data within 

the enterprise and outside the enterprise to mine the 


Full Agenda 

insights and influence decision making across various 

functions. The speakers will share real life application of 

enterprise wide analytics in Hewlett Packard and how it 

drives business growth and improves profitability for the 

corporation. The presentation will demonstrate some 

of the cutting edge analytics that helps HP in better 

customer targeting, optimize the supply chain, improve 

customer experience, leverage the power of social media 

analytics, forecasting future scenarios for the business to 

win in the marketplace. 

Speaker: Rohit Tandon, Vice President of Global 

Analytics, Hewlett Packard 

s Track 2: Healthcare Analytics 

Case Study: Pfizer 

Right Medicine, Right Patient 

Can predictive modeling improve patient care A wealth 

of data exists in large healthcare databases on patient 

disease characteristics and their response to specific 

treatments. Max will discuss some of the technical and 

non-technical issues in providing care providers with 

quantitative results related to how individual patients 

might response to therapies. 

Speaker: Max Kuhn, Director of Nonclinical Statistics, 

Pfizer 

4:20-4:40pm 

l Track 1: Education 

Case Study: University of Phoenix 

Predicting Student Success 

With courses of 5-6 weeks in length, there is a narrow 

window in which to intervene when a student begins to 

struggle with a course. This project used data about a 

student’s classroom and online interactions, prior courses 

and demographic characteristics to create a system 

that prioritizes students based on their likelihood of 

successfully completing their current class. Advisers can 

use this prioritization to identify students most in need of 

academic, time-management, or financial-management 

coaching. Challenges and solutions will be discussed, as 

will next steps. 

Speaker: Rebecca Barber, Manager of Academic 

Analytics, University of Phoenix 

s Track 2: Clinical Healthcare 

Case Study: Sisters of Mercy Health 

Systems 

Framework for Detection of Clinical 

States & Disease Onset Using 

Electronic Health Record (EHR) Data 

This case study describes the application of predictive 

analytics to the detection of disease onset and clinical 

states through the use of electronic health records 

(EHR). The framework presented here aims to improve 

prediction of a patient’s risk for developing severe sepsis 

and septic shock through a risk score generated as a 

function of measurements of patient vitals over time. A 

risk score threshold of 0.71 was found to yield the highest 

sensitivity while minimizing false negatives in the patient 

database. This predictive model can also be generalized 

to predict outcomes of other application domains. 

Speaker: Jeni Fan, Associate, Booz Allen Hamilton 

4:45-5:05pm 

l Track 1: Agile Analytics 

Case Study: Kelley Blue Book 

Agile Analytics: Model Development 

in an Agile Environment 

Technology organizations have been using Agile 

development for more than 10 years to promote adaptive 

planning, development and delivery of software. In this 

session, discover how the Kelley Blue Book predictive 

analytics organization leverages agile analytics to 

increase team collaboration, reduced spin and improve 

cross-functional communication/relationships. Agile 

analytics has imparted structure in a seemingly 

unstructured development process, improved overall 

model performance and increased business owner 

acceptance. Discussion will provide analysts and analytics 

leadership with an effective framework to improve model 

efficiency, accuracy and applicability. 

Speaker: Shawn Hushman, Vice President of Advanced 

Analytics, Kelley Blue Book 


Full Agenda 

s Track 2: Fraud Detection 

TBA Session on Fraud Detection 

Speaker: Antonia de Medinaceli, Senior Business 

Analyst, Elder Research, Inc. 

5:10-5:30pm 

l Track 1: Non-Profit 

Case Study: Volunteers of America, 

Chesapeake 

Turning Operational Outcome 

Metrics into an Actionable Predictive 

Forecasting Model 

Analysis of program operation/case management 

outcomes often involved weeks of labor to decipher 

relationships and identify those that had a meaningful 

impact on organizations financial forecast. Nimble 

human service non-profits and social service agencies 

are starting to leverage the power of open-source 

Analytic tools like R and the native algorithms available 

to build robust Predictive modeling within their budgeting 

process. Already committed to a cloud computing 

platform and having a limited budget, learn how VOA 

leveraged cloud services and open-source components to 

create a robust dashboard and reporting solution for our 

executive management team. 

Speaker: Shyam Desigan, CFO, Volunteers of America, 

Chesapeake 

s Track 2: Blackbox Trading 

Case Study: ZZAlpha 

Effective Market Forecasting 

in 5 Key Dimension 

We demonstrate that a machine learning technique 

predicts relative future price in five key dimensions of 

the US equities market. The price forecasts answer key 

investment questions: In or Out of the market, Value or 

Growth style, Large or Small cap, Which is best sector, 

and What is the direction of the economic core. Forecasts 

are implemented using large ETFs. Returns exceed 

benchmarks and risk is reduced. Large Monte Carlo 

simulations confirm statistical confidence exceeding 3 

sigma (99.7%). 

Speaker: Kevin Pratt, Founder & Chief Scientist, ZZAlpha 


Workshops 

Sunday, March 4, 2012 

s Full-Day Workshop 

9:00am-4:30pm 

R for Predictive Modeling: A Hands-On 

Introduction 

Instructor: Max Kuhn, Director, Nonclinical Statistics, 

Pfizer 

Intended Audience: Practitioners who wish to learn 

how to execute on predictive analytics by way of the 

R language; anyone who wants “to turn ideas into 

software, quickly and faithfully.” 

Knowledge Level: Either hands-on experience with 

predictive modeling (without R) or hands-on familiarity 

with any programming language (other than R) is 

sufficient background and preparation to participate in 

this workshop. 

Workshop Description: This one-day session provides a 

hands-on introduction to R, the well-known open-source 

platform for data analysis. Real examples are employed 

in order to methodically expose attendees to best 

practices driving R and its rich set of predictive modeling 

packages, providing hands-on experience and knowhow. 

R is compared to other data analysis platforms, and 

common pitfalls in using R are addressed. 

The instructor, a leading R developer and the creator of 

CARET, a core R package that streamlines the process 

for creating predictive models, will guide attendees on 

hands-on execution with R, covering: 

• A working knowledge of the R system 

• The strengths and limitations of the R language 

• Preparing data with R, including splitting, resampling 

and variable creation 

• Developing predictive models with R, including 

decision trees, support vector machines and ensemble 

methods 

• Visualization: Exploratory Data Analysis (EDA), and 

tools that persuade 

• Evaluating predictive models, including viewing lift 

curves, variable importance and avoiding overfitting 

Hardware: Bring Your Own Laptop 

Each workshop participant is required to bring their 

own laptop running Windows or OS X. The software 

used during this training program, R, is free and readily 

available for download. 

Attendees receive an electronic copy of the course 

materials and related R code at the conclusion of the 

workshop. 

Schedule: 

• Workshop starts at 9:00am 

• Morning Coffee Break at 10:30-11:00am 

• Lunch provided at 12:30-1:15pm 

• Afternoon Coffee Break at 2:30-3:00pm 

• End of the Workshop: 4:30pm 

Wednesday, March 7, 2012 

s Full-Day Workshop 

9:00am-4:30pm 

The Best & the Worst of Predictive 

Analytics: Predictive Modeling 

Methods & Common Data Mining 

Mistakes 

Instructor: John Elder, CEO & Founder, Elder Research, 

Inc. 

Intended Audience: Interested in the true nuts and bolts 

Knowledge Level: Familiar with the basics of predictive 

modeling. 

Attendees will receive an electronic copy of the course notes 

via USB drive. 

Workshop Description: Predictive analytics has proven 

capable of enormous returns across industries – but, with so 

many core methods for predictive modeling, there are some 

tough questions that need answering: 

• How do you pick the right one to deliver the greatest 

impact for your business, as applied over your data 

• What are the best practices along the way 

• And how do you avoid the most treacherous pitfalls 

This one-day session surveys standard and advanced 

methods for predictive modeling. 

Dr. Elder will describe the key inner workings of leading 

algorithms, demonstrate their performance with business 

case studies, compare their merits, and show you how to 

pick the method and tool best suited to each predictive 

analytics project. Methods covered include classical 

regression, decision trees, neural networks, ensemble 

methods, uplift modeling and more. 


Workshops 

The key to successfully leveraging these methods is to avoid 

“worst practices”. It’s all too easy to go too far in one’s 

analysis and “torture the data until it confesses” or otherwise 

doom predictive models to fail where they really matter: on 

new situations. 

Dr. Elder will share his (often humorous) stories from realworld 

applications, highlighting the Top 10 common, but 

deadly, mistakes. Come learn how to avoid these pitfalls by 

laughing (or gasping) at stories of barely averted disaster. 

If you’d like to become a practitioner of predictive analytics 

– or if you already are, and would like to hone your 

knowledge across methods and best practices, this workshop 

is for you! 

What you will learn: 

• The tremendous value of learning from data 

• How to create valuable predictive models for your 

business 

• Best Practices by seeing their flip side: Worst Practices 

Schedule: 


• First AM Break from 10:00-10:15am 

• Second AM Break from 11:15-11:30am 

• Lunch from 12:30-1:15pm 

• First PM Break: 2:00-2:15pm 

• Second PM Break: 3:15-3:30pm 

• Workshops ends at 4:30pm 

A free copy of John Elder’s book Statistical Analysis and 

Data Mining Applications is included. 

Thursday, March 8, 2012 

l Full-Day Workshop 

9:00am-4:30pm 

Business Friendly Data Mining 

with Decision Management 

Instructor: James Taylor, CEO, Decision 

Management Solutions 

How to talk so your business team will listen, how to listen so 

your business team will talk. 

Intended Audience: Analytic, business and IT 

professionals interested in improving their business with 

advanced analytics. 

Attendees receive a free copy each of James Taylor’s 

books “Decision Management Systems: A Practical Guide 

to Business Rules and Predictive Analytics”, and “PMML in 

Action: Unleashing the Power of Open Standards for Data 

Mining and Predictive Analytics”, a course materials book, 

and an official certificate of completion at the conclusion of 

the workshop. 

Workshop Description: There is a real opportunity to 

use predictive analytics to address key business challenges 

like fraud detection, risk management, customer next 

best activity, customer retention, validation, marketing 

segmentation, eligibility and more. Organizations that are 

more sophisticated with analytics are three times more 

likely to be in the top 17% of top business performers*. 

But too often there are gaps in understanding between 

business, IT and analytics teams. Gaps in building business 

understanding lead to analytic models that don’t have 

business value and gaps in implementation lead to models 

that take months or years to adopt or never get adopted at 

all. Wasted effort and “good” models that just sit on the shelf 

gathering dust are the result. 

This one day workshop on business friendly data mining with 

Decision Management will show you how to: 

• Improve business/analytics/IT collaboration and 

understanding 

• Clarify the data mining goals 

• Enable rapid deployment of models in IT/operational 

systems 

James Taylor will cover the principles of Decision 

Management, its application to critical business challenges, 

and the appropriate use of available technology to deliver 

business friendly data mining in your organization. 

You’ll see how identifying and prioritizing the operational 

decisions that drive your organization’s success creates a 

common language and collaboration environment between 

business, IT and the analytics team. You’ll learn how to 

model and understand these decisions and so clarify and 

focus the problem statements that drive your analytic efforts. 

This focus on decisions and the effective use of business rules 

and in-database technology also enables rapid deployment 

of analytic models in operational systems, to automate 

and improve these decisions for sustainable competitive 

advantage. 

Data miners working with the approach have said: 

“Clearly an effective way to get more understanding of a 

problem prior to modeling” 

“Great interaction with the business team.” 

“Much more interactive than our current process to gather 

requirements.” 


Workshops 

Schedule: 






This workshop is focused squarely on solving business 

problems. It is vendor-neutral and is for business, IT and 

analytic professionals responsible for best practices and 

improving the business with analytics 

Friday, March 9: Afternoon & 

Saturday, March 10, 2012 

s Two-Day Workshop 

9:00am-4:30pm 

Net Lift Models: Optimizing the 

Impact of Your Marketing 

Instructor: Kim Larsen, Vice President of Analytical 

Insights, Market Shares Partners 

Intended Audience: Statisticians, business analysts, 

and market researchers who build predictive models for 

marketing and retention campaigns. 

Workshop Description: The true effectiveness of a 

marketing campaign isn’t response rate, it is the incremental 

impact - that is, additional revenue directly attributable 

to the campaign that would not otherwise have been 

generated. Yet traditional targeting criteria are often 

designed to find clients that are interested in the product, 

but would have bought it anyway, whether or not they 

received a promotion. In such cases, the incremental impact 

is insignificant and the marketing dollars could have been 

spent elsewhere. 

Net Lift Models are designed to maximize incremental 

impact by targeting the undecided clients that can be 

motivated by marketing. These “swing customers” are akin 

to the swing states of a political election; data miners could 

learn a lot from political campaigns. 

Beyond targeted marketing, Net Lift methodology delivers 

tremendous performance improvements for deployed churn 

models - retaining “savables” while avoiding the adverse 

“reverse” affects retention outreach triggers for some 

customers - as well as other innovative business applications 

of this advanced analytical method. 

This workshop demonstrates how to build Net Lift Models 

that optimize the incremental impact of marketing 

campaigns, covering the pros and cons of various core 

analytical approaches. 

You will learn how to: 

• Build net lift models that maximize the difference in 

response rates between the clients who receive the offer 

and those that do not (the control group) 

• Identify good incremental lift predictive variables 

• Build net lift models using a variety of techniques 

• Evaluate and deploy net models 

Specific topics covered include: 

• Net lift models versus propensity models 

• Example net lift models in action 

• Comparison of net lift modeling approaches, including 

regression- and non-regression-based methods, and the 

Generalized Naive Bayes Classifier 

Access to working code and real examples. In order to 

illustrate net lift modeling in action and provide options for 

“take-home” usage, the instructor will provide 1) example 

datasets and 2) examples of code implementing incremental 

lift modeling methods, including the following SAS macros: 

INCREMENTAL, INFORMATION, GNBCREG, NWOE (net 

weight of evidence), and NIV (net information value). 

While very advanced attendees may optionally bring their 

own laptop and software to try out net lift modeling during 

the workshop, this concentrate topics course does not 

include enough time for guided hands-on instruction; it is 

not designed or intended as a “hands-on” training program. 

Schedule: 






This workshop is offered in cooperation and special 

arrangement with SAS Institute. 


Keynote Bios 

Piyanka Jain 

CEO, Aryng.com, Former PayPal 

Business Analytics Head 

Piyanka Jain’s interest lies in deriving actionable insights 

from data to enable informed trade-offs and decision 

making. She enjoys problem solving and finds herself 

driven towards empowering business professionals 

to make better data driven business decision through 

Aryng’s “Data to Decisions” framework she teaches. 

With Aryng, she is creating an organization to drive 

business transformation through the power of analytics. 

Before founding Aryng, she was heading the NA Business 

Analytics at PayPal, leading strategic analytics, managing 

and setting agenda for the team, defining strategic 

roadmap to find NA business drivers. At PayPal, she and 

her team have delivered several high impact projects 

including product portfolio analysis, merchant lifecycle 

analysis, Voice of Customer analysis, Next Best Product 

Model for Merchant with $84+ mm revenue impact. 

Prior to this, Piyanka drove direct measurable revenue 

impact of $18 mm through Strategic/Marketing 

analytics in partnership with Adobe Product Marketing 

and Relationship Marketing team. Within Marketing 

Operations and Analytics department, her role was to 

lead the organization into learning more about their 

products and customers through establishing appropriate 

engagement model with BU and rigorous mining of 

data. Within Relationship Marketing, Piyanka and her 

team’s role involved designing and analyzing campaigns, 

creating and executing appropriate segmentation and 

targeting strategy, Fine tuning messaging, creatives and 

offers by Test & Control and improving targeting and 

increasing marketable universe by building response 

models and propensity models. 

Anne Robinson 

Director of Supply Chain Strategy 

& Analytics, Verizon Wireless 

Bio is forthcoming. 

Keynote: Influencers, Skeptics, and Data Geeks 

Keynote: The Five Myths of Predictive Analytics 


Keynote Bios 

Eric Siegel 

Program 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 the engineering school’s award 

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 mid-tier 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 over 20 papers and articles in 

data mining research and computer science education, 

has served on 10 conference programme committees, 

has chaired a AAAI Symposium held at MIT, and is the 

founding chair of Predictive Analytics World. 

Keynote: Persuasion by the Numbers: Optimize 

Marketing Influence by Predicting It 

For more speaker bio’s please visit 

www.predictiveanalyticworld.com/sanfrancisco/2012/speakers.php 


Registration 

www.predictiveanalyticsworld.com/sanfrancisco/register.php 

2 Day Conference Pass 

Monday 3/5 and Tuesday 3/6 

2 Day PAW / TAW Combo Pass 

Monday 3/5 to Wednesday 3/7 

2 Day PAW / 3 Day eMetrics Combo Pass 

Monday 3/5 to Wednesday 3/7 

Social Networking Pass 

Monday 3/5 or Tuesday 3/6 (Access to Exhibit Hall and Keynotes Only) 

Early Bird 

ENDS JAN 20 

Regular 

Price 

ENDS MAR 2 

Onsite Price 

$1,690 $1,890 $2,090 

$2,180 $2,380 $2,580 

$2,540 $2,790 $3,090 

$150 $150 $150 

Exhibit Hall Only Passes $25 $25 $45 

PAW Workshops (see Overview) 

Hands-On Intro to R 

Sunday 3/4 – full day 

Predictive Modeling Methods 

Wednesday 3/7 - full day 

Early Bird 

ENDS JAN 20 

Regular 

Price 

ENDS MAR 2 

Onsite Price 

$895 $995 $995 

$895 $995 $995 

Business Friendly Data Mining 

Thursday 3/8 - full day 

$895 $995 $995 


Friday 3/9 – Saturday 3/10 (2 day) 

eMetrics Marketing Optimization Summit Workshops 

These workshops, co-located with PAW, cover hot topics in web analytics 

and online marketing optimization, but may not include content on 

predictive analytics. You may register for these workshops as part of the PAW 

registration process. 

$1,295 $1,495 $1,495 

Early Bird 

ENDS JAN 20 

Regular 

Price 

ENDS MAR 2 

Onsite Price 

Introduction to Web Analytics 101 $850 $900 $1,000 

Applying Web Analytics 102 $850 $900 $1,000 

Pimp Your Reports $850 $900 $1,000 

Roadmap to Online: Analytics Success $850 $900 $1,000 

Google Analytics Users’ Great Event 

This GA users’ conference, co-located with PAW, cover hot topics in web 

analytics, but may not include content on predictive analytics. You may 

register for these workshops as part of the PAW registration process. 

User Conference 

Day 1 - Thursday, March 8, 2012 

Training Workshops 

Day 2 - Friday, March 8, 2012 

Both Days 

March 8 & 9, 2012 

Early Bird 

ENDS JAN 20 

Regular 

Price 

ENDS MAR 2 

Onsite Price 

$445 $495 $595 

$445 $495 $595 

$795 $895 $995 

Includes breakfasts, lunches, priceless networking during coffee breaks, the PAW Reception, and full access to program sessions and sponsor expositions. 

Bring the Team Offer 

Each additional attendee from the same company registered at the same time receives an extra $200 off the Two Day Pass. 


Hotel & Location 

Hotel Rooms and Location: 


San Francisco Marriott Marquis 

55 Fourth Street 

San Francisco, CA 94103 

Reservations: 1-800-228-9290 

Phone: 1-703-845-1010 

Fax: 1-703-820-6425 

There are a limited number of rooms at the San 

Francisco Marriott Marquis which can be booked at 

the Predictive Analytics World Group Rate of $249 

plus taxes for a single or double room. To guarantee 

the group rate, reservations must be completed by 

February 13. Reservations can be made by calling 800- 

228-9290 and referencing Data Driven Business Week. 

Reservations must be received on or before 

February 13th, 2012 to guarantee the group rate. 

Hotel Highlights 

Rising 39 stories high in the skyline, the magnificent 

Marriott Marquis San Francisco Hotel exudes an 

essence of modern luxury and the convenience of an 

extraordinary downtown San Francisco hotel. Just 

south of Market Street, this San Francisco, California 

hotel next to the Moscone Convention Center is 

steps away from the Yerba Buena Gardens, renowned 

museums and cultural attractions, world-class shopping 

on Union Square, and AT&T Park, home of the 

San Francisco Giants. Enjoy inspiring penthouse views 

and cocktails at The View Lounge or the finest contemporary 

cuisine at Mission Grille before retiring to 

luxuriously appointed San Francisco hotel rooms and 

suites. Renowned as a meeting hotel in San Francisco, 

California, this business hotel provides flexible and 

expansive event venues to accommodate both grand 

and intimate San Francisco hotel receptions. Whether 

in town for business or pleasure, experience San Francisco 

at the Marriott Marquis hotel in San Francisco, 

CA where you’re sure to leave your heart.

CONFERENCE PREVIEW - Predictive Analytics World

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