Conference Day 1: Monday, October 1, 2012 |
Registration & Breakfast
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9:00-9:45am
Keynote
Case Study: LinkedIn and Accretive Health
Beyond Big Data: Better Living Through Data Science
The 'big data' theme is overrated and can be misleading. Ultimately data and data analytics cannot get you completely over the finish line; you also need a combination of asking the right questions, context/product intuition, and in some cases an understanding of the psychology behind decision-making. This end-to-end ownership and expertise are the role of the data scientist, and help your big/huge/fat data achieve the inflection point that leads to big insights. Using lessons from consumer internet (LinkedIn and online advertising), health care analytics (Accretive Health) and behavioral economics, we will discuss examples of how the combination of data science and different representations of 'big data' generate insights that help people make better decisions about their lives.
Speaker: Scott Nicholson, Chief Data Scientist, Accretive Health (formerly of LinkedIn)
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9:45-10:05amGold Sponsor Presentation
Raising the Bar for Predictive Analytics Deployment: The Newest Techniques
Although the use of predictive analytics has come a long way in recent years, it is clear that there are now much higher expectations for wider and more accurate deployment methods. So while more organizations see the value of analytics, few are comfortable with their current tools and abilities to create and deploy useful solutions. In this session we'll explore the very newest techniques and capabilities that have emerged to help you ingrain predictive analytics into the DNA of your organization, and deploy solutions that empower your team to make the right decisions and consistently deliver the best results.
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Gold Sponsor Presentation
Modeling Renewal Probability In Order to Boost It at Tripadvisor
For any subscription–based business, there is the danger that the subscription will not be renewed at the end of its term. Modeling the probability of renewal is a necessary, but not sufficient first step towards improving the likelihood of renewal. At Tripadvisor for Business, we use the rate of change in renewal probability as model parameters are varied to identify subscriptions that will benefit most from a marketing intervention.
Speaker: Michael Berry, Analytics Director, TripAdvisor
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Breaks / Exhibits
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Track 1: Thought Leadership
The Practical Data Scientist
The term Data Science has been derided as hype wrapped around the traditional role of the analyst. But if we look at the type of data we have today, the software we use to manipulate it, and the wide range of statistics, machine learning, and analytics tools at our disposal, it is fair to say that we are in a new world. Based on more than 20 interviews with top data scientists and a variety of other research, Dan Woods, CTO and Editor of CITOResearch.com and a contributor to Forbes.com, defines a practical approach to data science, one focused on experimentation, gradual development of skills, and achieving business value. Woods defines a reference model for data science, explores various types of maturity models, suggests organizational structures, and reviews technology that allows for a quick start without a large budget.
10:40-11:25am
Track 2: HR Analytics
Case Study: U.S. Special Forces
Hiring and Selecting Key Personnel Using Predictive Analytics
Hiring and selection of personnel in specialized work environments incurs huge direct and opportunity costs for organizations. One of the largest challenges is that the selection process is often left in the hands of those with either high experience in the domain area but little experience in selection or vice versa.
Predictive Analytics and statistics can play a critical role in formalizing and automating much of the selection process. This session provides an overview of the selection processes using both measures of skills and psychological measures to quantify IQ, domain knowledge, grit, and determination. Examples will be drawn from hiring practices for Special Forces (such as Army Rangers and Navy SEALs) and predictive analytics teams.
Speaker: Dean Abbott, President, Abbott Analytics, Inc.
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11:30am-12:15pm
Keynote
Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation's NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
We now live in a sea of tweets, posts, blogs, and updates coming from a significant fraction of the people in the connected world. Our personal and professional relationships are now made up as much of texts, emails, phone calls, photos, videos, documents, slides, and game play as by face-to-face interactions. Social media can be a bewildering stream of comments, a daunting fire hose of content. With better tools and a few key concepts from the social sciences, the social media swarm of favorites, comments, tags, likes, ratings, and links can be brought into clearer focus to reveal key people, topics and sub-communities. As more social interactions move through machine-readable data sets new insights and illustrations of human relationships and organizations become possible. But new forms of data require new tools to collect, analyze, and communicate insights.
Speaker: Marc Smith, Chief Social Scientist, Connected Action Consulting Group
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Platinum Sponsor Presentation
Managing Forward: Analytics For Today's Multi-Channel, Multi-Device Consumer
When done right, customer satisfaction measurement can yield more than just insights into how well your company, brand, or channel (e.g., web, mobile, store) is performing today. It can also predict the likelihood of customers to engage in critical future behaviors. However, not all methodologies are created equal. They must answer three essential questions of management while demonstrating success not only in theory but in the marketplace.
Speaker: Eric Feinberg, Senior Director of Mobile, Media and Entertainment, ForeSee
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Lightning Round of 2-Minute Sponsor Presentations
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Lunch / Exhibits
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Keynote
Case Study: Verizon
Influencers, Skeptics, and Data Geeks: Using Analytics to Drive Organizational Change
"What gets measured gets done" often is true when it comes to tactical execution. When applied to large-scale strategy, however, the implications of this adage are even more significant.
Advanced analytics can help reveal the true performance drivers in an organization. By leveraging the power of analytics in combination with the principles of change management, learn how to effectively lead your organization into a new era of operational success.
Speaker: Anne G. Robinson, Dir. of Supply Chain Strategy & Analytics, Verizon Wireless
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Platinum Sponsor Presentation
How Big Data Delivers a Competitive Advantage
There's a lot of discussion as to whether Big Data can live up to its hype. Apply it to a businesses' front line – sales - and the benefits become tangible.
Personalized and predictive selling is one of Big Data's more interesting applications. From Amazon's Recommendations to Google's targeted ads, we are all being touched by Big Data whether or not we know or like it. While the power of Big Data is recognizable on the retail side, what is less visible is its application to B2B sales. From a sales rep's perspective, Big Data answers a major question: how do I find the customers who are most receptive to my product or service at a given time? The best reps already have a talent for this. Big Data democratizes this excellence by automating the skills of excellent reps by delivering insight out of massive amounts of internal, external and social data.
This session will discuss the five steps to generate insight from Big Data, using customer case studies as an example of how to apply predictive analytics to selling complex product and service offerings to gain competitive advantage.
Speaker: Kent McCormick, Ph.D., President & CTO, Lattice Engines
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Track 1: Big DataBig Data and Big Analytics Trends: The Promise and the Hype
We will look at the current trends and buzzwords in Big Data, Data Mining and Predictive analytics field and examine how much hype and reality is in the promise of Big Data. We also analyze the growing and changing demand for data scientist skills and see which skills are the hottest.
Speaker: Gregory Piatetsky-Shapiro, Editor, KDNuggets
3:00-3:45pm
Track 2: Market Mix OptimizationCase Study: Penske
Marketing Mix Optimization: Forecasting and decision making under uncertainty
Most marketing mix solutions either fall short either due to reliance on imperfect information or failing to take into account management assessments and business uncertainties. We have developed a novel analytic approach based on a mix of Bayesian statistics and our own proprietary media tools, which help us to predict and optimize the offline and online media. Key features include Monte Carlo scenario analysis, forecasting under uncertainty (constrained media, lack of attribution data etc.), quantifying short and long-term channel effects. Using a client case study, we will highlight our approach and a framework to make timely and effective business decisions.
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Track 1: Predictive Project Management
Case Study: Tangent Design Engineering
Applying Predictive Analytics to Improve Project Management
Project managers today are struggling to keep control over multiple projects in today's complex work environment. Almost all projects are in a state of constant change and managers are tasked with making continual adjustments to reflect competing priorities, scheduling changes, and resource allocation. It's estimated that even small teams will require billions of calculations each month to accurately adjust their project schedules. Businesses today need predictive project management solutions that use advanced statistical algorithms to automatically adjust and accurately predict project schedules to positively impact the bottom line. Tangent Design Engineering is a great example.
3:50-4:10pm
Track 2: Self Updating ModelsCase Study: Ace Cash Express
Data Driven Modeling
Models break down over a period - we call this stability of the model. The more the modeler gets creative, the faster the model breaks-down. In this presentation, you will learn:
- The techniques and the benefits of building a self-updating model, so you never have to worry about models collecting dust
- Ways to regulate an automated model, so human control is not lost
- Methods you can employ in reporting against ever-changing model
Speaker: Senthil Ramanath, Head of Analytics, Ace Cash Express
Track 1: Marketing Lab
Case Study: Predicting Sales through Brand Research Data
Closing the Chasm Between Marketing and Sales
For marketing managers, consumer insight is what helps build brands. The brand's Key Performance Indicators (KPIs) are the measures that they live by. Every brand deploys extensive tracking of these brand performance measures and consumer sentiment across categories and markets at regular intervals.
However, in today's challenging economic environment, spends on research are being put under the scanner, and the ROI on such initiatives is often questioned. Increasingly, marketing managers are being challenged by their sales and operations planning counterparts on the efficacy of such measures and their relevance to sales. Marketing Mix Modeling and other advertising testing models explain the effects of promotions on sales, but they don't establish the connect with the brand's KPIs. This puts added pressure on marketers with their decision to conduct research for what in effect is seen as a status check on brands with no direct implications on business.
Through our case study on a global beverage major, we will showcase how consumer tracking research can predict total brand sales potential and provide foresight to the sales organization. We will be taking this initiative on behalf of the marketing fraternity.
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4:10-4:35pm
Break / Exhibits
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Track 2: Healthcare AnalyticsCase 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
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Track 1: Economic Research and ForecastingCase Study: Rebellion Research
Applying Machine Learning to Global Economics
Global Economic research is provided by the biggest banks and ratings institutions, yet the predictive powers of these firms is notably and infamously low. Bloomberg reported that major Wall Street research is typically 46% accurate. However, this is a field that deals with an extremely large and accessible data set. One that is ideal for machine learning. Yet, there are no machine learning powered research or ratings firms on Wall Street. Robotic ratings and research firms could dominate this field, yet are not even in existence today.
Speaker: Alexander Fleiss, Chairman, Rebellion Research
5:25-6:10pm
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.
Reception / Exhibits
Boston Predictive Analytics MeetUp
Lightning Talks: "Data Deluge!, U.S. Jobs Outlook, D3 DataViz, R Data Mining, Random Forests"
The goal of the Meetup group is to help the local community further it's understanding and proficiency regarding Predictive Analytics through informative lectures, hands-on tutorials, and networking events. Our group has three main focal points: business applications, advanced mathematics, and computer science. Past events have included sentiment analysis, web content recommendations, social media and network analysis; as well as several events pertaining to the Big Data / Hadoop ecosystem.
Boston's Meetup Community: John Verostek
Tapping the Data Deluge!: Jeffrey Breen
U.S. Jobs Outlook: John Muller
Data Visualizations using D3: Lynn Cherny
Data Mining with R / Rattle: David Weisman
Random Forests Case Study: Dan Gerlanc
www.meetup.com/Boston-Predictive-Analytics
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Conference Day 2: Tuesday, October 2, 2012 |
Registration & Breakfast
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Keynote
Putting IBM Watson to Work
IBM's Watson captured the imagination of over 34 million viewers when it beat the all time champions of the US game show, Jeopardy!. To do so, it navigated the complexities of human speech, churned through 200 million pages of unstructured data in under 3 seconds, delivered a confidence based response, all while learning and getting smarter with each outcome. But as impressive as this accomplishment was, it was only the beginning. IBM is working with leading organizations across industries to put Watson to work. The possibilities are endless! Join Bob Jewell, Director of Business Development and Partnerships for IBM Watson Solutions, in an engaging discussion of how IBM Watson can fundamentally transform the way businesses and individuals make decisions and how next generation systems will be designed.
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Platinum Sponsor Presentation
Forensic Analytics – Discover insights that can help you move forward
Internal audit, compliance groups and the like are increasingly being asked to do more work with less resources. In the world of forensic analytics we are utilizing advanced analytic techniques to help those charged with the tasks of investigating fraud, waste, abuse and corruption combat their resource constraints. Forensic Analytics applies a variety of techniques and methodologies to transform disparate data sources into forensic insights for timely action. But what happens when the fraud or corruption has already occurred? Utilizing predictive modeling to gain forensic insights into the vast amount of data can yield promising results, especially when looking at damages. This presentation explores the use of predictive modeling to look at what could have been, had unfair lending practices at a large financial institution not occurred.
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Breaks / Exhibits
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Expert Panel
Big Data for Predictive Analytics
If Big Data begs the question, "What to do with all this data?" predictive analytics answers, "Learn from it to predict behavior." But just how much predictive payoff comes with going so big? This expert panel will address the new demands on predictive analytics solutions and best practices as data grows to enormity, and will recommend tactics to fully leverage data's growing magnitude to improve the business performance of predictive analytics initiatives.
Expert Panelists: Satish Lalchand, Director, Deloitte Financial Advisory Services LLP
Jason Verlen, Director, SPSS Product Strategy & Management, IBM Software Group
Eric Feinberg, Senior Director of Mobile, Media and Entertainment, ForeSee
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Gold Sponsor Presentation
Data – Hurdle or Springboard? How to leverage data to transform your business?
Organizations look at data as a requirement to solve business problems on hand and often consider lack of data as a hurdle. Their data strategy is primarily driven by the business problem that they need to solve. (Problem driven approach: Business problem -> Hypothesis formation -> Data collection -> Hypothesis testing). The lack of data to answer key business questions, along with advances in technology have always served as a springboard for new data e.g., RFID, telematics, gene sequencing etc. Recently, analytically savvy organizations have discovered that new data sources could be used in more innovative ways than to just solve the problem in hand. This has led to a new paradigm in data strategy where the data story starts with the data in hand and not from the business problem (Discovery driven approach: Agenda less observations -> Pattern anticipation -> Data identification and collection -> Hypothesis testing -> Business Opportunity). The presentation focuses on how analytically savvy organizations incorporate both paradigms to leverage data in innovative ways to create game changing opportunities.
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Sponsored Lab
Lab Session: Live Topical Demo
Building Customer Relationships Through IBM SPSS Predictive Analytics
Building life-long customer relationships is a key goal for ING U.S. Financial Services. To build stronger customer relationships, we recently launched a new customer experience program across our U.S. contact centers. At the heart of this program is a predictive analytics capability that is designed to maximize the value of customer touchpoints. In the first 12 months since launch, predictive analytics has guided more than 300,000 customer interactions and resulted in 24,000 positive customer actions. This presentation will describe how predictive analytics has been used to deliver the right message to the right customer at the right time.
Tom Hamilton, Director, Business Intelligence Competency Center, ING U.S.
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Lunch / Exhibits
Special Plenary Session
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. Yet, our and computer analytic weaknesses are surprisingly severe. To explore how to improve the man/machine partnership, we compare and contrast natural and artificial intelligence, with special attention to the growing realization of how challenging it is to think truly rationally.
Speaker: John Elder, CEO & Founder, Elder Research, Inc.
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Gold Sponsor Presentation
"Quest for Big Insight"- Path to Actionable Insights from the Data Deluge
All firms today seek to leverage all of their data to make fact-based actionable business decisions. But how many are truly getting the ROI they want or the insights they need?
Technology has made it possible to harness a wealth of transactional data and even as industry was coming to terms it, the gamut of Big Data has opened up a virtual deluge, pun intended.
Insights however continue to remain the key to creating competitive advantage; in this session, we discuss how companies have leveraged WNS' proprietary analytics decision engine WADE to derive insight from data and drive customized and scalable business solutions enabling fact-based choices.
Speaker: Sanjit Bhoumick, Senior Vice President – Sales, WNS Global Services
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Track 1: Crowdsourcing Data Mining
Machines Learn, But Can They Teach?
In 2012, a British particle physicist, a data analyst for the National Weather Service in Washington, D.C., and a graduate student from Germany (none of whom had a background in education) collaborated on the Kaggle platform to win the $100,000 Hewlett Foundation Automated Student Assessment Prize, a competition to build an innovative algorithm that could score students' essays used in state standardized tests. Software scoring programs do not independently assess the merits of an essay; instead they predict, very accurately, how a person would have scored the essay. This is a critical distinction because it means that the software replicates the same scores as trained educators for significantly less time and money. The winning algorithm outperformed the current state-of-the-art in commercial grading software and achieved the same level of agreement with a trained human grader as two human graders have with each other. Anthony Goldbloom, the founder and CEO of Kaggle, discusses what these results mean for the future of education technology and crowd-sourced competitive analytics.
Speaker: Anthony Goldbloom, Founder & CEO, Kaggle
2:30-3:15pm
Track 2: True Lift Modeling
Case Study: Staples
True-Lift Modeling: Mining for the Most Truly Responsive Customers and Prospects
Stop spending direct marketing dollars on customers who would purchase anyway!
True-lift modeling can identify:
- which customers will purchase without receiving a marketing contact
- which customers need a direct marketing nudge to make a purchase
- which customers have a negative reaction to marketing (and purchase less if contacted)
This discussion will describe:
- the pros and cons of various approaches to true-lift modeling
- metrics for evaluating mdoel performance
- the basic requirements needed to succeed with true-lift modeling
- scenarios where this modeling method is most applicable
See the white paper on this topic
Speaker: Jane Zheng, Chief Scientist, Focus Optimal
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3:15-3:55pm
Breaks / Exhibits
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Track 1: Financial Services
Case Study: Commerzbank, Lloyds,
Ursus Advisors
Optimization in Financial Services
Financial institutions are improving business performance by using optimization techniques in additional to behavioral and statistical modeling. Hear how retail banks are using prescriptive along with predictive analytics to get more customers, get them more profitably, price them appropriately, and retain them strategically.
We will cover the barriers to adoption and introduce an organizational readiness checklist. Attendees should leave ready to kick off the projects internally to implement optimization.
Speaker: Frank Bria, Research Director, Tower Group
4:20-4:40pm
Track 1:
Vendor Recommendations (Beyond Product Recs)
Case Study: Intuit and Mint
Restaurant Recommendation using Financial Likeness
Imagine you are in a new neighborhood and want to discover the 'right' restaurant for 'you'. Instead of reading a host of reviews, we present a recommender system that learns from your historical spend transactions. The system predicts appropriate restaurants from financial transactions of users in Mint, a personal finance tool from Intuit. In contrast to domain specific apps, our recommender can be scaled across different verticals which have financial transaction data from users and hence is more general purpose. We will describe the challenges in using spend data for recommendation, scaling to millions of merchants and transactions with Hadoop and Mahout, and evaluating the performance of the app.
Speaker: Saikat Mukherjee, Data Scientist, Intuit
3:55-4:40pm
Track 2: Detecting Cannibalization
Case Study: TripAdvisor
Cannibalization Analysis Using Matched Pairs at TripAdvisor
In 2010, TripAdvisor launched our Business Listings product that sends travelers directly to hotel web sites, bypassing on-line travel agencies. Is the new product stealing revenue from our traditional CPC business? This case study explains why the question is hard to answer, describes the analytic approach we employed, and reveals the answer to the question.
Speaker: Michael Berry, Business Intelligence Director, TripAdvisor
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4:45-5:30pm
Track 1: Text Mining and Predictive Analytics
Contextually Augmented Predictive Models for Call Center Next Best Action
Using predictive analytics in real time to make call-center agents effective at problem solving, selling, and customer service, is one of the next frontiers for CRM. The Next Best Action for Call Centers solution directly puts predictive analytics in the hands of call-center agents. Over and above historical data based propensity models, we exploit the invaluable data source of agent-customer conversations to gather information about customer context. This helps backend predictive models to better score and rank product offers for selling to customers. This case study shows 25% boost in revenue for a global bank\'s call-center in a cross-sell/up-sell deployment.
Speaker: Shantanu Godbole, Analytics Architect, IBM GPS CRM
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Post-Conference Workshops: Tuesday, October 2, 2012 |
Three hour evening Workshop – 6:30-9:30pm
R Bootcamp: For Newcomers to R
Click here for a detailed workshop description
Instructor: Max Kuhn, Director, Nonclinical Statistics, Pfizer
Post-Conference Workshops: Wednesday, October 3, 2012 |
Full-day Workshop
Modeling Methods: The Best & the Worst of Predictive Analytics: Predictive Modeling Methods & Common Data Mining Mistakes
Click here for a detailed workshop description
Instructor: John Elder, CEO & Founder, Elder Research, Inc.
Full-day Workshop
R for Predictive Modeling: A Hands-On Introduction
Click here for a detailed workshop description
Instructor: Max Kuhn, Director, Nonclinical Statistics, Pfizer
Post-Conference Workshop: Thursday, October 4, 2012 |
Workshop sponsored by:
Full-day Workshop
Advanced Methods Hands-on:
Predictive Modeling Techniques
Click here for a detailed workshop description
Instructor: Dean Abbott, President, Abbott Analytics
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Post-Conference Workshop: Friday, October 5, 2012 |
Full-day Workshop
Making Text Mining Work: Practical Methods and Solutions
Click here for a detailed workshop description
Instructor: Dr. Andrew Fast, Director of Research,
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