October 24-27, 2016
New York
Delivering on the promise of data science

Full Agenda – Boston 2015
All level tracks Blue circle sessions are for All Levels
Red triangle sessions are Expert/Practitioner Level

Conference Day 1: Monday, September 28, 2015

8:00-8:45am • Room: Commonwealth Hall

Registration & Networking Breakfast

8:45-8:50am • Room: Amphitheater

Conference Chair Welcome Remarks

Eric Siegel,
Founding Chair
Predictive Analytics World

8:50-9:40am • Room: Amphitheater

Special Plenary Session
Top Five Technical Tricks to Try when Trapped

There's no better source for tricks of the analytics trade than Dr. John Elder, the established industry leader renowned as an acclaimed training workshop instructor and author -- and well-known for his "Top 10 Data Mining Mistakes" and advanced methods like Target Shuffling. In this special plenary session, Dr. Elder, who is the CEO & Founder of Elder Research, North America's largest pure play consultancy in predictive analytics, will cover his Top Five methods for boosting your practice beyond barriers and gaining stronger results.

Also sign up for Dr. John Elder's one-day workshop, The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes.

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


9:40-10:00am • Room: Amphitheater

Diamond Sponsor Presentation
New Advances in Data Preparation for Advanced Analytics

New self-service data preparation solutions are having a tremendous impact to users of advanced analytics by providing faster, more accurate results. This session will highlight some of the innovative new approaches including how to:

  • Leverage a wider variety of data including multi-structured content (PDF, log files, JSON, etc.) and streaming data
  • Reduce the time required to extract, manipulate, enrich and combine disparate data
  • Automate the preparation and delivery of new data
  • Provide transparency to how data was manipulated and promote reuse

Dan Potter
Chief Marketing Officer
Datawatch Corporation

10:00-10:30am • Room: Commonwealth Hall

Exhibits & Morning Coffee Break

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10:30-11:15am • Room: Harborview 1

All level tracks Track 1: Modeling Methods
Picking the Right Modeling Technique for the problem

Decision Tree? Neural Network? Regression? Naive Bayes? Support Vector Machine? It is said that when your only tool is a hammer, every problem looks like a thumb. Modern data mining toolkits are full of tools, but how do you pick the right tool for a particular predictive analytics task?

Michael Berry
Analytics Director
Tripadvisor for Business

10:30-11:15am • Room: Amphitheater
Track 2: Uplift Modeling
Case Study: Fidelity
Uplift Modeling and Uplift Prescriptive Analytics for Multiple Treatments

Uplift modeling is an emerging subfield of data mining that aims at identifying individuals who are likely to be positively influenced by a treatment and has gained popularity in marketing, political election, and medicine in recent years. Most academic and industrial applications of uplift modeling address the situation of a single treatment (versus a control group). However, business and medical applications often involve more than one treatment. Additionally, there are often budget and quantity constraints involved. This talk will review current uplift modeling methodologies, extend predictive modeling to multiple treatment situations, bridge the gap between predictive analytics and prescriptive analytics by introducing the mathematical problem for treatment optimization, and propose various solutions to both deterministic and stochastic optimization problems. Examples from the retail industry will be used as an illustration.

Victor Lo
Vice President
Fidelity Investments; Bentley University

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10:30-11:15am • Room: Harborview 2

Track 3: Analytics Tactics
Case Study: American Savings Bank
Driving Superior Growth Through Self-Developed Code, Scoring Modeling, and Price Optimization

The banking industry in Hawaii saw a rejuvenating net growth of $21 million in installment loans during 2014-Q4, driven by aggressive campaigns for the holidays.

American Savings Bank (ASB), third player with 12% of the market, contributed $15 million to this growth, surpassing its local competitors and also its peer group in the US.

Herman Jopia
First Vice President and Data Analytics Manager
American Savings Bank

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11:20am-12:05pm • Room: Harborview 1

All level tracks Track 1: Salesforce Analytics
Case Study: EMC
Predicting B2B Sales Success

Business-to-business selling has entered a new level of maturity in the big data era. EMC has tapped into their huge amounts of customer-specific data to begin predicting their next customer and their next upsell opportunity to current accounts. Now their sales team is fully equipped with the right message for the right prospect or customer with results enabling its recent annual revenues of $24.4B, outpacing market growth as an industry leader. Learn how they tackled the problem of disparate data systems from multiple acquisitions to create a holistic, predictive view of their customers.

John Smits
Chief Data Officer, Global Business Operations

11:20am-12:05pm • Room: Amphitheater

Track 2: Advanced Methods
Case Study: Opera Philadelphia
Pricing and Segmentation Utilizing Menu-based Conjoint

At a time when many businesses are searching for analytics to support shift towards subscription-based models, many arts organizations, which historically rely upon season ticket holders, are looking at their business through a different lens and have yet to find a suitable analytical approach that accounts for the unique segments that exist in the market for arts. This presentation covers a quantitative survey of 2,000 individuals utilizing a menu-based conjoint design. The speaker provides an excellent discussion surrounding the integration of traditional segmentation methods (i.e., factor and cluster analysis) with one of today's most progressive pricing analytics techniques.

Lawrence Cowan
Cicero Group

11:20am-12:05pm • Room: Harborview 2

Track 3: Policyholder Acquisition, Risk & Retention
Predictive Analytics in Life Insurance

The data science division at MassMutual Financial Group creates knowledge from data that enables the business to make decisions more effectively and efficiently. In this talk, I will present three applications of predictive analytics that touch on policy-holder acquisition, risk estimation, and retention. Along the way, I will provide details related to methods, evaluation, deployment, and important lessons learned.

Sears Merritt
Vice President, Data Science
MassMutual Financial Group

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12:05-1:30pm • Room: Commonwealth Hall

Lunch in Exhibit Hall

1:30-2:15pm • Room: Amphitheater

Buy or Wait? How the Bunny Predicts When to Buy Your Plane Ticket

Buying a plane ticket is a time-consuming and frustrating process that often leaves the consumer unhappy. Flight prices are less transparent and fluctuate more than almost anything else a consumer buys, even though airfare is one of the most expensive purchases for a typical family. On average, consumers spend almost two weeks comparison shopping but end up spending nearly 5% more than when they started looking. Ironically, all the variability in prices means that there are good deals to be found, but consumers lose out because they're only spot-checking prices occasionally. By watching continuously there's often a 5-10% lower price even within 24 hours, and the potential for further savings by picking the best time to buy.

Our goal at Hopper is to bring more transparency to pricing, by giving consumers advice about where and when to fly -- and when to buy -- to save money on their air travel. We believe this helps consumers buy more quickly, with less effort, and ultimately be happier with their purchase decision. One of our key features is our "when to buy" advice: we'll watch prices for your trip continuously and alert you when we think you should buy. The question is what makes a good deal? If we're too conservative and tell you to buy too early, we risk missing out on a better deal later, but if we're too optimistic and wait too long, you could end up paying more as prices rise towards your departure date. Because prices change in unpredictable ways, at the whim of the airlines, it's impossible to know for sure. But this session will outline the predictive approaches we use to make recommendations that save about 10% on average, and up to 40% in some cases.

Dr. Patrick Surry
Chief Data Scientist

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2:15-2:20pm • Room: Amphitheater
Lightning Round

UCI Extension      Experian   Tata Logo

Tata Logo 2:20-2:35pm • Room: Amphitheater

Gold Sponsor Presentation
Predictive Analytics in the Digital Era

We are in the digital era with data everywhere and in every form beyond just the available transactional data. This explosion of data has offered enterprises with huge opportunities for growth since the ultimate use of data is to learn from it to predict and take informed business decisions. This session will discuss how Predictive Analytics can help organizations drive organizational operations more effectively and how TCS with its plethora of offerings across the Analytics Value Chain catering to various industry verticals is helping its customers do so.

Kumar Amitesh
VP & Head
TCS BPS, North America

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2:40-3:25pm • Room: Harborview 1

All level tracks Track 1: Energy analytics - big data digital oil fields
Case Study: Halliburton
Challenges in Leveraging Predictive Analytics for Big Data in Oil and Gas

Oil and gas involves some of the most complex operations of any industry. During the life cycle of an oil well, the "predict and action" steps have leading or follower roles, vis-a-vis maximizing the benefit from the massive data that is collected. Although the power of predictive analytics is well recognized by the industry, the emergence of digital oil fields generates more data than can be fully leveraged with the technology currently implemented. This talk will address the challenges encountered during operational phases, from exploration to production to completion of an oil well.

Dr. Satyam Priyadarshy
Chief Data Scientist

2:40-3:25pm • Room: Amphitheater

All level tracks Track 2: Uplift Modeling
Case Study: Telenor
Applying Next Generation Uplift Modeling to Optimize Customer Retention Programs

Organizations must constantly work to drive greater retention and revenue whilst spending less money and using fewer resources. In this session, hear how the world's 7th largest mobile operator has applied next generation "uplift" modeling (i.e. "net lift" modeling) to optimize retention programs and seen results 36% better than those possible using traditional analytic practices. Impressively, these results were reached while, at the same time, slashing the cost of retention programs by a staggering 40% – making this an ideal fit for today's recessionary marketing requirements.

Uplift models are different from traditional modeling in that the approach measures and predicts the true incremental impact of marketing activity. Whereas traditional models only aim to predict "behavior", uplift models actually predict the incremental "change in behavior." Telenor's novel approach and application was recently featured in Forrester Research's popular new report "Optimizing Customer Retention Programs", where the approach was shown to achieve an 11-fold increase in campaign ROI when compared with existing programs.

Dr. Patrick Surry
Chief Data Scientist

2:40-3:25pm • Room: Harborview 2

Track 3: Debt Collection Optimization
Case Study: RCS Group (S. African creditor)
Recoveries: External Debt Collection Optimization

The Decision Science Department of RCS Group is innovative in developing integrated predictive models within business processes throughout the credit life cycle. The commercial imperative in the Collections and Recoveries operations is to maximize the recoveries on written-off credit facilities. By engaging predictive analytics and optimization theory we used a two phased approach to development a scientific, data-driven placements solution that assigns written-off accounts (1st placement) to External Debt collectors (EDC). The main objective is to maximize recovery yield by 1) the optimal placement of the number of written-off accounts based on the past performance of the EDC and 2) by profiling accounts according to EDC propensity to recover, leading to a double digit lift in Recovery rate, which drops straight to the company profit line. This session will demonstrate the use of mathematical optimization and goal programming to arrive at a dynamic placement solution that will maximize recovery yields with additional benefits of EDC performance evaluation reports and a variable commission structure.

Werner Britz
Head of Decision Science
RCS Group

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3:25-3:55pm • Room: Commonwealth Hall

Exhibits & Afternoon Break

3:55-4:40pm • Room: Harborview 1

Track 1: Bid Optimization (for construction)
Case Study: A construction contractor
"Confucius says..." - Using Analytics to Predict Construction Bids

Each year, a significant number of public construction contracts are awarded through the competitive bidding process. For most contractors, this represents the proverbial "Catch 22." Bid too high and you lose, bid too low and you jeopardize profit. What if you knew what the competition was going to bid? We developed a model that helps unravel this complex riddle and the results are astonishing. One Contractor increased the number of winning bids to 80% from 58% but more importantly maximized their profit potential for each award. With the help of "Confucius," the company achieved a CAGR of 46%.

Paul Mlakar
Graypoint Industries

3:55-4:40pm • Room: Amphitheater

Track 2: Ad & Marketing Analytics
Case Study: Verizon
Predicting Behaviorial Influence in real-time for Dynamic Offers

Advertisement Targeting today is typically driven by consumer behavior and the traits of the endpoints such as location, proximity, and associated potential to influence the consumer's psychographic profile. How wellcan behavioral influence be predicted and attributed? What are the economics associated with deriving predictive insight? What were our findings regarding buckets of models versus optimized predictive techniques? In this session, learnings from a real-world incubation of a dynamic offer marketing system will be explored.

Madhusudan Raman
Innovation Incubator

3:55-4:40pm • Room: Harborview 2

Track 3: Insurance; Analytics strategy
Case Study: MetLife
Establishing Value: the "Making Impact Through Analytics" Framework

Analytics has recently been treated by many as a silver bullet to solve business issues. However, analytics practitioners such as data scientists and data analysts often times find their analysis do not result in business actions, or when they are implemented, there is no mechanism to reflect the resulted impact. How can we solve this issue and demonstrate the analytics ROI? In this keynote, MetLife's VP Data Science Bin My will present the "Making Impact Through Analytics" framework, which is designed to help analytics practitioners address this issue.

Bin Mu
VP, Business Analytics

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4:45-5:05pm • Room: Harborview 1

All level tracks Track 1: Retail Advertising Analytics
Case Study: Top 500 Internet Retailer in the U.S.
Using Predictive Analytics to Drive Successful Product-Based Marketing in e-Commerce Product Listing Ad Channels

Predictive analytics is used to optimize performance in the fastest-growing digital marketing channel in e-commerce; product listing advertising. Success in PLA channels is largely based on a retailer's ability to assign the right bid to each product in a retailer's catalog at the right time. The Sidecar technology uses predictive analytics to intelligently group products together in bid groups and assign the optimum bid for ads that correlate to a consumer's search query. Using this technology has helped retailers see significant increases in ad impressions and revenue while also experiencing a decrease in cost of sale.

Ryan Williams
Director of Analytics

4:45-5:30pm • Room: Amphitheater

Track 2: Market Mix Modeling
Cross-Platform Media: How to Leverage Household Response Modeling and Track Campaign Measurement

The data generated from integrated marketing efforts has prompted adjustments not only in execution, but also measurement. Cross-channel media campaigns with up to nine different marketing vehicles can be best executed and measured with a regression-based methodology. dunnhumby's innovative collaborations have bridged the gap between single campaign measurement (ANCOVA) and media mix modeling (MMM) to document overall and individual channel impact. This allows retailers and brands to accommodate for the changing marketing space, by measuring the effect of exposures across a combination of platforms, which can help to determine the efficacy of difference media vehicles within a single campaign.

Justin Petty
Vice President, Client Solutions Media and Partnerships

4:45-5:30pm • Room: Harborview 2

Track 3: Forming an Analytics Team
Case Study: Travelers
A Journey along the Predictive Analytics Maturity Curve

There are significant differences between building up a predictive analytics team versus leading one that is well established. In this session we will discuss some of the challenges that evolve as a team moves along the predictive analytics maturity curve.

David McMichael
Second Vice President

5:10-5:30pm • Room: Harborview 1

All level tracks Track 1: Clustering for Marketing and Beyond
Driving Product Improvements and Marketing Efforts through Software Usage Logs

Every company seeks to understand how customers use their products. Companies traditionally rely on surveys, focus groups, and other qualitative research to infer customer behavior. Learn how SolidWorks, a leading provider of 3D computer aided design (CAD) software, is going beyond the traditional approaches by leveraging its vast collection of customer log data to gain insight into how to improve the user experience and to guide effective marketing strategies. Our approach combines clustering analysis with a predictive model to intelligently segment the customers based on the numerous capabilities they use within the product for a dynamic production environment of continuous user feedback.

Rick Chin
Director of Product Innovation

Kimberly Scott
Data Scientist
Elder Research, Inc.

Sponsored By:
Tata Logo

5:30-7:00pm • Room: Commonwealth Hall

Networking Reception

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Conference Day 2: Tueday, September 29, 2015

8:00-8:45am • Room: Commonwealth Hall

Registration & Networking Breakfast

8:45-9:30am • Room: Amphitheater

Case Study: SmarterHQ
The Revolution in Retail Customer Intelligence

In this new era of Big Data, retailers collect data in ever-increasing volume and variety. In the midst of Big Data, a revolution is taking place in how retailers gain insights about customers, whether they interact with the brand online, in stores, or both. This session will describe the transition from reporting to data-driven decisions using predictive analytics. Success requires collecting the right data, creating informative derived attributes, making this data accessible in a timely manner, and building predictive models. Examples, drawn from real-world retailers, will include shopping cart funnel management, shopping cart abandonment, marketing attribution, churn, and purchase propensity.

Also sign up for a one-day workshop by Dean Abbott:
Advanced Methods Hands-on: Predictive Modeling Techniques
Supercharging Prediction: Hands-On with Ensemble Models

Dean Abbott
Co-Founder and Chief Data Scientist

9:30-9:40am • Room: Amphitheater

Plenary Session
Industry Trends: Highlights from the 2015 Data Miner Survey

In the spring of 2015, over a thousand analytic professionals from around the world participated in the 7th Rexer Analytics Data Miner Survey. In this PAW session, Karl Rexer will unveil the highlights of this year's survey results. Highlights will include:

  • key algorithms
  • challenges of Big Data Analytics, and steps being taken to overcome them
  • trends in analytic computing environments & tools
  • analytic project deployment
  • job satisfaction
Karl Rexer
Rexer Analytics

9:40-10:00am • Room: AmphitheaterDatawatch
Diamond Sponsor Presentation
Big Data Analytics with Oracle Advanced Analytics12c and Big Data SQL

Oracle Advanced Analytics 12c delivers parallelized in-database implementations of data mining algorithms and integration with R. Data analysts use Oracle Data Miner GUI and R to build and evaluate predictive models and leverage R packages and graphs. Application developers deploy Oracle Advanced Analytics models using SQL data mining functions and R. Oracle Big Data SQL adds new big data sources. Oracle Big Data Discovery works natively with Hadoop to transform raw data rapidly into business insights.

Charles Berger
Sr. Director, Product Management, Data Mining and Advanced Analytics

Diamond Sponsor Presentation
Dunkin Brands Turn Big Data into Big Advantage Case Study

Dunkin' Brands owns two of the world's most recognized, beloved franchises: Dunkin' Donuts and Baskin-Robbins. This Case Study talks about a Descriptive, Predictive and Prescriptive Analytics approach using Oracle Advanced Analytics to enhance Dunkin's loyalty program. We leverage data from disparate sources to generate a 360 degree view of Dunkin's Loyalty guests, capturing various behaviors and transactional data to build models that drive personalization, up-sell recommendations, coupons and other promotions thorough Dunkin Mobile App..

Charles Berger
Sr. Director, Product Management, Data Mining and Advanced Analytics

Mahesh Jagannath
Senior Manager, Business Intelligence
Dunkin Brands

10:05-10:50am • Room: Harborview 1

All level tracks Track 1: Education Analytics & Personalized Learning
Case Study: McGraw Hill Education
Unlocking the Power of Big Data to Transform Learning

A number of commentators, including Clayton Christensen, have argued that education is prime for disruption. In this session, we discuss how Big Data is being used to transform education. Specifically, we will demonstrate how predictive analytics, artificial intelligence, visualization, and next-generation platform architectures are being applied to personalize learning and to scale systems to support millions of users world-wide.

Alfred Essa
Vice President, R&D and Analytics
McGraw Hill Education

10:05-10:50am • Room: Amphitheater

Track 2: Open Data Sources
The More the Merrier: Leveraging Open Data Sources to Increase Customer Insights Through Predictive Analytics

While organizations have historically looked to customer feedback to drive business decisions, changes in channels and styles of customer communication have increased actionable insights organizations can capture. With the adoption of social media, broader availability of open data and advancements in data science and analytics, organizations now have an unprecedented ability to understand customer needs and avoid complaints. But how? Based on his on-going analysis of open data sources, such as HMDA and the CFPB complaint database, Steven Ramirez will provide insight on the methods businesses should adopt to pro actively operationalize data in order to eliminate complaints and retain customers.

Steven Ramirez
Beyond the Arc

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10:50-11:15am • Room: Commonwealth Hall

Exhibits & Morning Coffee Break

11:15am-12:00pm • Room: Harborview 1

All level tracks Track 1: Workforce Analytics
Case Study: Paychex
Predicting Employee Churn with Anonymity

In the age of Big Data, ethics is becoming increasingly important when predicting behavior responsibly. This is especially true when using predictive analytics to understand the actions of consumers or employees. Join us in reviewing a case study where we apply predictive modeling to increase employee retention, while protecting employees from the invasive big brother perception.

Philip O'Brien
MIS and Portfolio Manager

11:15am-12:00pm • Room: Amphitheater

Track 2: Best Practices
Q&A: Ask Dean and Karl Anything (about Best Practices)

Preeminent consultant, author and instructor Dean Abbott, along with Rexer Analytics president Karl Rexer, field questions from an audience of predictive analytics practitioners about their work, best practices, and other tips and pointers.

Dean Abbott
Co-Founder and Chief Data Scientist

Karl Rexer
Rexer Analytics

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12:00-1:30pm • Room: Commonwealth Hall

Lunch in Exhibit Hall

1:30-2:15pm • Room: Amphitheater

Data Science at The New York Times

The New York Times is a technology company which aims not only to produce great journalism but also to ensure the reach and impact of this journalism. A growing effort within the engineering division is to reframe central business and newsroom goals as machine learning tasks, including prediction tasks. I will give examples of several such machine learning challenges I have addressed in my role as Chief Data Scientist at The New York Times, and lessons learned in building a data-driven culture at a 164-year old content company.

Christopher Wiggins
Chief Data Scientist
The New York Times

2:15-3:00pm • Room: Amphitheater

Expert Panel
Education and Training Options for Predictive Analytics

While it's easy to get excited about entering the field of predictive analytics, it's not so easy to choose from the many educational paths available. And decisions about training and education can be no less challenging to managers, who face many options in how to train existing staff, as well as many factors in how to evaluate the educational background of prospective hires. With forecasts warning of an analytics talent shortage, a lot is at stake for this growing industry. Our expert panelists will explore the educational options, and discuss how they're best navigated.

Session pre-study -- predictive analytics educational sources:
    – PAW's popular, well-received one-day training workshops
    – Complete list of degree programs in analytics, data mining, and data science
    – Predictive Analytics Guide

Thomas Hill, Ph.D.
Executive Director Analytics, Dell Software Group
Dell Statistica Software, Information Management Group

Dave Dimas
Lecturer, Department of Mechanical and Aerospace Engineering Director, Engineering, Sciences and Information Technology Programs
Extension University of California, Irvine

Will Ford
Director of Data Science
Predixion Software

3:00-3:30pm • Room: Commonwealth Hall

Exhibits & Afternoon Break

3:30-4:15pm • Room: Harborview 1

All level tracks Track 1: Analytics for Project Management
Case Study: State Street
Predictive Analytics for Project Management--Cost Avoidance

How can predictive analytics help avoid IT project development costs, show the risk associated with projects, and help determine achievable scope? How can the customer accept the risks associated with such projects when faced with a Federal Reserve mandated deadline? Drawing upon actual projects I'll show how State Street uses a predictive analytics framework consisting of empirical data, statistical analysis, and a data-driven model that understands the behavior of development projects to assess project risk and help the project team and the customer trade off scope, cost, and time to avoid cost overruns while meeting deadlines.

Scott Lancaster
Vice President
State Street Corp.

3:30-4:15pm • Room: Amphitheater

Track 2: Workforce Analytics
Case Study: An Organization's Sales Force
A Transaction-Based Approach to Understand Sales Representative Growth, Performance, and Gaming

In the search for quality sales representatives, it is tempting to think that there is one successful "type" of rep. In fact, the data in this study suggest that there are multiple pathways of sales achievement. In this study we put aside simple aggregations to do it the "hard way" - by modeling low-level sales transactions. Using big data technologies, we analyzed millions of geo-located sales records for thousands of national sales reps. The analysis revealed several well-traveled pathways - some hit the ground running, some were gradual learners but eventually excelled, some gamed the system to win, many others failed.

Pasha Roberts
Co-Founder and Chief Scientist
Talent Analytics, Corp.

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4:20-4:40pm • Room: Harborview 1

Track 1: Experimental Design
Case Study: Perion Networks
Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments

Controlled Experimentation has been universally adopted by the online world as an essential tool in aiding in the decision making process. One of the main challenges involved in setting up an experiment is deciding upon the OEC, or overall evaluation criteria. We demonstrate the importance of choosing a metric that focuses on long term effects. Such metrics include measures such as life-span or lifetime value. We present motivating examples where failure to focus on the long term effect may result in an incorrect conclusion. Finally we present an innovative methodology for early detection of lifetime differences between test groups, a joint work by Boris Rabinovich, Liron Gat-Kahlon, Yoni Schamroth and Prof. David Steinberg.

Boris Rabinovich
Lead Data Scientist
Perion Networks

4:45-5:05pm • Room: Harborview 1

All level tracks Track 1: Analytics for Strategy
Driving Strategy Execution Using Big Data Analytics

Poor strategy execution is the number one challenge facing CEOs and although the balanced scorecard can translate the strategy into operations, companies still struggle to identify reasons for low performance and understand precisely the cause and effect of their strategic assumptions. Now with the advent of big data and analytics, we will soon be able to predict with a high precision why our strategy is not working as planned or whether it's actually the right strategy. Case studies will be presented to illustrate how the concept works in practice. Lessons learnt from process modelling & simulation is discussed.

Mohamed Guidoum
Chief Strategy Officer

4:20-5:05pm • Room: Amphitheater

Track 2: Marketing; Advanced Methods
Case Study: A Financial Creditor
What Landing a Rover on Mars Taught us about Optimization of Marketing Strategies for Loan Portfolios

Analytical Mechanics Associates delivers big data analysis and predictive modeling for NASA to optimize trajectories and fuel consumption during flight. The chief constraint is mission success in all atmospheric and interplanetary conditions. The techniques developed and used by aerospace engineers (a.k.a. rocket scientists) are used by businesses to optimize marketing decisions with the chief constraint of being profitable in all economic and market conditions. In this presentation, AMA personnel will describe the work they do in big data, predictive modeling, simulation and optimization and talk about the similarities between landing a rover on mars and developing profitable marketing strategies for loan portfolios.

Dave Bose
V.P., Modeling and Simulation
Analytical Mechanics Associates, Inc.

5:30-7:30pmDell Logo

Big Data Analytics Meetup: Embed Analytics Everywhere

Redefining the Economics of Analytics
Becoming data-driven requires analytics to be embedded throughout the organization in different functional areas and different operational processes. But how do you provide more and more people with the ability to run any analytics on any data anywhere– without breaking the bank? In this session, you'll see real-world examples of how Dell embeds analytics across processes and operations to drive innovation and redefine the economics of analytics. After a short presentation, we'd like to offer the opportunity to speak one-on-one with subject matter experts and network with peers to gain a better understanding of how embedding analytics enables faster innovation and improves collaboration between data scientists, business analysts, and business stakeholders-- while simultaneously reducing on-going costs.
Click here for more details and to confirm your attendance.

Thomas Hill, Ph.D.
Executive Director Analytics, Dell Software Group
Dell Statistica Software, Information Management Group
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