Sessions

Keynote Abstracts

Abstracts for the Analytics 2013 keynote speakers will be available shortly. In the meantime, you can read through the keynote abstracts or watch select presentations from Analytics 2012.

Session Abstracts

The following sessions will take place at Analytics 2013. This page is updated often so please check back frequently for the most up-to-date information. Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime that appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analytics offers new insights in the propagation of fraud through a network. Indeed, fraud is usually not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real time when certain processes show some characteristics of irregular activities. Although many analyses typically focus in the first place on fraud detection, the emphasis should shift toward fraud prevention - detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environments and surrounding effects. With advances in big data, artificial intelligence and increased metric captures of everything we do, analytics will go through a radical transformation in the next few decades. As a result, there will be a shift from analytics simply influencing business decision makers to analytics actually owning the decisions. This transformation is already happening in the pricing community, but expect it to expand to even CFO-level decisions. The emergence of big data analytics has generated a lot of interest in the quantitative analysis of unstructured text data. Customer comments, news stories, industry report segments, tweets and email messages are now routinely analyzed by text mining software solutions such as SAS® Text Miner. Latent semantic analysis, a text analytic framework for extracting conceptual dimensions, offers solutions for analytic needs, including include document summarization and incorporation of unstructured text into quantitative predictive modeling. This presentation addresses the problem of latent semantic dimensionality selection. From simple visual examination of eigenvalue scree plots, to the implementation of an algorithm for multiple elbow point detection, the presentation will cover the detection of multiple dimensionalities in textual data. A number of illustration examples will show how document collections - including responses to open-ended surveys, customer comments and industry report paragraphs - can be examined at alternative levels of semantic abstraction that represent topics, megatopics and microtopics. Speech or voice analytics is an emerging technology that is gaining the interest of contact center operators, as it provides insights into a previously untapped, yet massive, source of information, which is the true "voice of the customer," i.e., the recorded phone calls. The potential range of applications of such technology is impressive: QA automation, 100 percent compliance adherence, call center metric (CSAT, AHT, Conversion, etc.) optimization through driver discovery, desktop performance optimization through targeted agent coaching, call model assessment, and predictive modeling. In this paper, we focus on the speech analytics technology application to improve the lift of propensity-to-pay models in an industrial setting by incorporating dynamic payment behavior indicators into existing static models. This approach is similar to using other types of unstructured data, yet quite different, in the sense that no text mining is performed on call recording transcriptions. The indicators are derived by the speech analytics tool by analyzing combinations of phrases and behaviors without the use of transcription. This proposed solution optimizes call center costs while concentrating agents' efforts on the most lucrative accounts and addresses the challenge of static treatment plans by listening to customer responses. Results pertaining to expected lift are provided. The last decade has seen unprecedented growth in the space of online advertising and digital media marketing. The new wave of social media (Facebook, Twitter, etc.) is making it easier than ever for marketers to reach the right customers at the right time with the right products and offers. However, the marketers, online advertising platforms and other stakeholders need to be equipped with suitable analytical tools and methodologies to maximize the potential of online and digital media. The traditional analytical tools are often insufficient due to the rapidly growing volumes of data as well as the increasing importance of dealing with textual and unstructured data. In this talk, we will present three case studies on applying data analytics in social media and online display advertising to help our clients stay competitive in the marketplace.

Agenda

The full Analytics 2013 agenda will be available in August. Below is an outline of the agenda.

Monday, October 21

7:30 a.m.	Registration Open; Breakfast in Exhibit Hall
8:30 - 8:45 a.m.	Welcome from Conference Co-Chairs
8:45 - 9:45 a.m.	General Session Keynote
9:45 - 10:00 a.m.	Break; Exhibit Hall Open
10:00 - 11:00 a.m.	General Session Keynote
11:00 - 11:30	Break; Exhibit Hall Open
11:30 a.m. - 12:30 p.m.	Featured Speaker	Topic-specific Breakout Sessions
12:30 - 1:45 p.m.	Lunch
1:45 - 2:45 p.m.	Featured Speaker	Topic-specific Breakout Sessions
2:45 - 3:00 p.m.	Break; Exhibit Hall Open
3:00 - 4:00 p.m.	Featured Speaker	Topic-specific Breakout Sessions
4:00 - 4:30 p.m.	Break; Exhibit Hall Open
4:30 - 5:30 p.m.	Featured Speaker	Topic-specific Breakout Sessions
5:45 - 7:15 p.m.	Conference Reception in Exhibit Hall

Tuesday, October 22

7:30 a.m.	Registration Open; Breakfast in Exhibit Hall
8:15 - 8:45 a.m.	Welcome from Conference Co-Chairs
8:45 - 9:45 a.m.	General Session Keynote
9:45 - 10:00 a.m.	Break; Exhibit Hall Open
10:00 - 11:00 a.m.	General Session Keynote
11:00 - 11:30	Break; Exhibit Hall Open
11:30 a.m. - 12:30 p.m.	Featured Speaker	Topic-specific Breakout Sessions
12:30 - 1:45 p.m.	Lunch; Roundtable Discussions
1:45 - 2:45 p.m.	Featured Speaker	Topic-specific Breakout Sessions
2:45 - 3:00 p.m.	Break; Exhibit Hall Open
3:00 - 4:00 p.m.	Featured Speaker	Topic-specific Breakout Sessions
4:00 - 4:30 p.m.	Break; Exhibit Hall Open
4:30 - 5:30 p.m.	Featured Speaker	Topic-specific Breakout Sessions

*Agenda is subject to change

Posters

Big data is creating big demand for analytics professionals. Here's your chance to be recognized by the analytics community for your work. Poster presentations provide an excellent opportunity for analytics practitioners to present their projects in a one-to-one setting and interact with other professionals in the field.

Educating and training the workforce is vital to meeting the demand for those skilled in analytics. Students are strongly encouraged to submit poster abstracts and to participate in the Student Poster Contest. Analytics 2013 provides the opportunity for students to showcase their work and receive feedback from analytics leaders.

The Analytics 2013 Poster Session is open to all analytics practitioners - from corporate or academic fields.

View the list of posters presented at Analytics 2012.

General Poster Submission Guidelines

• To participate, you must submit a poster abstract no later than September 6, 2013.
  • Abstracts must be 250 words or less and should include a description of how you have used analytics to improve your processes and/or analyze your work.
  • You will be notified if your submission has been accepted by September 10, 2013.
• Your poster should include your problem/research goal and show the application of analytics methodology.
  • You must be able to document the steps and show your results.
• The content of the poster must be either a business application, a class assignment (non-research), or a research project.
• SAS will provide a display board and a header denoting the poster title and author.
  • SAS will print the header and poster for display at the conference.
  • Further instructions and specifications for poster presentations will be provided when your abstract is accepted.
• Poster presentations accepted from academia (faculty and full-time students) will allow the primary presenter to attend the conference free. You must be currently enrolled in or employed by an accredited university or college to be eligible for the free conference registration. (You will be required to fax a letter with your department head's signature as verification of your affiliation.)
• Poster presentations accepted from the business world will allow the primary presenter to attend the conference at the early bird rate ($500 off the regular fee).

Student Poster Contest

The top six student poster submissions will be selected as winners of the Analytics 2013 Student Poster Contest. Winners receive a free trip to Analytics 2013 to present their research! The award includes airfare, hotel, meals and free conference registration. You must be a full-time student at an accredited university or college to be considered. To participate in the Student Poster Contest, abstracts must be received by September 6, 2013 and final posters must be received for judging by September 15, 2013. Read our official contest guidelines for more information.

Student Poster Contest Submission Guidelines

• For students participating in the contest, an abstract must be submitted no later than September 6, 2013. You will be notified if your submission has been accepted by September 10, 2013.
• If your submission is accepted, you must submit your completed poster no later than September 15, 2013 for judging.
• Posters will be judged by a committee, and applicants will be notified by September 19, 2013.

Successful Poster Presentation Tips

Poster Presentation Template (.zip)

Questions? Contact us.

Special Events

Roundtables

Numerous roundtable discussions will take place on Tuesday during lunch. A wide variety of topics will be discussed. Take advantage of this opportunity to talk to an expert in a small setting.

Exhibit Hall

Meet representatives from top technology companies to learn about the latest products and services that can move your organization forward in the world of analytics. Enjoy complimentary breakfasts and morning and afternoon breaks, located in the Exhibit Hall. An Internet café will also be available. View a listing of current exhibitors and sponsors or become an exhibitor or sponsor of Analytics 2013.

Poster Session

Be sure to visit the poster session inside the exhibit hall to view the innovative ways others are using analytics to solve real-world problems. Submit an abstract for a poster presentation of your own.

Bookstore

While visiting the Exhibit Hall, stop by the bookstore to browse the latest titles from SAS Press. Books topics include data mining, SAS software and more. Attendees will receive a 20% discount on all purchases!

Monday Evening Reception

Don't miss out on the networking event of the year. The Networking Reception will take place in the Exhibit Hall and is the perfect opportunity to socialize with attendees, exhibitors, conference presenters and staff. A delicious array of appetizers and beverages will be served.

Demo Theater

Located in the Exhibit Hall, the Demo Theater is a great way to learn more about products and solutions offered by SAS and Analytics 2013 sponsors. The Demo Theater agenda will include several short technical presentations. These presentations will take place during conference breaks.

Seventh Annual Analytics and Data Mining Shootout, Presented by SAS, Teradata, and The Institute for Health and Business Insight

Listen to presentations from the winners of the Data Mining Shootout in which students competed to solve a complex predictive modeling case study. Interested in competing in the Data Mining Shootout? Registration is open until May 1, 2013.

Roundtable Discussions

The following roundtable discussions will take place on Tuesday, Oct. 9 during lunch. Additional topics will be added weekly. No pre-registration is required to participate.

• Information Management in Big Data Analytics Platform
  – Bheeshma Tumati, Deloitte
• What's "in" to Managing Big Data Analytics: the Impact of In-Database and In-Memory for the SAS User
  – Tho Nguyen, Teradata
• The Analytic Challenges of Unstructured Data
  – Chris Twogood, Teradata
• Considerations for Determining Analytic Architecture(s) within your Organization
  – Mike Rote, Teradata
• High Performance Analytics Infrastructure for Structured and Unstructured Data, Q&A
  – Rohit Valia, Platform Computing/IBM
• Enabling the future of your enterprise with SAS Grid Manager
  – Adam Diaz, Platform Computing/IBM
• Engineering Analytics
  – Frank Payne, PQC International, Inc.
• Capital Measurement and Business Use Under Basel III
  – Dave Morgan, Kiwibank
• Analytic Training and Development
  – Gene Grabowski, SAS
• Marketing Analytics
  – Wouter Buckinx, Python Predictions
• New Risk Modeling
  – Glenn Bailey, Manheim Auctions
• Analytics in Operations/Supply Chain
  – Sudip Bhattacharjee, University of Connecticut
• Customer Analytics
  – Paul Grasso, Chico's FAS, Inc.
• Model Management and Validation
  – Wayne Thompson, SAS
• Optimization
  – Ivan Oliveira, SAS
• SAS in Financial Services
  – Dudley Gwaltney, SunTrust Bank
• Data Quality for Analytics
  – Gerhard Svolba, SAS
• Social Media Analysis and Beyond
  – Tom Reamy, KAPS Group, LLC
• Customer Segmentation
  – Goutam Chakraborty, Oklahoma State University
• Discovering, Characterizing and Predicting Fraudulent Motor Vehicle Insurance Claims Using SAS Analytics
  – Mark Schneider and Chase Zieman, Louisiana State University
• A Combination of Methodologies in Credit Scoring: Using Logistic Regression and Cluster Analysis to Improve Profitability
  –Chris Cusumano, Kennesaw State University
• Multistep Sales Forecasting in the Automotive Industry Based on Structural Relationship Identification
  – Akkarapol Sa-ngasoongsong, Satish T.S. Bukkapatnam, Jaebeom Kim, Parameshwaren S. Iyer and R.P. Suresh, Oklahoma State University
• P&C Underwriting Economic Capital Model
  – Alan Kessler, Research and Development Center, University of Illinois
• Analytics in Profiling Winning NBA Teams
  – Michelle Mancenido, Arizona State University
• Should We Audit or Not? Predicting Assessments at the IRS
  – Matthew Lawrence, University of Alabama
• Effective Analytics
  – Emmett Cox, BBVA Compass
• The Value of Deep Data
  – Denis Cremisio, Epsilon
• SAS and Hadoop
  – Alex Infanzon, Greenplum, A Division of EMC
• The Commoditization of Models
  – Matthias Kehder, Modern Analytics
• Predicting Risks: Utilizing Subject Matter Knowledge, Factor Analysis and Clustering to Estimate Environmental Hazards
  – Don Monson, Deloitte
• Human Capital Analytics
  – Gene Pease, Capital Analytics
• Student Programs at SAS
  – Julie Petlick, SAS
• Social Media Resources for Education
  – Mantosh Sarkar, Oklahoma State University
• Monetizing Big Data
  – Christopher Stephens, Greenplum, A Division of EMC
• Modeling in Property and Causality Insurance
  – Frank Travisano, Chubb Insurance
• Business Value of Analytics and Its Delivery
  – Robert Woodruff, SAS
• Consumer Marketing Data
  – Peter Zajonc, Epsilon
• Maximizing Efficiency and Performance of SAS with Netezza
  – Tracy Zerbin, IBM