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To build awareness of Eric Siegel’s new, acclaimed book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (published by Wiley Feb. 19), an offer ya can’t refuse.
Order the book on April 3 via Amazon ($15) for:
1. Free access to the first of 4 modules of the author’s online training program, Predictive Analytics Applied
2. A 35% discount off the full training ($495), or its in-person version, Predictive Analytics for Business, Marketing & Web ($1,495 – Apr 25-26 in NYC)
3. Automatic entrance into a drawing to receive a pass for any Predictive Analytics World this year (San Francisco, Chicago, DC, Boston, London, or Berlin).
Ajay- at $15 a pop, and quite a nice book, it’s a steal! See book review here–
I recently read a review copy of Dr Eric Siegel’s new book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
(Disclaimer-I have interviewed Eric here in September 2009, and we have been in touch over the years as his Predictive Analytics Conference became a blog partner and then a sponsor here at Decisionstats.com PAWCON also took off, becoming the biggest brand in independent analytics conferences)
So it was with a slight note of optimism that I opened this book, and it has so far exceeded my expectations. This is a very lucidly writtern, well explained book that can help people at all levels for analytics. There is a wealth of information here in a wide variety of business domains, and the beautifully designed book also has great tables, examples, and quotes, cartoons to make a very readable case for predictive analytics. Of course, Eric has some views, he loves ensemble modeling, conversion uplift models, privacy concerns, and his background in academics help explain even technical things very elaborately and in an interesting manner.
And at $14,6 it is quite a steal from Amazon. So buy a copy and read it. I would recommend it for helping build a case for predictive analytics , evangelizing to clients, and even to students in grad school programs. This is how analytics books should be , easy to read, and lucid to remember and practical to execute!
Watch out for the twitter hash news on PAWCON and the exciting agenda lined up. If your in the City- you may want to just drop in
Disclaimer- PAWCON has been a blog partner with Decisionstats (since the first PAWCON ). It is vendor neutral and features open source as well proprietary software, as well case studies from academia and Industry for a balanced view.
Little birdie told me some exciting product enhancements may be in the works including a not yet announced R plugin and the latest SAS product using embedded analytics and Dr Elder’s full day data mining workshop.
Monday November 15, 2010
All conference sessions take place in Edward 5-7
Registration, Coffee and Danish
Room: Albert Suites
Five Ways Predictive Analytics Cuts Enterprise Risk
All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.
Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.
In this keynote session, Dr. Eric Siegel will reveal:
- Five ways predictive analytics evolves your enterprise to reduce risk
- Hidden sources of risk across operational functions
- What every business should learn from insurance companies
- How advancements have reversed the very meaning of fraud
- Why “man + machine” teams are greater than the sum of their parts for
enterprise decision support
Platinum Sponsor Presentation
The Analytical Revolution
The algorithms at the heart of predictive analytics have been around for years – in some cases for decades. But now, as we see predictive analytics move to the mainstream and become a competitive necessity for organisations in all industries, the most crucial challenges are to ensure that results can be delivered to where they can make a direct impact on outcomes and business performance, and that the application of analytics can be scaled to the most demanding enterprise requirements.
This session will look at the obstacles to successfully applying analysis at the enterprise level, and how today’s approaches and technologies can enable the true “industrialisation” of predictive analytics.
Speaker: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd
Gold Sponsor Presentation
How Predictive Analytics is Driving Business Value
Organisations are increasingly relying on analytics to make key business decisions. Today, technology advances and the increasing need to realise competitive advantage in the market place are driving predictive analytics from the domain of marketers and tactical one-off exercises to the point where analytics are being embedded within core business processes.
During this session, Richard will share some of the focus areas where Deloitte is driving business transformation through predictive analytics, including Workforce, Brand Equity and Reputational Risk, Customer Insight and Network Analytics.
Speaker: Richard Fayers, Senior Manager, Deloitte Analytical Insight
Break / Exhibits
Room: Albert Suites
Case Study: Life Line Screening
Taking CRM Global Through Predictive Analytics
While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK.
Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc.
Speaker: Trish Mathe, Life Line Screening
Open Source Analytics; Healthcare
Case Study: A large health care organization
The Rise of Open Source Analytics: Lowering Costs While Improving Patient Care
Rapidminer and R were the number 1 and 2 in this years annual KDNuggets data mining tool usage poll, followed by Knime on place 4 and Weka on place 6. So what’s going on here? Are these open source tools really that good or is their popularity strongly correlated with lower acquisition costs alone? This session answers these questions based on a real world case for a large health care organization and explains the risks & benefits of using open source technology. The final part of the session explains how these tools stack up against their traditional, proprietary counterparts.
Speaker: Jos van Dongen, Associate & Principal, DeltIQ Group
Lunch / Exhibits
Room: Albert Suites
Case Study: Yahoo! and other large on-line e-businesses
Search Marketing and Predictive Analytics: SEM, SEO and On-line Marketing Case Studies
Search Engine Marketing is a $15B industry in the U.S. growing to double that number over the next 3 years. Worldwide the SEM market was over $50B in 2010. Not only is this a fast growing area of marketing, but it is one that has significant implications for brand and direct marketing and is undergoing rapid change with emerging channels such as mobile and social. What is unique about this area of marketing is a singularly heavy dependence on analytics:
- Large numbers of variables and options
- Real-time auctions/bids and a need to adjust strategies in real-time
- Difficult optimization problems on allocating spend across a huge number of keywords
- Fast-changing competitive terrain and heavy competition on the obvious channels
- Complicated interactions between various channels and a large choice of search keyword expansion possibilities
- Profitability and ROI analysis that are complex and often challenging
The size of the industry, its growing importance in marketing, its upcoming role in Mobile Advertising, and its uniquely heavy reliance on analytics makes it particularly interesting as an area for predictive analytics applications. In this session, not only will hear about some of the latest strategies and techniques to optimize search, you will hear case studies that illustrate the important role of analytics from industry practitioners.
Speaker: Usama Fayyad, , Ph.D., CEO, Open Insights
Platinum Sponsor Presentation
Creating a Model Factory Using in-Database Analytics
With the ever-increasing number of analytical models required to make fact-based decisions, as well as increasing audit compliance regulations, it is more important than ever that these models can be created, monitored, retuned and deployed as quickly and automatically as possible. This paper, using a case study from a major financial organisation, will show how organisations can build a model factory efficiently using the latest SAS technology that utilizes the power of in-database processing.
Speaker: John Spooner, Analytics Specialist, SAS (UK)
Room: Albert Suites
Case Study: SABMiller
Predictive Analytics & Global Marketing Strategy
Over the last few years SABMiller plc, the second largest brewing company in the world operating in 70 countries, has been systematically segmenting its markets in different countries globally in order optimize their portfolio strategy & align it to their long term country specific growth strategy. This presentation talks about the overall methodology followed and the challenges that had to be overcome both from a technical as well as from a change management stand point in order to successfully implement a standard analytics approach to diverse markets and diverse business positions in a highly global setting.
The session explains how country specific growth strategies were converted to objective variables and consumption occasion segments were created that differentiated the market effectively by their growth potential. In addition to this the presentation will also provide a discussion on issues like:
- The dilemmas of static vs. dynamic solutions and standardization vs. adaptable solutions
- Challenges in acceptability, local capability development, overcoming implementation inertia, cost effectiveness, etc
- The role that business partners at SAB and analytics service partners at AbsolutData together play in providing impactful and actionable solutions
Speaker: Anne Stephens, SABMiller plc
Speaker: Titir Pal, AbsolutData
Case Study: Overtoom Belgium
Increasing Marketing Relevance Through Personalized Targeting
Since many years, Overtoom Belgium – a leading B2B retailer and division of the French Manutan group – focuses on an extensive use of CRM. In this presentation, we demonstrate how Overtoom has integrated Predictive Analytics to optimize customer relationships. In this process, they employ analytics to develop answers to the key question: “which product should we offer to which customer via which channel”. We show how Overtoom gained a 10% revenue increase by replacing the existing segmentation scheme with accurate predictive response models. Additionally, we illustrate how Overtoom succeeds to deliver more relevant communications by offering personalized promotional content to every single customer, and how these personalized offers positively impact Overtoom’s conversion rates.
Speaker: Dr. Geert Verstraeten, Python Predictions
Break / Exhibits
Room: Albert Suites
Case Study: Lloyds TSB General Insurance & US Bank
Uplift Modelling: You Should Not Only Measure But Model Incremental Response
Most marketing analysts understand that measuring the impact of a marketing campaign requires a valid control group so that uplift (incremental response) can be reported. However, it is much less widely understood that the targeting models used almost everywhere do not attempt to optimize that incremental measure. That requires an uplift model.
This session will explain why a switch to uplift modelling is needed, illustrate what can and does go wrong when they are not used and the hugely positive impact they can have when used effectively. It will also discuss a range of approaches to building and assessing uplift models, from simple basic adjustments to existing modelling processes through to full-blown uplift modelling.
The talk will use Lloyds TSB General Insurance & US Bank as a case study and also illustrate real-world results from other companies and sectors.
Speaker: Nicholas Radcliffe, Founder and Director, Stochastic Solutions
Case Study: Canadian Automobile Association and other B2C examples
The Diminishing Marginal Returns of Variable Creation in Predictive Analytics Solutions
Variable Creation is the key to success in any predictive analytics exercise. Many different approaches are adopted during this process, yet there are diminishing marginal returns as the number of variables increase. Our organization conducted a case study on four existing clients to explore this so-called diminishing impact of variable creation on predictive analytics solutions. Existing predictive analytics solutions were built using our traditional variable creation process. Yet, presuming that we could exponentially increase the number of variables, we wanted to determine if this added significant benefit to the existing solution.
Speaker: Richard Boire, BoireFillerGroup
Reception / Exhibits
Room: Albert Suites
Tuesday November 16, 2010
All conference sessions take place in Edward 5-7
Registration, Coffee and Danish
Room: Albert Suites
Multiple Case Studies: Anheuser-Busch, Disney, HP, HSBC, Pfizer, and others
The High ROI of Data Mining for Innovative Organizations
Data mining and advanced analytics can enhance your bottom line in three basic ways, by 1) streamlining a process, 2) eliminating the bad, or 3) highlighting the good. In rare situations, a fourth way – creating something new – is possible. But modern organizations are so effective at their core tasks that data mining usually results in an iterative, rather than transformative, improvement. Still, the impact can be dramatic.
Dr. Elder will share the story (problem, solution, and effect) of nine projects conducted over the last decade for some of America’s most innovative agencies and corporations:
- Cross-selling for HSBC
- Image recognition for Anheuser-Busch
- Biometric identification for Lumidigm (for Disney)
- Optimal decisioning for Peregrine Systems (now part of Hewlett-Packard)
- Quick decisions for the Social Security Administration
- Eliminate Bad:
- Tax fraud detection for the IRS
- Warranty Fraud detection for Hewlett-Packard
- Highlight Good:
- Sector trading for WestWind Foundation
- Drug efficacy discovery for Pharmacia & UpJohn (now Pfizer)
Moderator: Eric Siegel, Program Chair, Predictive Analytics World
Speaker: John Elder, Ph.D., Elder Research, Inc.
Also see Dr. Elder’s full-day workshop
Break / Exhibits
Room: Albert Suites
Case Study: Leading Telecommunications Operator
Predictive Analytics and Efficient Fact-based Marketing
The presentation describes what are the major topics and issues when you introduce predictive analytics and how to build a Fact-Based marketing environment. The introduced tools and methodologies proved to be highly efficient in terms of improving the overall direct marketing activity and customer contact operations for the involved companies. Generally, the introduced approaches have great potential for organizations with large customer bases like Mobile Operators, Internet Giants, Media Companies, or Retail Chains.
Main Introduced Solutions:-Automated Serial Production of Predictive Models for Campaign Targeting-Automated Campaign Measurements and Tracking Solutions-Precise Product Added Value Evaluation.
Speaker: Tamer Keshi, Ph.D., Long-term contractor, T-Mobile
Speaker: Beata Kovacs, International Head of CRM Solutions, Deutsche Telekom
Nine Laws of Data Mining
Data mining is the predictive core of predictive analytics, a business process that finds useful patterns in data through the use of business knowledge. The industry standard CRISP-DM methodology describes the process, but does not explain why the process takes the form that it does. I present nine “laws of data mining”, useful maxims for data miners, with explanations that reveal the reasons behind the surface properties of the data mining process. The nine laws have implications for predictive analytics applications: how and why it works so well, which ambitions could succeed, and which must fail.
Lunch / Exhibits
Room: Albert Suites
Expert Panel: Kaboom! Predictive Analytics Hits the Mainstream
Predictive analytics has taken off, across industry sectors and across applications in marketing, fraud detection, credit scoring and beyond. Where exactly are we in the process of crossing the chasm toward pervasive deployment, and how can we ensure progress keeps up the pace and stays on target?
This expert panel will address:
- How much of predictive analytics’ potential has been fully realized?
- Where are the outstanding opportunities with greatest potential?
- What are the greatest challenges faced by the industry in achieving wide scale adoption?
- How are these challenges best overcome?
Panelist: John Elder, Ph.D., Elder Research, Inc.
Panelist: Colin Shearer, WW Industry Solutions Leader, IBM UK Ltd
Panelist: Udo Sglavo, Global Analytic Solutions Manager, SAS
Panel moderator: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World
Crowdsourcing Data Mining
Case Study: University of Melbourne, Chessmetrics
Prediction Competitions: Far More Than Just a Bit of Fun
Data modelling competitions allow companies and researchers to post a problem and have it scrutinised by the world’s best data scientists. There are an infinite number of techniques that can be applied to any modelling task but it is impossible to know at the outset which will be most effective. By exposing the problem to a wide audience, competitions are a cost effective way to reach the frontier of what is possible from a given dataset. The power of competitions is neatly illustrated by the results of a recent bioinformatics competition hosted by Kaggle. It required participants to pick markers in HIV’s genetic sequence that coincide with changes in the severity of infection. Within a week and a half, the best entry had already outdone the best methods in the scientific literature. This presentation will cover how competitions typically work, some case studies and the types of business modelling challenges that the Kaggle platform can address.
Speaker: Anthony Goldbloom, Kaggle Pty Ltd
Room: Albert Suites
Human Resources; e-Commerce
Case Study: Naukri.com, Jeevansathi.com
Increasing Marketing ROI and Efficiency of Candidate-Search with Predictive Analytics
InfoEdge, India’s largest and most profitable online firm with a bouquet of internet properties has been Google’s biggest customer in India. Our team used predictive modeling to double our profits across multiple fronts. For Naukri.com, India’s number 1 job portal, predictive models target jobseekers most relevant to the recruiter. Analytical insights provided a deeper understanding of recruiter behaviour and informed a redesign of this product’s recruiter search functionality. This session will describe how we did it, and also reveal how Jeevansathi.com, India’s 2nd-largest matrimony portal, targets the acquisition of consumers in the market for marriage.
Speaker: Eric Siegel, Ph.D., Program Chair, Predictive Analytics World
Wednesday November 17, 2010
The Best and the Worst of Predictive Analytics:
Predictive Modeling Methods and Common Data Mining Mistakes
Click here for the detailed workshop description
- Workshop starts at 9:00am
- First AM Break from 10:00 – 10:15
- Second AM Break from 11:15 – 11:30
- Lunch from 12:30 – 1:15pm
- First PM Break: 2:00 – 2:15
- Second PM Break: 3:15 – 3:30
- Workshop ends at 4:30pm
Speaker: John Elder, Ph.D., CEO and Founder, Elder Research, Inc.
Dr Eric Siegel (interviewed here at http://decisionstats.wordpress.com/2009/07/14/interview_eric-siege/ )
continues his series of excellent analytical conferences-
Oct 19-20 – WASHINGTON DC: PAW Conference & Workshops (pawcon.com/dc)
Oct 28-29 – SAN FRANCISCO: Workshop (businessprediction.com)
Nov 15-16 – LONDON: PAW Conference & Workshop (pawcon.com/london)
March 14-15, 2011 – SAN FRANCISCO: PAW Conference & Workshops
* Register by Sep 30 for PAW London Early-Bird – Save £200
* For the Oct 28-29 workshop, see http://businessprediction.com
INFORMATION ABOUT THE PAW CONFERENCES:
Predictive Analytics World ( http://pawcon.com ) is the business-focused event for predictive analytics professionals, managers and commercial practitioners, covering today’s commercial deployment of predictive analytics, across industries and across software vendors.
PAW delivers the best case studies, expertise, keynotes, sessions, workshops, exposition, expert panel, live demos, networking coffee breaks, reception, birds-of-a-feather lunches, brand-name enterprise leaders, and industry heavyweights in the business.
Case study presentations cover campaign targeting, churn modeling, next-best-offer, selecting marketing channels, global analytics deployment, email marketing, HR candidate search, and other innovative applications. The Conference agendas cover hot topics such as social data, text mining, search marketing, risk management, uplift (incremental lift) modeling, survey analysis, consumer privacy, sales force optimization and other innovative applications that benefit organizations in new and creative ways.
PAW delivers two rich conference programs in Oct./Nov. with very little content overlap featuring a wealth of speakers with front-line experience. See which one is best for you:
PAW’s DC 2010 (Oct 19-20) program includes over 25 sessions across two tracks – an “All Audiences” and an “Expert/Practitioner” track — so you can witness how predictive analytics is applied at 1-800-FLOWERS, CIBC, Corporate Executive Board, Forrester, LifeLine, Macy’s, MetLife, Miles Kimball, Monster, Paychex, PayPal (eBay), SunTrust, Target, UPMC Health Plan, Xerox, YMCA, and Yahoo!, plus special examples from the U.S. government agencies DoD, DHS, and SSA.
Sign up for event updates in the US http://pawcon.com/signup-us.php
View the agenda at-a-glance: http://pawcon.com/dc/2010/agenda_overview.php
For more: http://pawcon.com/dc
PAW London 2010 (Nov 15-16) will feature over 20 speakers from 10 countries with case studies from leading enterprises in e-commerce, finance, healthcare, retail, and telecom such as Canadian Automobile Association, Chessmetrics, e-Dialog, Hamburger Sparkasse, Jeevansathi.com (India’s 2nd-largest matrimony portal), Life Line Screening, Lloyds TSB, Naukri.com (India’s number 1 job portal), Overtoom, SABMiller, Univ. of Melbourne, and US Bank, plus special examples from Anheuser-Busch, Disney, HP, HSBC, Pfizer, U.S. SSA, WestWind Foundation and others.
Sign up for event updates in the UK http://pawcon.com/signup-uk.php
View the agenda at-a-glance: http://pawcon.com/london/2010/agenda_overview.php
For more: http://pawcon.com/london
PAW San Francisco Save-the-Date and Call-for-Speakers:
March 14-15, 2011
San Francisco Marriott Marquis
San Francisco, CA
PAW call-for-speakers information and submission form: (Due Oct 8)
If you wish to receive periodic call-for-speakers notifications regarding Predictive Analytics World, email firstname.lastname@example.org with the subject line “call-for-speakers notifications”.
Predictive Analytics World
Washington DC – London – San Francisco
I just got an email from the great Dr Eric Siegel, one of my many mentors in this field of learning analytics and data. There are just three more days left for the Early Bird Price- so if you are doing your Media Planning Budget – it is a good time to register here. You can click on the screenshot itself to go to Registration Page.
An interview with Eric Siegel, Ph.D.President of Prediction Impact, Inc. and founding chair of Predictive Analytics World.
Ajay- What does this round of Predictive Analytics World have —–which was not there in the edition earlier in the year.
Eric- Predictive Analytics World (pawcon.com) – Oct 20-21 in DC delivers a fresh set of 25 vendor-neutral presentations across verticals employing predictive analytics, such as banking, financial services, e-commerce, education, healthcare, high technology, insurance, non-profits, publishing, retail and telecommunications.
PAW features keynote speaker, Stephen Baker, author of The Numerati and Senior writer at BusinessWeek. His keynote is described at www.predictiveanalyticsworld.com/dc/2009/agenda.php#day2-2
A strong representation of leading enterprises have signed up to tell their stories — speakers will present how predictive analytics is applied at Aflac, AT&T Bell South, Amway, The Coca-Cola Company, Financial Times, Hewlett-Packard, IRS, National Center for Dropout Prevention, The National Rifle Association, The New York Times, Optus (Australian telecom), PREMIER Bankcard, Reed Elsevier, Sprint-Nextel, Sunrise Communications (Switzerland), Target, US Bank, U.S. Department of Defense, Zurich — plus special examples from Anheuser-Busch, Disney, HSBC, Pfizer, Social Security Administration, WestWind Foundation and others.
To see the entire agenda at a glance: www.predictiveanalyticsworld.com/dc/2009/agenda_overview.php
We’ve added a third workshop, offered the day before (Oct 19), “Hands-On Predictive Analytics. There’s no better way to dive in than operating real predictive modeling software yourself – hands-on.” For more info: www.predictiveanalyticsworld.com/dc/2009/handson_predictive_analytics.php
Ajay- What do academics, corporations and data miners gain in this conference? list 4 bullet points for the specific gains.
Eric- A. First, PAW’s experienced speakers provide the “how to” of predictive analytics. PAW is a unique conference in its focus on the commercial deployment of predictive analytics, rather than research and development. The core analytical technology is established and proven, valuable as-is without additional R&D — but that doesn’t mean it’s a “cakewalk” to employ it successfully to ensure value is attained. Challenges include data requirements and preparation, integration of predictive models and their scores into existing organizational systems and processes, tracking and evaluating performance, etc. There’s no better way to hone your skills and cultivate an informed plan for your organization’s efforts than hearing how other organizations did it.
B. Second, PAW covers the latest state-of-the-art methods produced by research labs, and how they provide value in commercial deployment. This October, almost all sessions in Track 2 are at the Expert/Practitioner-level. Advanced topics include ensemble models, uplift modeling (incremental modeling), model scoring with cloud computing, predictive text analytics, social network analysis, and more.
PAW’s pre- and post-conference workshops round out the learning opportunities. In addition to the hands-on workshop mentioned above, there is a course covering core methods, “The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes” (www.predictiveanalyticsworld.com/dc/2009/predictive_modeling_methods.php) and a business-level seminar on decision automation and support, “Putting Predictive Analytics to Work” (www.predictiveanalyticsworld.com/dc/2009/predictive_analytics_work.php).
C. Third, the leading predictive analytics software vendors and consulting firms are present at PAW as sponsors and exhibitors, available to provide demos and answer your questions. What do the predictive analytics solutions do, how do they compare, and which is best for your? PAW is the one-stop-shop for selecting the tool or solution most suited to address your needs.
D. Fourth, PAW provides a unique, focused opportunity to network with colleagues and establish valuable contacts in the predictive analytics industry. Mingle, connect and hang out with professionals facing similar challenges (coffee breaks, meals, and at the reception).
Ajay- How do you balance the interests of various competing softwares and companies who sponsor such event?
Eric- As a vendor-neutral event, PAW’s core program of 25 sessions is booked exclusively with enterprise practitioners, thought leaders and adopters, with no predictive analytics software vendors speaking or co-presenting. These sessions provide substantive content with take-aways which provide value that’s independent of any particular software solution — no product pitches! Beyond these 25 sessions are three short sponsor sessions that are demarcated as such, and, despite being branded, generally prove to be quite substantive as well. In this way, PAW delivers a high quality, unbiased program.
Supplementing this vendor-neutral program, the room right next door has an expo where attendees have all the access to software and solution vendors they could want (cf. in my answer to the prior question regarding software vendors, above).
Here are a couple more PAW links:
For informative PAW event updates:
To sign up for the PAW group on LinkedIn, see:
Ajay- Describe your career in science including research that you specialize in. How would you motivate students today to go for science careers
Eric- Well, first off, my work as a predictive analytics consultant, instructor and conference chair is in the application of established technology, rather than the research and development of new or improved methods.
But the Ph.D. next to my name reveals my secret past as an “academic”. Pure research is something I really enjoyed and I kind of feel like I had a brain transplant in order to change to “real world work”. I’m glad I made the change, although I see good sides to both types of work (really, they’re like two entirely different lifestyles).
In my research I focused on core predictive modeling methods. The ability for a computer to automatically learn from experience (data really is recorded experience, after all), is the best thing since sliced bread. Ever since I realized, as a kid, that space travel would in fact be a huge pain in the neck, nothing in science has ever seemed nearly as exciting.
Predictive analytics is an endeavor in machine learning. A predictive model is the encoding of a set of rules or patterns or regularities at some level. The model is the thing output by automated, number-crunchin’ analysis and, therefore, is the thing “learned” from the “experience” (data). The “magic” here is the ability of these methods to find a model that performs not only over the historical data on your disk drive, but that will perform equally well for tomorrow’s new situations. That ability to generalize from the data at hand means the system has actually learned something.
And indeed the ability to learn and apply what’s been learned turns out to provide plenty of business value, as I imagined back in the lab. The output of a predictive model is a predictive score for each individual customer or prospect. The score in turn directly informs the business decision to be taken with that individual customer (to contact or not to contact; with which offer to contact, etc.) – business intelligence just doesn’t get more actionable than that.
For the impending student, I’d first point out the difference between applied science and research science. Research science is fun in that you have the luxury of abstraction and are usually fairly removed from the need to prove near-term industrial applicability. Applied science is fun for the opposite reason: The tangle of challenges, although less abstract and in that sense more mundane, are the only thing between you and getting the great ideas of the world to actually work, come to fruition, and have an irrefutable impact.
Ajay- What are the top five conferences in analytics and data mining in your opinion in the world including PAW.
Eric- KDD – The leading event for research and development of the core methods behind the commercial deployments covered at PAW (“Knowledge Discovery and Data Mining”).
ICML – Another long-standing research conference on machine learning (core data mining).
eMetrics.org – For online marketing optimization and web analytics
Text Analytics Summit – Text mining can leverage “unstructured data” (text) to augment predictive analytics; the chair of this conference is speaking at PAW on just that topic: www.predictiveanalyticsworld.com/dc/2009/agenda.php#day2-15
Predictive Analytics World, the business-focused event for predictive analytics professionals, managers and commercial practitioners – focused on the commercial deployment of predictive analytics: pawcon.com
Ajay- Would PAW 2009 have video archives, videos as well or podcasts for people not able to attend on site.
Eric- While the PAW conferences emphasize in-person participation, we are in the planning stages for future webcasts and other online content. PAW’s “Predictive Analytics Guide” has a growing list of online resources: www.predictiveanalyticsworld.com/predictive_analytics.php
Ajay- How do you think social media marketing can help in these conferences.
Eric- Like most events, PAW leverages social media to spread the word.
But perhaps most pertinent is the other way around: predictive analytics can help social media by increasing relevancy, dynamically selecting the content to which each reader or viewer is most likely to respond.
Ajay- Do you have any plans to take PAW more international? Any plans for a PAW journal for trainings and papers.
Eric- We’re in discussions on these topics, but for now I can only say, stay tuned!
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 awards for teaching, including graduate-level courses in machine learning and intelligent systems – the academic terms for predictive analytics.He has published 13 papers in data mining research and computer science education, has served on 10 conference program committees, has chaired a AAAI Symposium held at MIT, and is the founding chair of Predictive Analytics World.
For more on Predictive Analytic World-