Predictive Analytics World

Breaking news: On Friday, Netflix Prize team “BellKor’s Pragmatic Chaos” passed the mark, qualifying for the $1,000,000 prize. The team includes last February’s PAW speaker Andreas Töscher.

But they haven’t won yet. Their qualification triggers a 30-day count-down during which all teams have a final chance to improve their efforts.

The Netflix Prize is an open contest in product recommendations. The competition has provided an incentive to teams worldwide to improve the state-of-the-art, and, in a sense, work as an extended R&D effort for Netflix to improve their movie recommendation accuracy.

How they did it: Joining forces. Only by an international collaboration, and the combining of methodologies, did the current leading team hit the mark. The team is composed of four teams that have also competed independently, located in the U.S., Canada, Austria and Israel.

Combined methodology made simple. Each team has developed an intricate approach. Once they agreed to collaborate, how hard did they have to work to integrate their systems? Actually, not hard at all. Rather than dig in, think hard, and assess where one system’s weaknesses may be compensated for by another team’s strengths, they let predictive modeling do it - at least, that’s how Mr. Töscher indicated they did it when 2 of these sub-teams combined to form “BellKor in BigChaos” and become the leader several months ago (we don’t yet officially know how they did it this time).

In this approach, each system may conveniently be treated as a “black box,” training a new “meta-system” to combine the respective outputs into one better output. This is called “meta-learning” or “ensemble methods”, which elicits the concept of collective intelligence.

So stay tuned - we should know more soon!

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Predictive Analytics World, coming October 20-21 to Washington DC, has a great line-up of keynote speakers:

Stephen Baker, author of The Numerati and senior writer at BusinessWeek, where he’s been since 1987. Steve’s book has received a tremendous amount of attention this year. It is a revealing and insightful exploration of the opportunities and pitfalls of applied analytics, and consumer perception thereof.

Usama Fayyad, Ph.D. — CEO, Open Insights and formerly Yahoo!’s Chief Data Officer and Executive Vice President of Research & Strategic Data Solutions. Dr. Fayyad will return as an acclaimed keynote speaker. His keynote at February’s PAW (San Francisco) received extremely strong ratings from conference attendees.

Finally, this blogger, Eric Siegel, Ph.D., will be kicking off PAW with a reprise of my keynote, “Five Ways to Lower Costs with Predictive Analytics.”

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Save-the-date for the next PAW: Oct 19-22, 2009 in Washington DC

Speaker proposals deadline: June 4, 2009

Join Predictive Analytics World (www.predictiveanalyticsworld.com) to share how predictive analytics delivers a business impact for your organization. Here is the opportunity to present your work deploying predictive analytics, predictive modeling, product recommendations, response modeling, churn modeling, behavior-based content/ad selection, customer segmentation, fraud detection or other incarnations of predictive analytics.

PAW - for which I act as program chair - is the business-focused event for predictive analytics professionals, managers and practitioners. This conference covers today’s commercial deployment of predictive analytics. It is the only conference of its kind, with sessions and content reaching across business applications, across industries, and across vendors of solutions and software.

This conference delivers case studies, expertise and resources to achieve two objectives:

1) Bigger wins: Strengthen the business impact of predictive analytics deployment

2) Broader capabilities: Establish new opportunities with predictive analytics

The inaugural Predictive Analytics World, February 18-19 2009, was an acclaimed success, with over 200 attendees from 13 countries witnessing case studies from over 20 named companies that represent 11 industries. Excitement and buzz ran high as participants learned how to improve efficiency and optimize across more than 10 business applications of predictive analytics.

“Predictive Analytics World was probably the best analytics conference I have attended, from a knowledge point of view, in a long time…[and] turned into my new must-go-to conference.”

Dennis R. Mortensen
Director of Data Insights
Yahoo!

Much more: Numerous articles and blog entries about February’s PAW.

Speak to a valuable audience. The inaugural event drew attendees from heavy-hitters such as Apple, Booking.com, Business Objects, Capital One, Charles Schwab, Digi-Key, e-Dialog, eBay, efficient frontier, Ernst & Young, Expedia, Hewlett-Packard, HSBC, Intuit, LexisNexis, Mars, Merkle, MetLife Auto & Home, MindTree, NEC Laboratories America, Orange Labs, Pacific Northwest National Laboratory, PayPal, Premera Blue Cross, PricewaterhouseCoopers, Razorfish, Stamps.com, and Thermo Fisher Scientific.

As a PAW speaker, you’ll be in good company. February’s PAW included case studies from the following companies: Acxiom, Amazon.com, Bella Pictures, Charles Schwab, ClickForensics, Google, The National Rifle Association, Pinnacol Assurance, Reed Elsevier, Sun Microsystems, TaxBrain, Telenor, Wells Fargo, Yahoo! — plus mini-csae studies from the following companies: Anheuser-Busch, Disney, Hewlett-Packard, HSBC, IRS, Pfizer, Social Security Administration, WestWind Foundation — and plus case studies at the coscheduled R users group meeting from Facebook and Ancestry.com.

PAW is a one-of-a-kind event:
* While focused on commercial deployment rather than research and development, the conference is substantive in predictive analytics
* PAW is the only such vendor-neutral event
* Chaired by a former Columbia University professor with a decade of real-world, commercial experience
* Run by the producers of the eMetrics Marketing Optimization Summit (the leading web analytics conference), and the acclaimed predictive analytics training program, Predictive Analytics for Business, Marketing and Web.

Speaker Perks - What You Get:
* Widen your reputation as an expert in predictive analytics
* Strengthen your company’s reputation as a leader in predictive analytics
* Gain exposure to consumers of predictive analytics
* Receive free registration to attend Predictive Analytics World
* Post your bio and a link to your site on the PAW website and in the conference program guide

Click here for more information about the call-for-speakers, and for the speaker submission form.

Click here to register for our informative event updates.

Click here to sign up for the PAW group on LinkedIn.

Finally, let me know if you have any questions.

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I conduct the following training seminar on predictive analytics - here’s the 2009 schedule:

Predictive Analytics for Business, Marketing and Web

A two-day intensive seminar brought to you by Prediction Impact, Inc.

Dates: May 3-4, May 27-28, Oct 14-15, Oct 18-19, and Nov 11-12, 2009
Locations: San Jose (May), NYC (May), Stockholm (Oct), DC (Oct), San Francisco (Nov)
Or access the online training: Predictive Analytics Applied. Immediate access at any time - SNEAK PREVIEW VIDEO

93% rate this program Excellent or Very Good. (details)

**The official training program of Predictive Analytics World**
**Offered in conjunction with eMetrics events**

Business metrics do a great job summarizing the past. But if you want to predict how customers will respond in the future, there is one place to turn—predictive analytics. By learning from your abundant historical data, predictive analytics provides the marketer something beyond standard business reports and sales forecasts: actionable predictions for each customer. These predictions encompass all channels, both online and off, foreseeing which customers will buy, click, respond, convert or cancel. If you predict it, you own it.

The customer predictions generated by predictive analytics deliver more relevant content to each customer, improving response rates, click rates, buying behavior, retention and overall profit. For online applications such as e-marketing and customer care recommendations, predictive analytics acts in real-time, dynamically selecting the ad, web content or cross-sell product each visitor is most likely to click on or respond to, according to that visitor’s profile. This is AB selection, rather than just AB testing.

Predictive Analytics for Business, Marketing and Web is a concentrated training program that includes interactive breakout sessions and a brief hands-on exercise. In two days we cover:

• The techniques, tips and pointers you need in order to run a successful predictive analytics and data mining initiative
• How to strategically position and tactically deploy predictive analytics and data mining at your company
• How to bridge the prevalent gap between technical understanding and practical use
• How a predictive model works, how it’s created and how much revenue it generates
• Several detailed case studies that demonstrate predictive analytics in action and make the concepts concrete
• NEW TOPIC: Five Ways to Lower Costs with Predictive Analytics

Instructor: Eric Siegel, Ph.D.

No background in statistics or modeling is required. The only specific knowledge assumed for this training program is moderate experience with Microsoft Excel or equivalent.

For more information, visit Predictive Analytics for Business, Marketing and Web, e-mail us at training@predictionimpact.com or call (415) 683-1146.

Cross-Registration Special: Attendees earn $250 off the Predictive Analytics World Conference

$100 off early registration, 3 weeks ahead

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A mammoth new must-have data mining book is coming, the Handbook of Statistical Analysis and Data Mining Applications, by Robert Nisbet, PhD, John Elder, PhD, and Gary Miner, PhD.

“The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book for business analysts, scientists, engineers and researchers that brings together in a single resource all the information a beginner will need to rapidly learn how to conduct data mining and the statistical analysis required to interpret the data once mined.”

I previewed the book and wrote this heartfelt testimony:

Data mining practitioners, here is your bible, the complete “driver’s manual” for data mining.  From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering and medical applications.  What are the best practices through each phase of a data mining project?  How can you avoid the most treacherous pitfalls?  The answers are in here.

Going beyond its responsibility as a reference book, this resource also provides detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications.  This way, newcomers start their engines immediately and experience hands-on success.

If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource.  To put it lightly, if this book isn’t on your shelf, you’re not a data miner.

You can pre-order it on Amazon.

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News flash: The next Predictive Analytics World will be held October 19-22, 2009 in Washington, DC. Stay tuned for more details.

The inaugural Predictive Analytics World, February 18-19 2009, was an acclaimed success, with over 200 attendees from over 13 countries witnessing case studies from over 20 named companies that represent 11 industries. Excitement and buzz ran high as participants learned how to improve efficiency and optimize across more than 10 business applications of predictive analytics.

Here’s what people are saying about PAW - quotes, articles and many blogs are linked below.

“Predictive Analytics World was probably the best analytics conference I have attended, from a knowledge point of view, in a long time… [and] turned into my new must-go-to conference.”

Dennis R. Mortensen
Director of Data Insights
Yahoo!
As stated on his blog

“Despite limited modeling experience, I was able to draw parallels between speakers in various industries and the types of analysis we do at uShip. Everyone seemed excited to be attending despite the economy. We left San Francisco with a long list of actionable items — can’t wait to see what’s on tap for next year!”

Michael C. Foroobar
Sr. Associate - Strategy, Reporting, and Analytics
uShip.com

Articles about Predictive Analytics World:

5 Ways to Cut Costs with Predictive Analytics
Welcome to the Customer Data Revolution
Both articles by Lauren McKay, Destination CRM

Blog entries about Predictive Analytics World:

Some thoughts after attending Predictive Analytics World
James Taylor, BeyeNetwork - Includes links to 11 live event blog entries during PAW.

How Google and Facebook are using R
Michael E. Driscoll, Data Evolution

R Prominent at Predictive Analytics World
David Smith, Revolutions

What Yahoo! Knows About Social Media, Marketing, & Predictive Analytics
Lauren McKay, Destination CRM

More from Predictive Analytics World
TammiKay George, BI and the Chicken Pot Pie

Predictive Analytics World
TammiKay George, BI and the Chicken Pot Pie

More from Predictive Analytics World
TammiKay George, BI and the Chicken Pot Pie

Day one at predictive analytics world
Anne Milley, sascom voices

Day two at predictive analytics world
Anne Milley, sascom voices

It’s a Predictable World!

Immeria

Stats Man’s Corner and another by same

My comments to Predictive Analytics World - San Francisco 2009
Dennis R. Mortensen, Visual Revenue

Applying Next Generation Uplift Modeling to Optimize Customer Retention Programs
Customer Think

Survey Results: Predictive Analytics Helps Increase ROI

The Data Mining Blog

On Intelligence & Software
Paul O’Rorke

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Predictive Analytics World has announced the results of its survey of current and future users of predictive analytics software and services, vendors and consultants. The survey findings indicate a widespread move toward predictive analytics technology in order to guide better business and marketing decisions. According to the survey, the vast majority of those who have already deployed predictive analytics are experiencing increased ROI from online channels.

The Predictive Analytics World survey was designed to discover how companies are currently using or plan to use predictive analytics technology in the future, what applications are most important across verticals as well as the primary motivations and expectations for deploying the technology. Of the respondents who indicated they were from corporate, non-profit or government organizations, 51.5 percent have never deployed predictive analytics. However, roughly 85 percent have plans to do so within the next five years, with 51.5 percent planning to do so in the next six months.

Perhaps the most promising insight revealed by the Predictive Analytics World survey is that the majority of respondents who have deployed predictive analytics have attained a positive ROI, even for their least successful initiative. In their most successful deployment of predictive analytics, 90.1 percent of respondents saw a lift in ROI.

Click here for a complete analysis of the survey results (PDF file).

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It’s hard to pick up a newspaper these days without seeing companies cutting more costs. Part of this story is that companies are shifting their spending to invest in a new flavor of business intelligence technology that predicts the buying behavior of each customer or prospect - predictive analytics.

Predictively modeling customer response provides something completely different from standard business reporting and sales forecasting: actionable predictions for each customer. These per-customer predictions are key to allocating marketing and sales resources. By predicting which customer will respond to which offer, you can better target to each customer.

As your company prepares to deploy a predictive model, there are best practices that avert the risk the model won’t perform up to par.  Here are three guidelines to ensure this risk is minimized.

1. Don’t evaluate the predictive model over the same data you used to create it.

When evaluating a predictive model, never test it over the same data that you used to produce it, known as the training data.  The data used for evaluation purposes must be held-aside data, called test data, which provides an unbiased, realistic view of how good the model truly is.  If it’s not doing well on that data, you need to revisit model generation, change the data, or change the modeling method until you get a better one.

2. Only deploy your predictive model incrementally.

Once you have a predictive model that looks good and ready for deployment, start by deploying it in a “small dose”.  Keep the current, existing method of decision-making in place, and simultaneously - perhaps 5% of the time - employ the predictive model.  This way, the old and the new stand in contrast, so you can see whether indeed the value of the model is proven - that profits have increased or that response rates have increased.

3. Always maintain and test against a control set.

Finally, in similar vein to (2) above, keep this kind of A-B testing in place moving forward, pitting “use the model” against “don’t use the model”.  Ideally, you always keep that going, so that you have a small control set for which things continue the old way, or, in any case, for which decisions are automated in a way that does not require a predictive model.  This serves as a baseline against which the performance of the predictive model is constantly monitored.  This way, you are alerted when a predictive model’s performance is degrading, at which point it’s time to produce an updated model over more up-to-date data.

In sum, by following these best practices, your company can benefit from the accurate targeting of predictive analytics while minimizing risk.
For further predictive analytics reading, case studies, training options and other resources, see the Predictive Analytics Guide.

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Here are six recent blog interviews I’ve given about predictive analytics and the Predictive Analytics World conference. These add to the running FAQ I’ve begun here.

Interview by Romakanta Irungbam on DataLLigence

• What are the most common mistakes you’ve encountered while working on data mining projects?
• Which approaches do you recommend/use to define the acceptable accuracy/cut-off level for a data mining project?
• What are the new areas/domains where data mining is being applied?
• And more

Interview by John Langford on Hunch.net

• How fast or difficult is it to transfer academic methods to business use?
• And more

Interview by Sandro Saitta on dataminingblog.com

• Data mining, machine learning, knowledge discovery in databases, pattern recognition, etc. Are these fields really different?
• What is the most common data mining question you have heard?
• Imagine that I can give you any data set by tomorrow. What kind of data would you like mining?
• And more

Interview by Vincent Granville on AnalyticBridge

• Which analytical fields are likely to experience growth, and why?
• Which methodologies might become obsolete, which ones are likely to entertain growth?
• What do you recommend for students starting an analytical career or choosing a University curriculum?
• What are the biggest successes of data mining and statistical sciences in the corporate world?
• What are the best practices for analytic professionals?
• And more

Interview by Lars Johansson on WebAnalysts.Info

• What is your definition of predictive analytics?
• And more

Interview by Gary Angel on SemAngel

• Why do you think analytics - especially advanced analytics - has proven challenging for many industries to really embed?
• Do you sometimes find yourself surprised at the low-level of analytic sophistication in even very big organizations with very large marketing budgets?
• And more

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Predictive analytics FAQ #1: What does it take for predictive analytics to deliver business value - what are the prerequisites for success?

Predictive analytics can only succeed with the right ingredients in place:

• One or more experts in-house or deeply engaged
• A business case for predictive analytics deployment, such as one of the business applications listed in this article (i.e., a way a predictive model can and will be used, rather than just being a nifty model that may not provide business value); management buy-in for the integration and deployment of predictive scores
• Sufficient data to train a predictive model for the prediction goal at hand
• General understanding and buy-in of a predictive analytics initiative by stakeholders across business functions
• Implementation of organizational process best practices.  For analytics, this means CRISP-DM (Cross-Industry Standard Process for Data Mining — www.crisp-dm.org) or equivalent.  An iterative process that ensures comprehension, feedback and buy-in is attained across a group of relevant managers at key phases of a predictive analytics project
• When initial deployment success is achieved, sufficient executive buy-in to facilitate long-term maintenance that keeps the deployment alive and effective

Some of these are elusive; if one goes astray, adoption or longevity is not attained.

The good news is that in fact these ingredients usually do exist for mid-tier to large companies - and often for smaller companies, if they have data pertaining to enough customers or prospects.  And, with these ingredients place, predictive analytics delivers high returns - significantly higher than analytics that are not predictive in nature.  An IDC study showed that predictive analytics initiatives show an average ROI of 145%, in comparison to 89% for non-predictive analytics (”Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study,” September, 2003).

What makes the difference is widespread understanding and buy-in, executive buy-in (and perhaps a bit of executive understanding :), and adoption of best practice business processes (on top of killer core analytical methods).  This is where Predictive Analytics World comes in.  There’s no better way for non-experts to learn what predictive analytics does and how it works - and to become convinced of its effectiveness - than named case studies, which is why PAW’s program is built primary of such success stories, across verticals.  See the full program, at http://www.predictiveanalyticsworld.com/agenda_overview.php

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