Archive for March, 2013

March 30th 2013

Predicting Lying and Predicting Dying

 

This article was originally published on SAS Knowledge Exchange

Who benefits by predicting your behavior? Organizations do—companies, governments, hospitals, and political campaigns. They employ predictive analytics, technology that learns from data to render per-person predictions, one individual at a time.

People have been struck by the final words in the title of my new book on this subject, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (www.thepredictionbook.com).

An old friend even sent me a photo of the book aside an onion, suggesting the material might be lightened to predict who will click, buy, lie, or cry. Or, we might consider changing it to, "The power to predict who will drink Coke, choke, or croak."

Joking aside, this exercise in enumerating verbs serves to demonstrate just how wide a variety of human actions and behavior can be predicted, such as whether an individual will buy, steal, drop out of school, quit his or her job, donate, crash his or her car, or vote.

Prediction is possible when we have at our disposal pertinent data that records such behavior. And we do! In case you haven't noticed, there's a well-publicized flood of data. Data is a recording of history, of things that have happened and actions people have taken. We aren't drowning in data, we're drowning in experience from which to learn.

Predictive analytics is the technology that leverages data to generate predictions of such human behavior on the individual level, one person at a time. Its capacity to do so reflects the power intrinsic to the data from which it learns. And the value attained by so doing relies on organizations making active use of such predictions, employing them to drive per-person operational decisions, one individual at a time. Lying and dying are pertinent examples.

Predicting Lying

Law enforcement is improving lie detection with predictive analytics methods.  As with medical diagnosis or assessing the risk of an applicant for insurance coverage, predictive analytics augments established methodology to improve—by way of machine learning methods—the ability to assess the risk that an individual is lying based on the collection of known characteristics about that individual.

For example, University at Buffalo researchers trained a system to detect lies with 82 percent accuracy by observing eye movements alone. In another project, researchers predict deception with 76 percent accuracy within written statements by persons of interest in military base criminal investigations.

Predicting Dying

With all the human behavior being predicted, how about the final thing each of us do: die? In fact, there are five reasons organizations may predict your death. Sometimes they do it with altruistic intent, for healthcare-related purposes. In other cases, there's a financial incentive—they predict death for the money.

Healthcare providers predict death to help prevent it. For example, Riskprediction.org.uk predicts your risk of death in surgery, based on aspects of you and your condition, in order to help inform medical decisions.

Law enforcement and military predict kill victims in order to protect, and safety institutes predict system failure casualties to help avert them.

Life insurance prices policies according to predicted life expectancy. A growing number of life insurance companies go beyond conventional actuarial tables and employ predictive analytics to establish mortality risk.

Beyond life insurance, it turns out health insurance companies also predict death—of policyholders. Until recently, death prediction has not been within the usual domain for health insurance. I got the inside scoop, anonymously, from a top-five U.S. health insurance company—but I'll reserve the details for my book (Predictive Analytics), or see more details in my Smart Blogs article, Deathwatch: Five Reasons Organizations Predict When You Will Die.

Eric Siegel, Ph.D., is the founder of Predictive Analytics World (www.pawcon.com)coming in 2013 to Toronto, San Francisco, Chicago, Washington D.C., Boston, Berlin, and Londonand the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (February 2013, published by Wiley). For more information about predictive analytics, see the Predictive Analytics Guide (www.pawcon.com/guide).

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March 24th 2013

The New Predictive Profession – Odd Yet Newly Legitimate

Here's a review of my book Predictive Analytics from Robert Nisbet, Ph.D., a leading consultant, author, and predictive analytics instructor at University of California – Irvine (posted here with his permissoin).

Review of Predictive Analytics – The Power to Predict Who Will Click, Buy, Lie, or Die.  By Eric Siegel.

Robert Nisbet, Ph.D.
March 21, 2013

Predictions have a problem.  They are viewed as either magic or “snake-oil” by most people.  It doesn’t help that many previous predictors in society like Nostradamus and Edgar Cayce were viewed somewhat askance at best, and as charlatans at worst.  Only recently has the making of predictions gained some legitimacy, and this is due to the recent rise of predictive analytics in many sectors of our society.  This rather odd profession has developed out of the science of Artificial Intelligence, which seeks to capture, however crudely, some of the intelligent predictive processing capability of human brains, and express it mathematically in computer programs.  Initially, predictive analytics (aka data mining) lived in the rarefied atmosphere of academics or highly-paid consultants.  The challenge for predictive analytics scientists and professionals is to recast the subject in a form that is “mapped” more closely to common perceptions of what we do in our brains and why we do it.  Eric Siegel has done it!

 

The human brain is a tricky thing to understand.  I was trained initially as a Biologist, which exposed me to the view that the human brain is the most complex non-linear pattern processing system in the universe.  Much of its function is devoted to prediction and decision-making, and much of it is done unconsciously. One of the biggest challenges confronting scientists of the brain is to explain its function in terms and expressions that everybody understands.  It is people with brains that do things.  Eric Siegel’s book helps to explain some very fascinating aspects of why people do what they do, in a very engaging way.

 

He arrays the topics in the book around five “effects” of prediction: (1) the prediction effect; (2) the data effect; (3) the induction effect; (4) the ensemble effect; and (5) the persuasion effect.  To explain the nature and significance of these effects, what does he do?  He tells stories. Everybody likes stores. The most popular book ever written (the Bible) is basically a story book.  One of his extended stories is about John Elder, who is near and dear to my heart (many among his students and audiences think so to).  Eric couches a number of predictive analytics truisms in terms of how John Elder learned them, such as stumbling over the error of “leakage from the future”.  John loves to wax eloquently on that mistake in his presentations of “The Top 10 Data  Mining Mistakes”.  Another extended story is about the Watson supercomputer that won over 2 human competitors on the TV show, “Jeopardy”.  Eric explains how Watson did it, using the ensemble effect.  An ensemble uses many mathematical techniques (algorithms) to predict an outcome, and then combines them to compose an overall prediction.  Watson does not just use ensembles, Eric explains that  its processing architecture consists of “an ensemble of ensembles of ensembles”.  That complexity would hurt my head, if Eric had not brought it down to earth in his explanation of what Watson is and how it works.

 

The third extended story is about… (who else) Barack Obama.  Obama set up a team of data miners (as they were called then) in 2011, to be based in Chicago, and tasked with the challenge to leverage data mining technology to further his election campaign.  When I saw the many ads for these data mining professionals in several online job posts, I thought, “Watch out, Republicans; he’s going to eat your lunch”.  And, he did. Eric explains how Obama’s predictive analytics team predicted those “swing voters” who had the greatest likelihood of being influenced to vote for Obama.  Then, they used data from social media, like Twitter and Facebook, to predict which people were strong influencers of the swing voters; they targeted them, not the swing voters themselves (an example of the “Persuasion Effect”).  That approach is at the very cutting edge of predictive analytics today, largely because of Obama’s election campaign.  And Eric’s presentation of it makes you think, “Well, duh… of course”!

 

Eric Siegel has brought predictive analytics down from the intellectual stratosphere  where most scientists and engineers dwell, and expressed it in terms that anyone can understand, and vended it in the form of a bunch of stories.  This book should be your first predictive analytics book on your bookshelf, or to give to clients and friends when they ask, “So, what do you do”?  That is the question that Eric poses in the Preface to the book, and then he marshals his stories in the rest of the book to answer it.

 

In the University of California at Irvine Predictive Analytics Certification Program (where I teach), we require our own book (“Handbook of Statistical Analysis & Data Mining Applications”, R. Nisbet, John Elder, and Gary Miner, 2009).  NOW, we will require Eric Siegel’s book also, and direct students to read it first!  You should too.

 

Bob Nisbet, Ph.D.
Consulting Data Scientist
Instructor, UC-Irvine Predictive Analytics Certification Program

 

Bio:

Bob was trained initially in Ecology and Ecosystems Analysis.  He has over 30 years experience in complex systems analysis and modeling.  Currently, he teaches several courses in predictive analytics in the University of California – Irvine Predictive Analytics Certification Program.  In business, he pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications  Insurance, Banking, and Credit industries.   In addition to data mining, he has expertise in data warehousing technology for Extract, Transform, and Load (ETL) operations, Business Intelligence reporting, and data quality analyses.  Currently, he functions as a consulting data scientist. He is lead author of the "Handbook of Statistical Analysis & Data Mining Applications" (Academic Press, 2009), and a co-author of "Practical Text Mining" (Academic Press, 2012). His current book project is to serve as contributor and general editor for a new book, Predictive Analytics in Medicine and Healthcare, under contract with Academic Press (Elsevier Publ.) for publication in 2014.  His current research is focused on the capture and tracking in near real-time of 12 emotions extracted from millions of Twitter tweets daily, using Natural Language Processing techniques.  He uses these emotions to predict with data mining algorithms various online indexes like the Gallup Daily Mood Index for Happiness several days ahead of their public release, for use by stock traders as a guide to where the market is going.

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March 15th 2013

WSJ: HP Piloted Program to Predict Which Workers Would Quit

Joel Schectman at the Wall Street Journal wrote about a story broken in my new book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

 

Wall Street Journal Article:

Book: HP Piloted Program to Predict Which Workers Would Quit


Joel Schectman, Wall Street Journal

Hewlett Packard Co. tested a predictive scoring system that attempted to grade the likelihood that individual workers would quit the company, according to a new book.

HP piloted the scoring system in 2011 aimed at lowering attrition through a better understanding of which workers were most likely to leave, according to Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie Or Die by Eric Siegel… HP data scientists believed a companywide implementation of the system could deliver $300 million in potential savings “related to attrition replacement and productivity”… 

… "The scarcest resource any company has is human resources," Mr. Siegel said. Predictive analytics offers the possibility to "preemptively intervene" in employee attrition, and "that's the holy grail," Mr. Siegel said. 

Read the article on the Wall Street Journal website (update: no Paywall – free to view)

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March 11th 2013

Get “Predictive Analytics” – the Book – and Enjoy Free Online Training

Get Predictive Analytics –  the Book –  and Receive Free Online Training


Predictive Analytics bookApril 3rd is Predictive Analytics Day – not the science, the book! To build awareness of my new book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (published by Wiley Feb. 19), we're providing an offer ya can't refuse.

Order the book on April 3rd via Amazon (under $15) for:

> Free access to the first of four modules of the author's acclaimed online training program, Predictive Analytics Applied

> A 35% discount code off the full online training ($495), or its in-person version, Predictive Analytics for Business, Marketing and Web ($1,495  April 25-26 in NYC)

> Automatic entrance into a drawing to receive a pass for any Predictive Analytics World this year (San Francisco, Chicago, DC, Boston, London, or Berlin)

Order multiple copies for your colleagues on April 3rd and get the full training program:

  • Order 20 copies and get access to the full online training program Predictive Analytics Applied  a $495 value for under $300 (plus you get the 20 books you ordered)
  • Order 30 copies and get two trainee registrations  a $990 value (and 30 books) for under $450
  • Order 50 copies and get four trainee registrations  a $1,980 value (and 50 books) for under $750
  • Order 100 copies and get ten trainee registrations  a $4,950 value (and 100 books) for under $1,500
  • Plus get five more trainee registrations for every 50 copies beyond 100

Multiple orders also gain proportional entries into the drawing to receive a pass to any Predictive Analytics World this year. Prices above assume free shipping and no tax; free shipping to any USA address is available by selecting "Super Saver Shipping" during checkout, but tax may be charged for some destination states. Non-USA customers are eligible for this promotion (training materials ship internationally), but will be charged for shipping by Amazon for the book order. If you are considering ordering 20 or more copies on April 3, please email eric@predictionimpact.com in advance so we can help inform Amazon's "predictive supply planning" for this book.

Instructions to take this offer:

  1. Order the hardcover book with Amazon.com on exactly the date April 3, 2013, between 8:00am and 10:00pm Eastern Daylight Time. Only hardcover orders by way of Amazon are eligible   e-book orders do not qualify for this offer. Use this link to order: amzn.to/TEWSsA
  2. Forward your Amazon email receipt to admin12@predictionimpact.com. If you order enough books to earn online training access for more than one person, also provide the list of registrant names, email addresses, and postal mailing addresses for training fulfillment.

 

Within two business days, you will receive three months of on-demand access to the training module(s), as well as a 35% discount code for further training (must be used by April 24).

 

About the book, Predictive Analytics:

In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction.

Well received by a broad readership, the book has landed as the #1 Best Seller in two different categories on Amazon. However, note there are five reasons this book matters to experts.

Read the preface

39 of your colleagues who loved this book

More info – excerpts, videos, reviews, and more


Happy reading!

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March 9th 2013

The Predictive Advantage One Political Party Currently Holds

I was quoted a fair bit in this FISCAL TIMES article – check it out:

The Big Data Advantage: Can Republicans Catch Up?

 

The Republicans are now talking in earnest about the virtues of Big Data – because they have to.

 

Ever since Mitt Romney lost the November presidential election, Republicans as disparate as former GOP presidential candidate and Speaker of the House Newt Gingrich and House Majority Whip Eric Cantor are pressing the need to get behind Big Data. They know now what the GOP ignored in 2012 – Obama won in large part because his campaign used the skills of over 50 data analysts in the tech space to micro-target key segments of the electorate. They know it’s critical not just to appeal to broader swaths of the American public, but to go out and win elections.

 

RELATED:  The Real Story Behind Obama's Election Victory

 

“Data science Obama-style has no relationship to the Republican model of Internet politics,” Gingrich wrote recently in a plaintive memo designed to push the GOP off its rump ahead of CPAC – the conservative gathering next week. “The Obama system is helped in data science by its 85-to-90 percent dominance of Silicon Valley. If you have the founders of Google and Facebook helping you design your system, you have an enormous advantage over your competitors.”


 

This is hardly limited to political organizations. Eric Siegel, author of the new book Predictive Analytics and founder of Predictive Analytics World, a gathering of predictive data experts, says that by leveraging the data they collect, “organizations attain a position of power: They learn from the data how to predict human behavior.”

 

A company with hundreds of thousands of customer records, for example, “can learn from the experience encoded in this data. It’s a kind of pattern-detection that can help them discover which combinations of factors about a customer makes the individual much more likely than average to cancel.”  

 

These factors aren’t always obvious or intuitive, Siegel told The Fiscal Times. “They are signals that reveal odds, even if a customer has not yet begun to formulate any particular plan of action.”

 

Continued… Read the full article here

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March 1st 2013

Deathwatch: Five Reasons Organizations Predict When You Will Die

 

Deathwatch: Five Reasons Organizations Predict When You Will Die

This article was also published on Smart Blogs

Eric Siegel, Ph.D.

Retirement kills more people than hard work ever did.

—Malcolm Forbes

I'm not afraid of death; I just don't want to be there when it happens.

—Woody Allen

Who benefits by predicting your behavior? Organizations do—companies, government agencies, and political campaigns. They employ predictive analytics, technology that learns from data to render per-person predictions, one individual at a time.

The payoff for predicting extends beyond boosting sales and winning elections: everyone benefits when this technology strengthens the fight against risk, crime, and even spam.

In these efforts, each important thing a person does can be valuable to predict, namely click, buy, steal, drop out of school, quit your job, donate, crash your car, or vote.

So how about the final thing each of us do, die? In fact, there are five reasons organization predict your death. Sometimes they do it with altruistic intent, for healthcare-related purposes. In other cases, there's a financial incentive—they predict death for the money.

To begin with, there are two fairly well-known reasons to predict when an individual's death will come:

1. Healthcare: predicts death to help prevent it. For example, Riskprediction.org.uk predicts your risk of death in surgery, based on aspects of you and your condition, in order to help inform medical decisions. In other work, psychiatric research predicts which patients are at the greatest risk of suicide.

2. Life insurance: prices policies according to predicted life expectancy. A growing number of life insurance companies go beyond conventional actuarial tables and employ predictive analytics to establish mortality risk. It's not called death insurance, but their core analytical competency is to calculate when you are going to die.

Beyond life insurance, it turns out health insurance also predicts death—of policyholders. Until recently, death prediction has not been within the usual domain for health insurance. On the surface, given that the ulterior motives of health insurance are at times under scrutiny, one may imagine dubious implications. For what purpose do they predict dying?

We will return to this question—for now, here's a bit more about how death prediction works.

Standard actuarial methods assess mortality risk from a handful of factors such as age, gender, whether the individual smokes and drinks, Body Mass Index, and psychological outlook (e.g., "optimistic"). These are the attributes you may enter—right now, if you like—into http://www.death-clock.org to calculate the Grim Reaper's ETA. This website bases its predictions on data from the World Health Organization.

Predictive analytics extends beyond the limits of standard actuarial methods to incorporate a greater range of factors, and to combine them—for each individual being predicted—by way of more sophisticated mathematical models. In healthcare, for example, a patient's diagnostic codes and lab results provide further predictive oomph. Moving to a wider range of domains, here are a few more colorful examples of risk factors:

Solo rockers die younger than those in bands. Although all rock stars face higher risk, solo rock stars suffer twice the risk of early death as rock band members. This may be due to the fact that band members benefit from peer support and solo artists exhibit even riskier behavior (factoid courtesy of public health offices in the UK).

Men on the Titanic faced much greater risk than women. A woman on the Titanic was almost four times as likely to survive as a man. Most men died and most women lived. This may be due to the fact that priority for access to life boats was given to women.

Retirement is bad for your health. For a certain working category of males in Austria, each additional year of early retirement was shown to decrease life expectancy by 1.8 months.  This may be due to the fact that unhealthy habits such as smoking and drinking follow retirement (factoid courtesy of the University of Zurich).

Some organizations predict when death will arise not by natural causes, by instead by accident, or even intentionally, in the cases of wartime battles and murder.

3. Law enforcement and military: predict kill victims in order to protect. U.S. Armed Forces conduct research to analytically predict terrorist attacks. Researchers also assess the risk to individual soldiers, e.g., when parachuting. Law enforcement in Maryland applies predictive models to detect inmates more at risk to be perpetrators or victims of murder. Further, university and law enforcement researchers have developed predictive models that foretell murder among those previously convicted for homicide.

4. Safety institutes: predict system failure casualties. For example, researchers have identified aviation incidents that are five times more likely than average to be fatal, using data from the National Transportation Safety Board.

We come now to the final item: why would a health insurance company predict death? Fear not, it's actually done for benevolent purposes.

5. A top-five U.S. health insurance company: predicts the likelihood an elderly insurance policy holder will pass away within 18 months in order to trigger end-of-life counseling, e.g., regarding living wills and palliative care. The predictions are based on clinical markers in the insured's recent medical claims.

While the more fortunate elderly are surrounded by caring family fretting about comfort care, many aren't as lucky. In lieu of the doting supervision of family, many nearing the end of life will greatly benefit from pertinent screenings and service offerings, often available only by way of accurate, timely targeting.

Despite the benefits of this work, predicting death is so sensitive that the health insurance company in question must keep its humanitarian activity a secret. An employee of this company told me the predictive performance is strong, and the project is providing clear value for the patients. Despite this, those at the company quake in their boots that the project could go public, agreeing only to speak to me anonymously. "It's a very sensitive issue, easily misconstrued," the employee said.

Given the sensitivity of a predicted passing, some organizations feel it's better not to know. Industry leader John Elder (Elder Research, Inc.) tells of the adverse reaction from one company's human resources department when the idea of predicting employee death was put on the table. Since death is one way to lose an employee, it's in the data mix. In a meeting with a large organization about predicting employee attrition, one of John's staff witnessed a shutdown when someone mentioned the idea. The project stakeholder balked immediately: "Don't show us!" Unlike health care organizations, this human resources group was not meant to handle and safeguard such prognostications.

Nevertheless, whether by accident, murder, or natural causes, organizations have made a science of predicting when we each will die.

But is there prediction after death? It turns out that death is not the final event to be predicted for a life. The Chicago Police Department predicts whether a murder can be solved. The department found that characteristics of a homicide and its victim help predict whether the crime will be solvable.

Eric Siegel, Ph.D., is the founder of Predictive Analytics World (www.pawcon.com)coming in 2013 to Toronto, San Francisco, Chicago, Washington D.C., Boston, Berlin, and Londonand the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (February 2013, published by Wiley). For more information about predictive analytics, see the Predictive Analytics Guide (www.pawcon.com/guide).

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