November 18th 2013

The power to predict who will click, buy, lie or die.

Product Margins

I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by  Shakthi Poornima in Product Margins. Here is an excerpt from the review.

The power to predict who will click, buy, lie or die

Working in the field of Big Data means taking into consideration not hundreds or thousands, but millions, billions, or even bigger datapoints.  And underneath all that data, lies unparalleled potential. Just imagine being able to predict one’s location up to multiple years beforehand by using GPS data (Microsoft), or being able to predict one’s risk of death in surgery ( That’s what the book, “Predictive Analytics: The power to predict who will click, buy, lie or die” is about. It covers building applications in marketing, health care, fraud, finance, human resources., etc by a variety of parties — companies, banks, governments, even universities. Everyone has an interest in data.

…overall, the examples in the book are well-researched. What was interesting to me was the possibility of taking the predictions from various studies to building new products.s For example, Orbitz found that Mac users book more expensive hotels. “Orbitz applies this insight, altering displayed options according to your operating system” (p.81). A different study found that one’s inclination to buy online varies by the time of day:  8pm for retmail, late night for dating, 1pm for finance, and so on. Combining the insights from both studies can come in handy for marketing a new product, or starting an A/B test for that product. The potential for meshing various different types of data grows as different applications are developed around same or similar datasets, and as these datasets grow in size.

Click here to read the full review at

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November 11th 2013

Expanding the Predictable Universe


I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, reviewed by Patrick Tucker in The Furturist. Here is an excerpt from the review.

Expanding the Predictable Universe

Data scientist Eric Siegel explains the brave, new, and surprising world of predictive analytics.

Whenever you go to a major merchandise retailer and pull items off the shelf, you create a little piece of information that the retailer stores in a database. As more people pull items off those shelves, the retailer has the opportunity to learn something about all of you, in real time, and can use that information to predict what you might be interested in buying next. With the emergence of extremely large databases and ever-better transaction records, the relationship between what we buy, where we go, and what we might do next is becoming ever more clear.

In his new book, Predictive Analytics, researcher Eric Siegel refers to this computerized semi-clairvoyance as “the prediction effect.” Siegel achieved some small notoriety in 2012, when New York Times writer Charles Duhigg interviewed him on a story about predictive analytics (PA). Siegel recalls that Duhigg “asked for interesting discoveries that had come from PA. I rattled off a few that included pregnancy prediction.” Siegel directed him to a video from one of the many PA conferences that Siegel runs.

The video was a keynote presentation by data scientist Andrew Pole of Target, discussing how Target used data from its massive baby-registry service to predict pregnancy through consumer habits. For instance, many women, upon discovering that they are pregnant, may put unscented skin lotion on their registries, since pregnancy can dry out skin and scented lotion can have a negative effect on a developing fetus. The switch to unscented baby lotion can serve as one of many predictors of pregnancy—an issue of keen interest to Target, since expectant mothers can become much more profitable customers.

The Target model, in the words of Siegel, “identified 30 percent more customers for Target to contact with pregnancy-oriented marketing material—a significant marketing success story.”

Click here to read the full review at

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November 4th 2013

Review of Predictive Analytics in The Seattle Post-Intelligencer


I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by The Seattle Post-Intelligencer. Here is an excerpt from the review.

Review of Predictive Analytics in The Seattle Post-Intelligencer

Can computers learn? How can computers increase our predictive capacities? If you've always wondered about these questions, Predictive Analytics: The Power to Predict Who will Click, Buy, Lie, or Die is for you!

We seem to be obsessed with prediction. We'd love to predict and know what will happen in our future. We go to palm readers, read our horoscopes daily or weekly, and feast upon fortune cookies to get some idea, however, inaccurate, of what may happen to us in the future.

But is prediction of this sort accurate? Regardless, people are very interested in this type of prediction and will spend any money and effort to achieve it.

Most people don't really know what predictive analytics means and how anyone can be interested in such a mysterious discipline. But after reading Eric Siegel's book, readers will find this a mesmerizing and fascinating study. I know I did! And given my background in philosophy, I was entranced by the book.

Predictive analytics is intuitive, powerful, and awe-inspiring. A little bit of prediction can go a long way towards combatting financial risk, fortifying healthcare, conquering spam, toughing crime fighting, and boosting sales. It can even be used to predict when someone is going to die.

Click here to read the full review at The Seattle

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October 29th 2013

Speaker Proposals Due This Friday for PAW Chicago & PAW Manufacturing

Call for speakers:

PAW Chicago - June 16-19, 2014 (Speaker days are June 17-18)

PAW Manufacturing - June 17-18, 2014

Speaker proposal deadline: November 1, 2013 

Why speak at PAW?

Predictive Analytics World provides speakers the opportunity to present predictive analytics case studies, deployment successes and lessons learned. At this event, potential consumers of predictive analytics witness proof demonstrating it's more than just a bunch of great ideas – predictive analytics is actively applied to optimize many business functions across industry verticals. And predictive analytics practitioners have the opportunity to gain from the lessons you've learned, whether by serendipity, or – more likely – the hard way.

What is PAW Manufacturing?

Predictive Analytics World Manufacturing is a practically-focused conference that highlights case studies of how manufacturing companies are currently using data analytics to solve real world problems.

Don’t delay – submit your proposal today!

Information and submissions:


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October 28th 2013

Learn from Big Data How to Predict the Future

Business Intelligence Software


I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by Doug Lautzenheiser at the Business Intelegence Software blog. Here is an excerpt from the review.

Learn from Big Data How to Predict the Future

Experts believe our collection of Big Data will double every two years until 2020. 

Much of those digital artifacts come from people like you and me as we "Like" things on Facebook, buy books over the web, post blog entries, and share smartphone photos on Instagram. Yet only a fraction of this data is actually being used.  

So what should we do with it?

Eric Siegel says that most valuable thing we can do with data is to "learn from it how to predict."

The founder of the Predictive Analytics World conference, Dr. Siegel is also the author of the bestselling book, "Predictive Analytics," with the catchy subtitle of "The Power to Predict Who Will Click, Buy, Lie, or Die." 

I read his work right on the heals of taking a Coursera MOOC on Data Analysis and was pleased to get Siegel's common-sense clarifications of the same academic topics.

Throughout the book, Siegel provides real-life examples of how organizations use data and software to infer something unknown, perhaps imperfectly but often with surprising accuracy.

For example, Siegel covers how the retail giant Target Corporation uses predictive analytics to decide which of its shoppers might be pregnant and how financial services giant Chase predicts which customers might pay off mortgages early (good for the homeowner but bad for Chase since they lose interest payments).

Click here to read the full review at Business Intelligence Software blog.

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October 21st 2013

Are We Puppets in a Wired World?

I was honored to have my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by Sue Halpern in The New York Review of Books. Here is an excerpt from the review.

… In other words, you are not only what you eat, you are what you are thinking about eating, and where you 've eaten, and what you think about what you ate, and who you ate it with, and what you did after dinner and before dinner and if you 'll go back to that restaurant or use that recipe again and if you are dieting and considering buying a Wi-Fi bathroom scale or getting bariatric surgery and you are all these things not only to yourself but to any number of other people, including neighbors, colleagues, friends, marketers, and National Security Agency contractors, to name just a few. According to the Oxford professor Viktor Mayer-Schönberger and Kenneth Cukier, the "data editor" of The Economist, in their recent book Big Data:

Google processes more than 24 petabytes of data per day, a volume that is thousands of times the quantity of all printed material in the US library of Congress … Facebook members click a "like" button or leave a comment nearly three billion times per day, creating a digital trail that the company can mine to learn about users' preferences.

How all this sharing adds up, in dollars, is incalculable because the social Web is very much alive, and we keep supplying more and more personal information and each bit compounds the others. Eric Siegel in his book Predictive Analytics notes that "a user 's data can be purchased for about half a cent, but the average user's value to the Internet advertising ecosystem is estimated at $1,200 per year." Just how this translates to the bottom line is in many cases unclear, though the networking company Cisco recently projected that the Internet will be worth $14.1 trillion by 2022.

For the moment, however, the crucial monetary driver is not what the Internet will be worth, it's the spread between what it costs to buy personal information (not much) and how much can be made from it…

Click here to read the full review in The New York Review of Books

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October 14th 2013

A tale of two books on decision-making


I was honored to have my book,  Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die reviewed by Vijay Mehrotra — check it out.

A tale of two books on decision-making

By Vijay Mehrotra

Daniel Kahneman is a psychologist who was awarded the 2002 Nobel Prize for his influence on the burgeoning field of behavioral economics. I recently read his bestselling 2011 book “Thinking Fast and Slow” [1]. The book begins with a set of chapters collectively entitled “Two Systems.” This is where the book’s title comes from: System 1 [the “Thinking Fast” from the book’s title] “operates automatically and quickly, with little or no effort and no sense of voluntary control,” while System 2 [“Thinking Slow”] is engaged in “the effortful mental activities that demand it, including complex computations …” [2].

Kahneman then proceeds to illustrate how these Systems interact. He presents several examples in which System 1’s assessment processes are simplistic and biased. System 2, while capable of making much better decisions, is shown to be “lazy” as a result of the volume and variety of demands that leave it in a busy and depleted state. The tendency toward lazy System 2 processes, it turns out, is also why so many people turn out to be quite unskilled at probabilistic reasoning and associated decision-making; it is simply much, much easier for System 1’s automatic (and often incorrect) heuristics to be deployed than for System 2 to break away from its many other demands.

My System 2 was exhausted by the time I finished “Thinking,” so I simply started reading the next book that was sitting on my nightstand, which was Eric Siegel’s “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” [3]. Siegel is a former computer science professor, an experienced analyst and more recently the founder of the Predictive Analytics World conference series. As its title suggests, he has written a book that focuses on data-driven predictions, which he collectively labels as “predictive analytics” (PA).

The centerpiece, or rather centerfold, of the book is a list of more than 100 success stories that involve PA, grouped into categories ranging from “Financial Risk and Insurance” to “Family and Personal Life.” In turn, each chapter tells its own tale through these PA success stories. For example, in the chapter that explores the ethical and privacy implications of using data for prediction (“With Power Comes Responsibility”), Siegel illustrates the key ideas through the story of HP’s model for predicting the likelihood of employees leaving the company and Target’s algorithm and processes for predicting which customers were likely to be pregnant, while in the last chapter (“Persuasion by the Numbers”) he shines a bright light on U.S. Bank, Telenor (a Norwegian telecommunications company) and the Obama 2012 campaign.

Click here to read the full review in


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October 7th 2013

Why I Became a Believer in Artificial Intelligence

big think

Big Think originally published this transcript of my own words. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Why I Became a Believer in Artificial Intelligence

I’ve been asked periodically for a couple of decades whether I think artificial intelligence is possible.  And I taught the artificial intelligence course at Columbia University.  I’ve always been fascinated by the concept of intelligence.  It’s a subjective word.  I’ve always been very skeptical. And I am only now newly a believer.  

Now this is subjective: my opinion is that IBM’s Watson computer is able to answer questions, and so, in my subjective view, that qualifies as intelligence.  I spent six years in graduate school working on two things.  One is machine learning and that’s the core to prediction – learning from data how to predict.  That’s also known as predictive modeling.  And the other is natural language processing or computational linguistics.  

Working with human language really ties into the way we think and what we’re capable of doing and that does turn out to be extremely hard for computers to do.  Now playing the TV quiz show Jeopardy means you're answering questions – quiz show questions.  The questions on that game show are really complex grammatically.  And it turns out that in order to answer them Watson looks at huge amounts of text, for example, a snapshot of all the English speaking Wikipedia articles.  And it has to process text not only to look at the question it’s trying to answer but to retrieve the answers themselves.  Now at the core of this it turns out it’s using predictive modeling.  Now it’s not predicting the future but it’s predicting the answer to the question. 

The core technology is the same.  In both cases it involves learning from examples.  In the case of Watson playing the TV show Jeopardy it takes hundreds of thousands of previous Jeopardy questions from the TV show having gone on for decades and learns from them.  And what it’s learning to do is predict whether this candidate answer to this question is likely to be the correct answer.  So it’s going to come up with a whole bunch of candidate answers, hundreds of candidate answers, for the one question at hand at any given point in time.  And then amongst all these candidate answers it’s going to score each one.  How likely is it to be the right answer?  And, of course, the one that gets the highest score as the highest vote of confidence – that’s ultimately the one answer it’s going to give.   

Click here to read the rest of this transcript at

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October 1st 2013

Data Mine or Data Yours? Info Wars and the Escalating Arms Race



I originally published this article in Expert Marketer Magazine. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

Data matters.  It’s the very essence of what we care about. 

Personal data is not equivalent to a real person—it’s much better. It takes no space, costs almost nothing to maintain, lasts forever, and is far easier to replicate and transport. Data is worth more than its weight in gold—certainly so, since data weighs nothing; it has no mass.

Data about a person is not as valuable as the person, but since the data is so much cheaper to manage, it’s a far better investment. Alexis Madrigal, senior editor at The Atlantic, points out that a user’s data can be purchased for about half a cent, but the average user’s value to the Internet advertising ecosystem is estimated at US $1,200 per year.

Data’s value—its power, its meaning—is the very thing that also makes it sensitive. The more data, the more power. The more powerful the data, the more sensitive. So the tension we feel around data governance is inevitable. If nobody cared about some piece of data, nobody would try to protect it, and nobody would want to access it or even bother to retain it in the first place. Data mining industry leader John Elder reflects, “The fact that it’s perceived as dangerous speaks to its power; if it were weak, it wouldn’t be a threat.”

Ever since the advent of paper and pen, this has been the story. A doctor scribbled a note, and the battle to establish and enforce access policies began.

But now, digital data travels so far, so fast, between people, organizations, and nations. Combine this ability of data to go anywhere at almost no cost with the intrinsic value of the stuff that’s traveling, and you have the makings of a very fickle beast, a swarm of gremlins impressively tough to control. It’s like trying to incarcerate the X-Men’s superhero Nightcrawler, who has the ability to teleport. It’s not confined to our normal three dimensions of movement, so you just can’t lock it up.

Click here to access the full published article as a PDF


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September 24th 2013

The Risk of Prejudice in Computerized Prediction

I originally published this article in Profiles in Diversity Journal. The article relates to my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

The Risk of Prejudice in Computerized Prediction

Today’s predictive technology introduces a new risk of prejudice on a massive, automated scale. Predictive analytics predicts what each person will do so that companies and government agencies can operate more effectively. In some cases, this technology does drive life-changing decisions. Large organizations like Hewlett-Packard inform human resource decisions by predicting whether each employee is likely to quit, and states such as Oregon and Pennsylvania analytically predict whether each convict will commit crime again (recidivism) in order to make sentencing and parole decisions.

Given this influence on the lives of individuals, predictive analytics introduces a new risk of prejudice in two ways:

1. Prediction of minority status. Fueled with data, computers automatically detect one’s minority status. A new study from the University of Cambridge shows that race, age, and sexual orientation can be accurately determined by one’s Facebook likes. The capacity to predict grants marketers and other researchers access to unvolunteered demographic information. Some such personnel may be keen on managing and using this information appropriately, but have not necessarily been trained to do so.

2. Prediction with minority status. When utilizing predictive analytics, it is difficult to avoid incorporating minority status into the predictive model as one basis of prediction. There is no place this threat is more apparent than in law enforcement, where computers have become respected advisers that have the attention of judges and parole boards.

While science promises to improve the effectiveness of law enforcement, when the organization formalizes and quantifies decision making, it inadvertently instills existing prejudices against minorities. Why? Because prejudice is cyclical, a self-fulfilling prophecy, and this cycling could be intensified by the deployment of predictive analytics.

Click here to read the rest of this article at

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