Archive for January, 2013

January 24th 2013

Newsmax TV: Digital Analytics Helped Obama Get Re-Elected

Newsmax TV interviewed me regarding, in their words:

The Obama campaign’s use of computerized data in deciding which potential voters to target helped the president win in November, says Eric Siegel, a former computer science professor at Columbia University.

Watch the video here (and read their write-up about it)

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January 22nd 2013

Team Obama Mastered the Science of Mass Persuasion — And Won

This blog post was also distributed on Big Think, Truthout, The Moderate Voice.

Last October, a colleague and I speculated on how a special, powerful form of predictive analytics would revolutionize presidential campaigning—and, if successful, how it might be poorly received by the public thereafter. In our work, he and I focus more on financial, marketing, and online applications of this technology. But we bet the story would break within politics by 2016 or 2020. 

 

Surprise: There's no wait! After Obama's win in November, we've learned they already did this. The president won reelection with the help of the science of mass persuasion, a very particular, advanced use of predictive analytics, which is technology that produces a prediction for each individual customer, patient, or voter.

 

This is the first story ever of a presidential campaign performing and proving the effectiveness of mass scientific persuasion.

 

The technology's purpose is to predict for each individual, and act on each prediction. But you may be surprised to know what the Obama Campaign analytics team predicted. In this persuasion project, they did not predict:

 

  • Who would vote Obama
  • Who would vote Romney
  • Who would turn out to vote at all

 

… and they didn't even predict:

 

  • Who was "undecided" 

 

Instead, they predicted persuasion:

 

  • Who would be convinced to vote Obama if (and only if) contacted

 

This is the new microcosmic battleground of political campaigns—significantly more refined than the ill-defined concept of "swing voter".

 

Put another way, they predicted for which voters campaign contact would make a difference. Who is influenceable, susceptible to appeal? If a constituent were already destined to vote for Obama, contact would be a waste. If an individual was predicted as more likely swayed towards Obama by contact than not swayed at all, they were added to the "to-contact" list.  Finally, to top it off, if the voter was predicted to be negatively influenced by a knock on the door—a backfired attempt to convince—he or she was removed from the campaign volunteers' contact list: "Do-not-disturb!"

 

I interviewed in detail Rayid Ghani, Chief Data Scientist of Obama for America—who will be keynoting on this work at Predictive Analytics World in San Francisco (April 14-19) and Chicago (June 11-12)—for an article (January 21, 2013 in The Fiscal Times) and book chapter on this topic.

 

To make this possible, team Obama first collected data on how campaign contact (door knocks, calls, direct mail) faired across voters within swing states. Of course, such contact normally helps more than it hurts. But, since the number of volunteers to pound the pavements and dial phones is limited, targeting their efforts where it counts—where contact actually makes a difference—meant more Obama votes. The same army of Obama activists was suddenly much stronger, simply by issuing more intelligent command.

              

Therefore, they used the collected data not just to measure the overall effectiveness of campaigning, but to predict the persuadability of individual swing state constituents. Each person got a score, and the scores drove the army of volunteers' every move.

 

Persuasion modeling (aka uplift modeling or net lift modeling) has been honed in recent years for use in marketing. It's the same principle as for political campaigning, guiding calls and direct mail just the same (although marketing more rarely employs door knocks)—but selling a product rather than a president.

 

I've extensively covered this technology, which is more advanced than "regular" predictive analytics. Normally, you predict human behavior like click, buy, lie, or die (the subtitle of my forthcoming book on the topic). In this case, you predict the ability to influence said behavior.

 

If consumer advocates consider mass marketing a form of manipulation, they may find in this work even more to complain about. Was the election Moneyballed? As mere mortals are we consumers, patients, and voter too susceptible to the invisible powers of advanced mathematics?  Will privacy proponents whip out their favorite adjective-of-concern, creepy? Shouldn't elections be about policies, not number-crunching?

 

No question, the power of persuasion prediction is poignant. Industries are salivating and pouncing.

 

Sometimes this kind of work truly helps the world. Less paper is consumed when direct mail is more focus and consumers receive fewer "junk mail" items. Patients receive predictively improved healthcare. Police patrol more effectively by way of crime prediction. Fraud is similarly detected, several times more effectively. Movie and music recommendations improve.

 

How can this power be harnessed without doing harm? And how is "harm" to be defined in this arena?

 

On a related note, click here for my TV clip deliberating the tricky issue of Target's pregnancy-prediction.

 

More details: my article in The Fiscal Times on this topic

 

 

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January 21st 2013

The Obama Camp Persuaded Millions of Voters with Uplift Modeling

 

The Fiscal Times is running an excerpt from my book, Predictive Analytics, about the Obama campaign's use of uplift modeling (aka net lift or persuasion modeling).

ARTICLE: The Real Story Behind Obama's Election Victory 

By Eric Siegel

Elections hang by a thinner thread than you think.
 
By now you probably know that Barack Obama's 2012 campaign for a second term "moneyballed" the election, employing a team of over 50 analytics experts.
 
You may also know that the huge volume of contentious and costly presidential campaign tactics – executed in the eleventh hour in pursuit of the world's most powerful job – ultimately served only to sway a thin slice of the electorate: swing voters within swing states.
 
But what most people don't realize is that presidential campaigns must focus even more narrowly than that, taking micro-targeting to a whole new level. The Obama campaign got this one right, breaking ground for election cycles to come by applying an advanced form of predictive analytics that pinpoints rare gems: truly persuadable voters.
 
This is the new microcosmic battleground.
 
 
This article is excerpted from my book Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (published by Wiley Feb 18).

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January 13th 2013

Preorder My Book “Predictive Analytics” and Get Free Online Training

 

To drive early orders for my about-to-launch book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (published by Wiley Feb. 18), we're providing the following offer:

 

Preorder now ($15 on Amazon, currently) and receive:

Instructions:

  1. Order the book by Feb 6 (from any online vendor)
  2. Forward your email receipt to admin12@predictionimpact.com

 

Within two business days, you will receive three months of on-demand access to the training module (68 minute video – view a sneak preview now), as well as a 40% discount code for further training (must be used by March 20).

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.

Five reasons this book matters to experts

Read the preface

39 of your colleagues who loved this book

More info

 
Happy reading!

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January 9th 2013

Read the Preface for Siegel’s Book – Predictive Analytics

 

 

Here is the preface for

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die

By Eric Siegel, with a foreword from Tom Davenport

(Wiley, February 2013)

To order the book: www.thepredictionbook.com

Praise:

"What Nate Silver did for poker and politics, this does for everything else. A broad, well-written book easily accessible to non-nerd readers."

– David Leinweber, author, Nerds on Wall Street: Math, Machines and Wired Markets

Preface:

"Yesterday is history, tomorrow is a mystery, but today is a gift. That's why we call it the present."

– Attributed to A.A. Milne, Bill Keane and Oogway, the wise turtle in Kung Fu Panda

 

People look at me funny when I tell them what I do. It's an occupational hazard.

The Information Age suffers from a glaring omission. This claim may surprise many, considering we are actively recording Everything That Happens in the World. Moving beyond history books that document important events, we've progressed to systems that log every click, payment, call, crash, crime, and illness. With this in place, you would expect lovers of data to be satisfied, if not spoiled rotten.

But this apparent infinity of information excludes the very events that would be most valuable to know of: things that haven't happened yet.

Everyone craves the power to see the future; we are collectively obsessed with prediction. We bow to prognostic deities. We empty our pockets for palm readers. We hearken to horoscopes, adore astrology, and feast upon fortune cookies.

But many people who salivate for psychics also spurn science. Their innate response says "yuck" – it's either too hard to understand or too boring. Or perhaps many believe prediction by its nature is just impossible without supernatural support.

Most people have the luxury of describing their job in a single word: doctor, lawyer, waiter, accountant, or actor. But, for me, describing this largely unknown field hijacks the conversation every time. Any attempt to be succinct falls flat…

 

Read the full preface: www.pawcon.com/patimes/december12/#preface

Order the book: www.thepredictionbook.com

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