Predictive Analytics

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

Eric Siegel

• Publisher: Wiley
• Year: 2016
• Print ISBN: 9781119145677
• eBook ISBN: 9781119153658
• Edition: 2
• Topic: Business
• Subtopic: Consumer Behaviour
• Index: Business

"Mesmerizing & fascinating..." — The Seattle Post-Intelligencer

"The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com

Award-winning | Used by over 30 universities | Translated into 9 languages

An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a "how to" for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning)unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction:

• What type of mortgage risk Chase Bank predicted before the recession.
• Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves.
• Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights.
• Five reasons why organizations predict death — including one health insurance company.
• How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual.
• Why the NSA wants all your data: machine learning supercomputers to fight terrorism.
• How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy!
• How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job.
• How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison.
• 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.

How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more.

A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Chapter 1
Liftoff! Prediction Takes Action

How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain? What happens when a man invests his entire life savings into his own predictive stock market trading system? Launching predictive analytics means to act on its predictions, applying what's been learned, what's been discovered within data. It's a leap many take—you can't win if you don't play.

In the mid-1990s, an ambitious postdoc researcher couldn't stand to wait any longer. After consulting with his wife, he loaded their entire life savings into a stock market prediction system of his own design—a contraption he had developed moonlighting on the side. Like Dr. Henry Jekyll imbibing his own untested potion in the moonlight, the young Dr. John Elder unflinchingly pressed “go.”

There is a scary moment every time new technology is launched. A spaceship lifting off may be the quintessential portrait of technological greatness and national prestige, but the image leaves out a small group of spouses terrified to the very point of psychological trauma. Astronauts are in essence stunt pilots, voluntarily strapping themselves in to serve as guinea pigs for a giant experiment, willing to sacrifice themselves in order to be part of history.

From grand challenges are born great achievements. We've taken strolls on our moon, and in more recent years a $10 million Grand Challenge prize was awarded to the first nongovernmental organization to develop a reusable manned spacecraft. Driverless cars have been unleashed—“Look, Ma, no hands!” Fueled as well by millions of dollars in prize money, they navigate autonomously around the campuses of Google and BMW.

Replace the roar of rockets with the crunch of data, and the ambitions are no less far-reaching, “boldly going” not to space but to a new final frontier: predicting the future. This frontier is just as exciting to explore, yet less dangerous and uncomfortable (outer space is a vacuum, and vacuums totally suck). Millions in grand challenge prize money go toward averting the unnecessary hospitalization of each patient and predicting the idiosyncratic preferences of each individual consumer. The TV quiz show Jeopardy! awarded $1.5 million in prize money for a face-off between man and machine that demonstrated dramatic progress in predicting the answers to questions (IBM invested a lot more than that to achieve this win, as detailed in Chapter 6). Organizations are literally keeping kids in school, keeping the lights on, and keeping crime down with predictive analytics (PA). And success is its own reward when analytics wins a political election, a baseball championship, or…did I mention managing a financial portfolio?

Black-box trading—driving financial trading decisions automatically with a machine—is the holy grail of data-driven decision making. It's a black box into which current financial environmental conditions are fed, with buy/hold/sell decisions spit out the other end. It's black (i.e., opaque) because you don't care what's on the inside, as long as it makes good decisions. When working, it trumps any other conceivable business proposal in the world: Your computer is now a box that turns electricity into money.

And so with the launch of his stock trading system, John Elder took on his own personal grand challenge. Even if stock market prediction would represent a giant leap for mankind, this was no small step for John himself. It's an occasion worthy of mixing metaphors. By putting all his eggs into one analytical basket, John was taking a healthy dose of his own medicine.

Before continuing with the story of John's blast-off, let's establish how launching a predictive system works, not only for black-box trading but across a multitude of applications.

Going Live

Learning from data is virtually universally useful. Master it and you'll be welcomed nearly everywhere!

—John Elder

New groundbreaking stories of PA in action are pouring in. A few key ingredients have opened these floodgates:

• wildly increasing loads of data;
• cultural shifts as organizations learn to appreciate, embrace, and integrate predictive technology;
• improved software solutions to deliver PA to organizations.

But this flood built up its potential in the first place simply because predictive technology boasts an inherent generality—there are just so many conceivable ways to make use of it. Want to come up with your own new innovative use for PA? You need only two ingredients.

Each Application of PA Is Defined by:

1. What's predicted: the kind of behavior (i.e., action, event, or happening) to predict for each individual, stock, or other kind of element.
2. What's done about it: the decisions driven by prediction; the action taken by the organization in response to or informed by each prediction.

Given its open-ended nature, the list of application areas is so broad and the list of example stories is so long that it presents a minor data-management challenge in and of itself! So I placed this big list (182 examples total) into nine tables in the center of this book. Take a flip through to get a feel for just how much is going on. That's the sexy part—it's the “centerfold” of this book. The Central Tables divulge cases of predicting: stock prices, risk, delinquencies, accidents, sales, donations, clicks, cancellations, health problems, hospital admissions, fraud, tax evasion, crime, malfunctions, oil flow, electricity outages, approvals for government benefits, thoughts, intention, answers, opinions, lies, grades, dropouts, friendship, romance, pregnancy, divorce, jobs, quitting, wins, votes, and more. The application areas are growing at a breakneck pace.

Within this long list, the quintessential application for business is the one covered in the Introduction for mass marketing:

PA Application: Targeting Direct Marketing

1. What's predicted: Which customers will respond to marketing contact.
2. What's done about it: Contact customers more likely to respond.

As we saw, this use of PA illustrates The Prediction Effect.

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The Prediction Effect: A little prediction goes a long way.

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Let's take a moment to see how straightforward it is to calculate the sheer value resulting from The Prediction Effect. Imagine you have a company with a mailing list of a million prospects. It costs $2 to mail to each one, and you have observed that one out of 100 of them will buy your product (i.e., 10,000 responses). You take your chances and mail to the entire list.

If you profit $220 for each rare positive response, then you pocket:

$$\begin{array}{lll}\text{Overall profit}\hfill & =\hfill & \text{Revenue}-\text{Cost}\hfill \\ \hfill & =\hfill & (\$220\times \mathrm{10,000}\text{responses})-(\$2\times 1\text{million})\hfill \end{array}$$

Whip out your calculator—that's $200,000 profit. Are you happy yet? I didn't think so.

If you are new to the arena of direct marketing (welcome!), you'll notice we're playing a kind of wild numbers game, amassing great waste, like one million monkeys chucking darts across a chasm in the general direction of a dartboard. As turn-of-the-century marketing pioneer John Wanamaker famously put it, “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” The bad news is that it's actually more than half; the good news is that PA can learn to do better.

A Faulty Oracle Everyone Loves

The first step toward predicting the future is admitting you can't.

—Stephen Dubner, Freakonomics Radio, March 30, 2011

The “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.

—Nate Silver, The Signal and the Noise: Why So ManyPredictions Fail—but Some Don't

Your resident “oracle,” PA, tells you which customers are most likely to respond. It earmarks a quarter of the entire list and says, “These folks are three times more likely to respond than average!” So now you have a short list of 250,000 customers of whom 3 percent will respond—7,500 responses.

Oracle, shmoracle! These predictions are seriously inaccurate—we still don't have strong confidence when contacting any one customer, given this measly 3 percent response rate. However, the overall IQ of your dart-throwing monkeys has taken a real boost. If you send mail to only this short list then you profit:

$$\begin{array}{lll}\text{Overall profit}\hfill & =\hfill & \text{Revenue}-\text{Cost}\hfill \\ \hfill & =\hfill & (\$220\times \mathrm{7,500}\text{responses})-(\$2\times \mathrm{250,000})\hfill \end{array}$$

That's $1,150,000 profit. You just improved your profit 5.75 times over by mailing to fewer people (and, in so doing, expending fewer trees). In particular, you predicted who wasn't worth contacting and simply left them alone. Thus you cut your costs by three-quarters in exchange for losing only one-quarter of sales. That's a deal I'd take any day.

It's not hard to put a value on prediction. As you can see, even if predictions themselves are generated from sophisticated mathematics, it takes only simple arithmetic to roll up the plethora of predictions—some accurate, and others not so much—and reveal the aggregate bottom-line effect. This isn't just some abstract notion; The Prediction Effect means business.

Predictive Protection

Thus, value has emerged from just a little predictive insight, a small prognostic nudge in the right direction. It's easy to draw an analogy to science fiction, where just a bit of supernatural foresight can go a long way. Nicolas Cage kicks some serious bad-guy butt in the movie Next, based on a story by Philip K. Dick. His weapon? Pure prognostication. He can see the future, but only two minutes ahead. It's enough prescience to do some damage. An unarmed civilian with a soft heart and the best of intentions, he winds up march...

Table of contents

• Cover
• Praise for Predictive Analytics
• Title Page
• Copyright
• Dedication
• Foreword Thomas H. Davenport
• Preface to the Revised and Updated Edition
• Preface to the Original Edition
• Introduction: The Prediction Effect
• Chapter 1: Liftoff! Prediction Takes Action
• Chapter 2: With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA Deduce Your Secrets
• Chapter 3: The Data Effect: A Glut at the End of the Rainbow
• Chapter 4: The Machine That Learns: A Look inside Chase's Prediction of Mortgage Risk
• Chapter 5: The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction
• Chapter 6: Watson and the Jeopardy! Challenge
• Chapter 7: Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence
• Afterword: Eleven Predictions for the First Hour of 2022
• Appendices
• Hands-On Guide: Resources for Further Learning
• Acknowledgments
• About the Author
• 182 Examples of Predictive Analytics: A Cross-Industry Compendium of Mini-Case Studies
• Index
• End User License Agreement