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

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

Eric Siegel

Language: English

Pages: 368

ISBN: 1119145678

Format: PDF / Kindle (mobi) / ePub

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

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

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 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
 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 — and Hillary for America 2016 plans to calculate — 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,, 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.


















predicted to be adversely influenced by contact are avoided. Appendix C Prediction People—Cast of “Characters” Eric Siegel, PhD—this book’s author President of Prediction Impact, Inc. (business-speak for an independent consultant). Founder, Predictive Analytics World. Executive Editor of the Predictive Analytics Times. Former computer science professor at Columbia University. John Elder, PhD Invested his entire life savings into his own predictive stock market trading system (see

pondering what this might be you heard a pin drop, you have your answer. They are both silent. Their mechanics make no sound. Sure, a computer may have a disk drive or cooling fan that stirs—just as one’s noggin may emit wheezes, sneezes, and snores—but the mammoth grunt work that takes place therein involves no “moving parts,” so these noiseless efforts go along completely unwitnessed. The smooth delivery of content on your screen—and ideas in your mind—can seem miraculous.3 They’re both

bill payments in order to manage risk. PREMIER Bankcard: Lowered delinquency and charge-off rates, increasing net by $10+ million. Nonpayment Brasil Telecom (now Oi, which means “hi”): Predicted bad debt to recover US$4 million. DTE Energy: 700 percent increase in net savings (e.g., by preempting charge-offs and decreasing service disconnects). Financial institution: Saved $2.1 million in losses by offering collection deals to those accounts that will otherwise not pay, and not offering to

significantly in its favor—and does so without highly accurate predictions. In fact, its utility withstands quite poor accuracy. If the overall marketing response is at 1 percent, the so-called hot pocket with three times as many would-be responders is at 3 percent. So, in this case, we can’t confidently predict that any one individual customer will respond. Rather, the value is derived from identifying a group of people who—in aggregate—will tend to behave in a certain way. This demonstrates in

coverage was soon ending. He promptly put his camera into the microwave in order for it to break so he could return it. It would inevitably be more cost-effective to avoid triggering such criminal activity than to prosecute for it after the fact. Prompting a cell phone customer to leave can be especially costly because it may trigger a social domino effect: People tend to stick with the same wireless carrier as their friends. One major North American carrier showed that a customer is seven times

Download sample