Predictive Analytics The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and Updated 1st edition by Eric Siegel, Thomas Davenport 1118356853 978-1118356852

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ISBN-10 ‏ : ‎ 1118356853

ISBN-13 ‏ : ‎ 978-1118356852

Author:   Eric Siegel, Thomas H. Davenport

“The Freakonomics of big data.”
—Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital One

This book is easily understood by all readers. Rather than a “how to” for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.

You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, “I knew you were going to do that!” These institutions are seizing upon the power to predict whether you’re going to click, buy, lie, or die.

Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.

 

Predictive Analytics The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and Updated 1st Table of contents:

Chapter 1 Liftoff! Prediction Takes Action (deployment) 23

How much guts does it take to deploy a predictive model into field operation, and what do you stand to gain?Whathappens when aman invests his entire life savings into his own predictive stock market trading system?

Chapter 2 With Power Comes Responsibility: Hewlett-Packard,Target, the Cops, and the NSA Deduce Your Secrets (ethics) 47

How do we safely harness a predictive machine that can foresee job resignation, pregnancy, and crime? Are civil liberties at risk? Why does one leading health insurance company predict policyholder death?Two extended sidebars reveal: 1) Does the government undertake fraud detection more for its citizens or for self-preservation, and 2) for what compelling purpose does the NSA need your data even if you have no connection to crime whatsoever, and can the agency use machine learning supercomputers to fight terrorism without endangering human rights?

Chapter 3 The Data Effect: A Glut at the End of the Rainbow (data) 103

We are upto our ears in data, but how much can this raw material really tell us? What actually makes it predictive? What are the most bizarre discoveries from data? When we find an interesting insight, why are we often better off not asking why? In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy?

Chapter 4 The Machine That Learns: A Look inside Chase’s Prediction of Mortgage Risk (modeling) 147

What form of risk has the perfect disguise? How does prediction transform risk to opportunity? What should all businesses learn from insurance companies? Why does machine learning require art in addition to science? What kind of predictive model can be understood by everyone? How can we confidently trust a machine’s predictions? Why couldn’t prediction prevent the global financial crisis?

Chapter 5 The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles) 185

To crowd source predictive analytics—outsource it to the public at large—a company launches its strategy, data, and research discoveries into the public spotlight. How can this possibly help the company compete? What key innovation in predictive analytics has crowd sourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds?

Chapter 6 Watson and the Jeopardy! Challenge (question answering) 207

How does Watson—IBM’s Jeopardy!-playing computer—work? Why does it need predictive modeling in order to answer questions, and what secret sauce empowers its high performance? How does the iPhone’s Siri compare? Why is human language such a challenge for computers? Is artificial intelligence possible?

Chapter 7 Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 251

What is the scientific key to persuasion? Why does some marketing fiercely backfire? Why is human behavior the wrong thing to predict? What should all businesses learn about persuasion from presidential campaigns? What voter predictions helped Obama win in 2012 more than the detection of swing voters? How could doctors kill fewer patients inadvertently? How is a person like a quantum particle? Riddle: What often happens to you that cannot be perceived and that you can’t even be sure has happened afterward—but that can be predicted in advance?

 

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Tags: Predictive Analytics, The Power, Will Click, Thomas Davenport, Eric Siegel