Predictive Analytics
Table of Contents

Foreword       Thomas H. Davenport xiii
Preface

What is the occupational hazard of predictive analytics?

xv
Introduction
The Prediction Effect

How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, and boost sales? Why must a computer learn in order to predict? How can lousy predictions be extremely valuable? What makes data exceptionally exciting? How is data science like porn? Why shouldn't computers be called computers? Why do organizations predict when you will die?

1
Chapter 1
Liftoff! Prediction Takes Action (deployment)

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?

17
Chapter 2
With Power Comes Responsibility: Hewlett-Packard, Target, and the Police Deduce Your Secrets (ethics)

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 policy holder death? An extended sidebar on fraud detection addresses the question: how does machine intelligence flip the meaning of fraud on its head?

37
Chapter 3
The Data Effect: A Glut at the End of the Rainbow (data)

We are up to our ears in data, but how much can this raw material really tell us? What actually makes it predictive? Does existing data go so far as to reveal the collective mood of the human populace? If yes, how does our emotional online chatter relate to the economy's ups and downs?

67
Color Book Insert
147 Examples of Predictive Analytics

A cross-industry compendium of 147 mini-case studies in predictive analytics, divided by vertical:

  • Personal Life
  • Marketing
  • Finance
  • Healthcare
  • Crime Fighting and Fraud Detection
  • Reliability Modeling
  • Government and Nonprofit
  • Human Language and Thought
  • Human Resources

 
Chapter 4
The Machine That Learns: A Look Inside Chase's Prediction of Mortgage Risk (modeling)

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?

103
Chapter 5
The Ensemble Effect: Netflix, Crowdsourcing, and Supercharging Prediction (ensembles)

To crowdsource 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 crowdsourcing helped develop? Must supercharging predictive precision involve overwhelming complexity, or is there an elegant solution? Is there wisdom in nonhuman crowds?

133
Chapter 6
Watson and the Jeopardy! Challenge (question answering)

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?

151
Chapter 7
Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift)

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?

187
Afterword

Ten Predictions for the First Hour of 2020

218
Appendices
A. Five Effects of Prediction 221
B. Twenty-One Applications of Predictive Analytics 222
C. Prediction People—Cast of "Characters" 225
Notes 228
Acknowledgments 290
About the Author 292
Index 293

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

Praise for the Book:

"Littered with lively examples..."

— The Financial Times

"A mesmerizing and fascinating study."

— The Seattle Post-Intelligencer

"Siegel is a capable and passionate spokesman with a compelling vision"

— Analytics Magazine

"A must read for the normal lay person"

— Journal of Marketing Analytics

"This is Moneyball for business, government, and healthcare."

— Jim Sterne, founder, eMetrics Summit; chairman, Digital Analytics Association

"Simultaneously entertaining, informative, and nuanced. Siegel goes behind the hype and makes the science exciting."

— Rayid Ghani, Chief Data Scientist, Obama for America 2012 Campaign

"Predictive Analytics is not only a deeply informative dive into a topic that is critical to virtually every sector of business today, it is also a delight to read."

— Geoffrey Moore, author, Crossing the Chasm

"The future is right now — you're living in it. Read this book to gain understanding of where we are and where we're headed."

— Roger Craig, record-breaking analytical Jeopardy! champion; CEO, Cotinga

"The Freakonomics of big data."

— Stein Kretsinger, founding executive of Advertising.com; former lead analyst at Capital One

"A clear and compelling explanation of the power of predictive analytics, and how it can transform companies and even industries."

— Anthony Goldbloom, Founder and CEO, Kaggle.com

"Both sophisticated and fully accessible to the non-quantitative reader. It's got great stories, great illustrations, and an entertaining tone."

— From the book's foreword by Thomas H. Davenport, coauthor, Competing on Analytics

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