Predictive Analytics
Table of Contents

Foreword       Thomas H. Davenport xvii

Preface to the Revised and Updated Edition

What's new and who's this book for—the Predictive Analytics FAQ


xxi
Preface to the Original Edition

What is the occupational hazard of predictive analytics?

xxix
Introduction
The Prediction Effect

How does predicting human behavior combat risk, fortify healthcare, toughen crime fighting, boost sales, and cut costs? 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?

23
Chapter 2
With Power Comes Responsibility: Hewlett-Packard, Target, the Cops, and the NSA 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 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 endagering human rights?

47
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? 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?

103
Color Book Insert
182 Examples of Predictive Analytics

A cross-industry compendium of 182 mini-case studies in predictive analytics, divided into these industry groups:

  • Family and Personal Life
  • Marketing, Advertising, and the Web
  • Financial Risk and Insurance
  • Healthcare
  • Law Enforcement and Fraud Detection
  • Fault Detection, Safety, and Logistical Efficiency
  • Government, Politics, Nonprofit, and Education
  • Human Language Understanding, Thought, and Psychology
  • Workforce: Staff and Employees

 
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?

147
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?

185
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?

207
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?

251
Afterword

Eleven Predictions for the First Hour of 2022

291
Appendices
A. Five Effects of Prediction 295
B. Twenty Applications of Predictive Analytics 296
C. Prediction People—Cast of "Characters" 300

Hands-On Guide

Resources for Further Learning


303
Acknowledgments 307
About the Author 311
Index 313

Also see the Central Tables (color insert) for a cross-industry compendium of 182 examples of predictive analytics.

This book's Notes—120 pages of citations and comments pertaining to the chapters above—are available online at www.PredictiveNotes.com.


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

Praise for the Book:

"A mesmerizing and fascinating study."

— The Seattle Post-Intelligencer

"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 Freakonomics of big data."

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

"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

"Littered with lively examples..."

— The Financial Times

"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

"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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