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

Book Description

"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.

How? Prediction is powered by the world's most potent, booming 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 — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.

In this rich, entertaining primer, 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 are even aware of it themselves.

  • Why early retirement decreases life expectancy and vegetarians miss fewer flights.

  • Five reasons why organizations predict death, including one health insurance company.

  • How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual.

  • 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 who stays in prison and who goes free.

  • What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia.

A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.

Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?

Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Table of Contents

  1. Cover
  2. Contents
  3. Title
  4. Copyright
  5. Dedication
  6. Foreword
  7. Preface
  8. Introduction: The Prediction Effect
  9. Chapter 1: Liftoff! Prediction Takes Action (deployment)
    1. Going Live
    2. A Faulty Oracle Everyone Loves
    3. Predictive Protection
    4. A Silent Revolution Worth a Million
    5. The Perils of Personalization
    6. Deployment’s Detours and Delays
    7. In Flight
    8. Elementary, My Dear: The Power of Observation
    9. To Act Is to Decide
    10. A Perilous Launch
    11. Houston, We Have a Problem
    12. The Little Model That Could
    13. Houston, We Have Liftoff
    14. A Passionate Scientist
    15. Launching Prediction into Inner Space
  10. Chapter 2: With Power Comes Responsibility (ethics)
    1. The Prediction of Target and the Target of Prediction
    2. A Pregnant Pause
    3. My 15 Minutes
    4. Thrust into the Limelight
    5. You Can’t Imprison Something That Can Teleport
    6. Law and Order: Policies, Politics, and Policing
    7. The Battle over Data
    8. Data Mining Does Not Drill Down
    9. HP Learns about Itself
    10. Insight or Intrusion?
    11. Flight Risk: I Quit!
    12. Insights: The Factors behind Quitting
    13. Delivering Dynamite
    14. Don’t Quit While You’re Ahead
    15. Predicting Crime to Stop It Before It Happens
    16. The Data of Crime and the Crime of Data
    17. Machine Risk without Measure
    18. The Cyclicity of Prejudice
    19. Good Prediction, Bad Prediction
    20. The Source of Power
  11. Chapter 3: The Data Effect (data)
    1. The Data of Feelings and the Feelings of Data
    2. Predicting the Mood of Blog Posts
    3. The Anxiety Index
    4. Visualizing a Moody World
    5. Put Your Money Where Your Mouth Is
    6. Inspiration and Perspiration
    7. Sifting Through the Data Dump
    8. The Instrumentation of Everything We Do
    9. Batten Down the Hatches: T.M.I.
    10. The Big Bad Wolf
    11. The End of the Rainbow
    12. Prediction Juice
    13. Far Out, Bizarre, and Surprising Insights
    14. Correlation Does Not Imply Causation
    15. The Cause and Effect of Emotions
    16. A Picture Is Worth a Thousand Diamonds
    17. Validating Feelings and Feeling Validated
    18. Serendipity and Innovation
    19. Investment Advice from the Blogosphere
    20. Money Makes the World Go ‘Round
    21. Putting It All Together
  12. Chapter 4: The Machine That Learns (modeling)
    1. Boy Meets Bank
    2. Bank Faces Risk
    3. Prediction Battles Risk
    4. Risky Business
    5. The Learning Machine
    6. Building the Learning Machine
    7. Learning from Bad Experiences
    8. How Machine Learning Works
    9. Decision Trees Grow on You
    10. Computer, Program Thyself
    11. Learn Baby Learn
    12. Bigger Is Better
    13. Overlearning: Assuming Too Much
    14. The Conundrum of Induction
    15. The Art and Science of Machine Learning
    16. Feeling Validated: Test Data
    17. Carving Out a Work of Art
    18. Putting Decision Trees to Work for Chase
    19. Money Grows on Trees
    20. The Recession—Why Microscopes Can’t Detect Asteroid Collisions
    21. After Math
  13. Chapter 5: The Ensemble Effect (ensembles)
    1. Casual Rocket Scientists
    2. Dark Horses
    3. Mindsourced: Wealth in Diversity
    4. Crowdsourcing Gone Wild
    5. Your Adversary Is Your Amigo
    6. United Nations
    7. Meta-Learning
    8. A Big Fish at the Big Finish
    9. Collective Intelligence
    10. The Wisdom of Crowds . . . of Models
    11. A Bag of Models
    12. Ensemble Models in Action
    13. The Generalization Paradox: More Is Less
    14. The Sky’s the Limit
  14. Chapter 6: Watson and the Jeopardy! Challenge (question answering)
    1. Text Analytics
    2. Our Mother Tongue’s Trials and Tribulations
    3. Once You Understand the Question, Answer It
    4. The Ultimate Knowledge Source
    5. Artificial Impossibility
    6. Learning to Answer Questions
    7. Walk Like a Man, Talk Like a Man
    8. A Better Mousetrap
    9. The Answering Machine
    10. Moneyballing Jeopardy!
    11. Amassing Evidence for an Answer
    12. Elementary, My Dear Watson
    13. Mounting Evidence
    14. Weighing Evidence with Ensemble Models
    15. An Ensemble of Ensembles
    16. Machine Learning Achieves the Potential of Language Processing
    17. Confidence without Overconfidence
    18. The Need for Speed
    19. Double Jeopardy!—Would Watson Win?
    20. Jeopardy! Jitters
    21. For the Win
    22. After Match: Honor, Accolades, and Awe
    23. Iambic IBM AI
    24. Predict the Right Thing
  15. Chapter 7: Persuasion by the Numbers (uplift)
    1. Churn Baby Churn
    2. Sleeping Dogs
    3. A New Thing to Predict
    4. Eye Can’t See It
    5. Perceiving Persuasion
    6. Persuasive Choices
    7. Business Stimulus and Business Response
    8. The Quantum Human
    9. Predicting Influence with Uplift Modeling
    10. Banking on Influence
    11. Predicting the Wrong Thing
    12. Response Uplift Modeling
    13. The Mechanics of Uplift Modeling
    14. How Uplift Modeling Works
    15. The Persuasion Effect
    16. Influence Across Industries
    17. Immobilizing Mobile Customers
  16. Afterword: Ten Predictions for the First Hour of 2020
  17. Appendices
    1. Appendix A: Five Effects of Prediction
    2. Appendix B: Twenty-One Applications of Predictive Analytics
    3. Appendix C: Prediction People—Cast of “Characters”
  18. Notes
  19. Acknowledgments
  20. About the Author
  21. Index