"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
- Introduction: The Prediction Effect
Chapter 1: Liftoff! Prediction Takes Action (deployment)
- Going Live
- A Faulty Oracle Everyone Loves
- Predictive Protection
- A Silent Revolution Worth a Million
- The Perils of Personalization
- Deployment’s Detours and Delays
- In Flight
- Elementary, My Dear: The Power of Observation
- To Act Is to Decide
- A Perilous Launch
- Houston, We Have a Problem
- The Little Model That Could
- Houston, We Have Liftoff
- A Passionate Scientist
- Launching Prediction into Inner Space
Chapter 2: With Power Comes Responsibility (ethics)
- The Prediction of Target and the Target of Prediction
- A Pregnant Pause
- My 15 Minutes
- Thrust into the Limelight
- You Can’t Imprison Something That Can Teleport
- Law and Order: Policies, Politics, and Policing
- The Battle over Data
- Data Mining Does Not Drill Down
- HP Learns about Itself
- Insight or Intrusion?
- Flight Risk: I Quit!
- Insights: The Factors behind Quitting
- Delivering Dynamite
- Don’t Quit While You’re Ahead
- Predicting Crime to Stop It Before It Happens
- The Data of Crime and the Crime of Data
- Machine Risk without Measure
- The Cyclicity of Prejudice
- Good Prediction, Bad Prediction
- The Source of Power
Chapter 3: The Data Effect (data)
- The Data of Feelings and the Feelings of Data
- Predicting the Mood of Blog Posts
- The Anxiety Index
- Visualizing a Moody World
- Put Your Money Where Your Mouth Is
- Inspiration and Perspiration
- Sifting Through the Data Dump
- The Instrumentation of Everything We Do
- Batten Down the Hatches: T.M.I.
- The Big Bad Wolf
- The End of the Rainbow
- Prediction Juice
- Far Out, Bizarre, and Surprising Insights
- Correlation Does Not Imply Causation
- The Cause and Effect of Emotions
- A Picture Is Worth a Thousand Diamonds
- Validating Feelings and Feeling Validated
- Serendipity and Innovation
- Investment Advice from the Blogosphere
- Money Makes the World Go ‘Round
- Putting It All Together
Chapter 4: The Machine That Learns (modeling)
- Boy Meets Bank
- Bank Faces Risk
- Prediction Battles Risk
- Risky Business
- The Learning Machine
- Building the Learning Machine
- Learning from Bad Experiences
- How Machine Learning Works
- Decision Trees Grow on You
- Computer, Program Thyself
- Learn Baby Learn
- Bigger Is Better
- Overlearning: Assuming Too Much
- The Conundrum of Induction
- The Art and Science of Machine Learning
- Feeling Validated: Test Data
- Carving Out a Work of Art
- Putting Decision Trees to Work for Chase
- Money Grows on Trees
- The Recession—Why Microscopes Can’t Detect Asteroid Collisions
- After Math
Chapter 5: The Ensemble Effect (ensembles)
- Casual Rocket Scientists
- Dark Horses
- Mindsourced: Wealth in Diversity
- Crowdsourcing Gone Wild
- Your Adversary Is Your Amigo
- United Nations
- A Big Fish at the Big Finish
- Collective Intelligence
- The Wisdom of Crowds . . . of Models
- A Bag of Models
- Ensemble Models in Action
- The Generalization Paradox: More Is Less
- The Sky’s the Limit
Chapter 6: Watson and the Jeopardy! Challenge (question answering)
- Text Analytics
- Our Mother Tongue’s Trials and Tribulations
- Once You Understand the Question, Answer It
- The Ultimate Knowledge Source
- Artificial Impossibility
- Learning to Answer Questions
- Walk Like a Man, Talk Like a Man
- A Better Mousetrap
- The Answering Machine
- Moneyballing Jeopardy!
- Amassing Evidence for an Answer
- Elementary, My Dear Watson
- Mounting Evidence
- Weighing Evidence with Ensemble Models
- An Ensemble of Ensembles
- Machine Learning Achieves the Potential of Language Processing
- Confidence without Overconfidence
- The Need for Speed
- Double Jeopardy!—Would Watson Win?
- Jeopardy! Jitters
- For the Win
- After Match: Honor, Accolades, and Awe
- Iambic IBM AI
- Predict the Right Thing
Chapter 7: Persuasion by the Numbers (uplift)
- Churn Baby Churn
- Sleeping Dogs
- A New Thing to Predict
- Eye Can’t See It
- Perceiving Persuasion
- Persuasive Choices
- Business Stimulus and Business Response
- The Quantum Human
- Predicting Influence with Uplift Modeling
- Banking on Influence
- Predicting the Wrong Thing
- Response Uplift Modeling
- The Mechanics of Uplift Modeling
- How Uplift Modeling Works
- The Persuasion Effect
- Influence Across Industries
- Immobilizing Mobile Customers
- Afterword: Ten Predictions for the First Hour of 2020
- About the Author
- Title: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
- Release date: February 2013
- Publisher(s): Wiley
- ISBN: 9781118416853