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

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die [Kindle Edition]

Eric Siegel , Thomas H. Davenport

Kindle-Preis: EUR 14,99 Inkl. MwSt. und kostenloser drahtloser Lieferung über Amazon Whispernet

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Produktbeschreibungen

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Praise for Predictive Analytics
 
"What Nate Silver did for poker and politics, this does for everything else. A broad, well-written book easily accessible to non-nerd readers."
--DAVID LEINWEBER, author of Nerds on Wall Street: Math, Machines and Wired Markets
 
"This book is an operating manual for twenty-first-century life. Drawing predictions from big data is at the heart of nearly everything, whether it's in science, business, finance, sports, or politics. And Eric Siegel is the ideal guide."
--STEPHEN BAKER, author of The Numerati and Final Jeopardy: Man vs. Machine and the Quest to Know Everything
 
"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
 
"This is Moneyball for business, government, and healthcare."
--JIM STERNE, founder, eMetrics Summit; chairman, Digital Analytics Association
 
"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 of 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

Kurzbeschreibung

"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.


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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)
Amazon.com: 4.2 von 5 Sternen  192 Rezensionen
38 von 44 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Online Data Converts to a Plethora of Predictions 18. Februar 2013
Von connywithay - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
Title: Predictive Analytics - The Power to Predict Who Will Click, Buy, Lie or Die
Author: Eric Siegel
Publisher: John Wiley & Sons, Inc.
ISBN: 978-1-118-35685-2

With the astronomical mass of electronic data collected today, one may be wary of driving a GPS-tracked automobile, texting on a cellphone, purchasing grocery items with a credit card, posting on Facebook, anxiously blogging or clicking a mouse for information on Google. But to Eric Siegel, this collective and easily-available data is fascinating as he compiles, analyzes and predicts in his eye-opening book, "Predictive Analytics - The Power to Predict Who Will Click, Buy, Lie or Die."

In a little over three hundred pages in the hardbound book, Siegel breaks down predictive analytics (aka PA) into seven chapters with an afterword, appendices, notes, acknowledgement, author biography and index. The book is targeted from the small to large business owner, entrepreneurs, other PAers and us common folk who want to further understand how computerized data research is analyzed to predict specified outcomes and scenarios.

Cause and effect charts, illustrations along with a few comics and a glossy centerfold divulge cases of predictions in advertising, finance, healthcare, fraud, insurance, government, employment and personal venues. Some topics discussed explain ways to increase consumer buying, limit bank loan defaulting or paying off, anticipate employees quitting or clients dropping cellphone coverage along with collecting online blogs, social networking and risk information. Each chapter includes sections of "what's predicted" and "what's done about it" to show the correlation of PA and gathered data.

The author explains the art of predicting has five effects that include: a little prediction goes a long way, data is always predictive, induction is reasoning from detailed facts to general principles, ensembles compensate for limitations and persuasion can be predictable through outcomes. Using the predictive models of large corporations such as Target, Hewlett-Packard, Chase Bank, Netflix and Telenor along with John Elder's stock market techniques, Jeopardy!'s Watson computer, Kaggle's competitions, and Obama's second term presidential campaign, one learns the ins and outs of predicting through collecting and interpreting simple to complex data.

By entrusting computers to make decisions, privacy concerns are bought up, prejudices are determined and effects are manipulated when machine learning becomes the translated voice of data. Artificial intelligence can often limit overlearning, crowdsourcing and correlation pitfalls, but will it be able to always correctly interpret language, emotions and feelings of humans as it influences, persuades and molds us?

With even the book's title been subjected to analysis and written sometimes humorously of the writer's own experience of stolen identity and mockery of his geekness, it is an excellent source to any reader that sees computers overtaking and controlling our every move as we continue to be co-dependent on them as we happily benefit from increased information and understanding, attain higher profits and enjoy an easier lifestyle through such a conglomerate of PA data bytes. The only remaining question is how much PA will be gleaned from this book reviewer's post?
25 von 29 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Bringing Predictive Analytics to the masses 2. März 2013
Von Sujit Pal - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
This book is aimed mostly at people who are interested in learning about where (as opposed to how) one can effectively use Predictive Analytics and related technologies such as Machine Learning and Natural Language Processing. There is some high level discussion of algorithms such as linear regression, decision trees, random forests and even a nice discussion about Watson's question answering algorithms. The book has many examples of where Predictive Analytics can and is being used. Some of these are relatively obscure, because companies prefer to make money off these techniques rather than talk about it (and dilute their competitive edge). The narrative is interesting and humorous, and the author shares many anecdotes from his own life, having lived through Predictive Analytics relatively short life-span. Finally, the bibliography/reference section lists URLs that will probably take you months to get through. All in all, a "popular" book aimed at people who are looking into learning about and/or adopting Predictive Analytics rather than established practitioners, but very useful and well written nevertheless.
23 von 28 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Good Stuff! 27. Februar 2013
Von jasonhoward7 - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe|Verifizierter Kauf
I'm an IT guy. When I read and research I want solid concepts and clear explanations. This book has it all without missing a step. Even better, Siegel delivers his insight with humor and inventiveness. This book is jammed packed with real world applications for Predictive Analytics, told with a colorful, dramatic flair. Read it! Good Stuff!
11 von 12 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen very interesting examples of applied PA 24. Juni 2013
Von Prz - Veröffentlicht auf Amazon.com
Format:Kindle Edition|Verifizierter Kauf
Interesting book for beginners and people curious about PA. The examples are explained in easy and understandable way without any technical jargon. I recommend this position as a start point for those who want to apply PA. Great introduction for further more technical readings.
6 von 6 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Before reading this book I did not realize how much PA effects my every day life 4. Juni 2013
Von Ali Julia - Veröffentlicht auf Amazon.com
Format:Gebundene Ausgabe
No matter where you are and what you do it is very likely that your behaviors are stored and used for analysis and predictions of future behaviors. A small improvement in ability to predict how people will behave can result in a big monetary gain for a company... and this means this type of behavioral analysis will continue.

These predictions are call predictive analytics (PA) and if you want to understand how it works and where it is currently used, this book gives clear explanations and explicit examples of where it is used effecting our every day life.

Before reading this book I only thought of this type of analysis in a context of displaying ads on the web or mail SPAM generators targeting people who are more likely to respond better, but this book opened my eyes to many more uses from identify fraud to identifying people who are likely to default on their mortgage.

Some uses of PA are disturbingly invasive from Target trying to predict which customers are likely to become pregnant so they sell them more baby products to Hewlett-Packard predicting which employees are more likely than others to quit their jobs. No matter how you feel about that type of predictions it is useful to know how PA does it, since this activity happens around us every day.

PA is a process by which an organization can learn from the previous experiences. It is uses historical data for modeling of what is more likely to happen in the future. Predictive analytics cannot accurately predict how any one individual will respond but it can predict how a group of people are more likely to behave in an aggregate.

How is PA different from forecasting? Forecasting makes aggregate predictions on a macro level, for example, how will economy fare or which presidential candidate will get more votes in Ohio. Whereas forecasting estimates the total number of ice cream cones to be purchased next week, predictive analytics attempts to tell which individuals will buy those cones.

I found the book not only interesting and rather eye opening - I did not realize the extend to which predictive analytics are used all around us!

Ali Julia review

Contents

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?

Chapter 1
Liftoff! Prediction Takes Action
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?

Chapter 2
With Power Comes Responsibility: Hewlett-Packard, Target, and the Police Deduce Your Secrets
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?

Chapter 3
The Data Effect: A Glut at the End of the Rainbow
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?

Chapter 4
The Machine That Learns: A Look Inside Chase's
Prediction of Mortgage Risk
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 machined predictions? Why couldn't prediction prevent the global financial crisis?

Chapter 5
The Ensemble Effect: Netflix, Crowdsourcing, and
Supercharging Prediction
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 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
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
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?

Afterword
Ten Predictions for the First Hour of 2020
Appendices
A. Five Effects of Prediction
B. Twenty-One Applications of Predictive Analytics
C. Prediction People--Cast of "Characters"
Notes
Acknowledgments
About the Author
Index

Note: I received a copy of this book for review.
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