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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die Hardcover – February 19, 2013
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From the Publisher
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Q & A with Author Eric Siegel
Did Nate Silver use predictive analytics to forecast Obama's 2012 election?
No—but Obama did use predictive analytics to help get elected. Nate Silver made election forecasts for each state as a whole: which way would a state trend, overall? In the meantime, the Obama campaign was using predictive analytics to render per-voter predictions. Moving beyond forecasting, true power comes in influencing the future rather than speculating on it—the raison d'être of predictive analytics. Nate Silver publicly competed to win election forecasting, while Obama's analytics team quietly competed to win the election itself. Specifically, team Obama drove per-voter campaign decisions by way of per-vote predictions.
Why does early retirement predict a shorter life expectancy & why do vegetarians miss fewer flights?
These are two more colorful examples of the multitudes of predictive discoveries waiting within data.
University of Zurich discovered that, for a certain working category of males in Austria, each additional year of early retirement decreases life expectancy by 1.8 months. They conjecture that this could be due to unhealthy habits such as smoking and drinking following retirement.
One airline discovered that customers who preorder a vegetarian meal are more likely to make their flight, with the interpretation that knowledge of a personalized or specific meal awaiting the customer provides an incentive, or establishes a sense of commitment.
Predictive analytics seeks out such predictive connections and then works to see how they may combine together for more precise prediction.
What are the hottest trends in predictive analytics?
There have been many exciting improvements in the core technology of predictive analytics. One is "uplift modeling" (a.k.a. "persuasion modeling"), which predicts influence. ..in order to do influence. The Obama campaign used it to influence voters in the 2012 presidential election; marketing uses it to more adeptly persuade customers; and medicine uses it to better select per-patient treatments. This topic is the focus of the final chapter of this book.
Another hot trend is ensemble models. Like the collective intelligence that spawns the wisdom of a crowd of people, we see the same effect with a crowd of predictive models. Each model alone may be fairly primitive such as a few simple rules, so it gets prediction wrong a lot, as an individual person trying to predict also does. But have them come together as a group and there emerges a new level of predictive performance.
Does the NSA use predictive analytics, and how does that impact the amount of data collected on us?
It's a foregone conclusion that the world's largest spy organization employing the world's largest number of Ph.D. mathematicians considers predictive analytics a strategic priority. Predictive analytics realizes a great potential for law enforcement: The automatic discovery of new suspects. The value of this capability multiplies the incentive to collect increasing amounts of data about civilians. The NSA needs data about everyone, including those of us with no connection to crime whatsoever—not to spy on us but to establish a quantitative baseline. This in turn only amplifies the stakes of the contentious security-versus-privacy debate.
What is the coolest thing predictive analytics has done?
One of the most inspirational accomplishments of predictive analytics is IBM's "Jeopardy!"-playing Watson computer, which triumphed against the all-time human champions on the TV quiz show. The questions can be about most any topic, are intended for humans to answer, and can be complex grammatically. It turns out that predictive modeling is the way in which Watson succeeds in determining the answer to a question: it predicts, "Is this candidate answer the correct answer to this question?" It knocks off one correct answer after another—incredible.
What are companies predicting about me as a customer?
Here are just a few examples:
- Facebook predicts which of 1,500 candidate posts (on average) will be most interesting to you in order to personalize your ordered news feed.
- Microsoft helped develop technology that, based on GPS data, accurately predicts one's location up to multiple years beforehand.
- Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn's parents.
- Tesco (UK) annually issues 100 million personalized coupons at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
- Netflix sponsored a $1 million competition to predict which movies you will like in order to improve movie recommendations.
- One top-five U.S. health insurance company predicts the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
Praise for "Predictive Analytics"
"Mesmerizing & fascinating..."
—The Seattle Post-Intelligencer
"Littered with lively examples..."
—The Financial Times
"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"
""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 most readable (for we laymen) 'big data' book I've come across. By far. Great vignettes/stories."
—Tom Peters, co-author of "In Search of Excellence"
"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: The Story of Watson, the Computer That Will Transform Our World"
"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
""Moneyball" for business, government, and healthcare."
—Jim Sterne, founder, eMetrics Summit; chairman, Digital Analytics Association
- Item Weight : 1.25 pounds
- Hardcover : 320 pages
- ISBN-10 : 1118356853
- ISBN-13 : 978-1118356852
- Product Dimensions : 6.3 x 1.1 x 9.3 inches
- Publisher : Wiley; 1st Edition (February 19, 2013)
- Language: : English
- Best Sellers Rank: #813,201 in Books (See Top 100 in Books)
- Customer Reviews:
Top reviews from the United States
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The book is intentionally not filled with math formulas (which may turn off some) but it focuses more on use cases of how the businesses around you are leveraging the data they already collect through daily operations. It's about how they are gaining a better insight into where their efforts are best spent to maximize their return on investment or capitalize on a previously masked rich subset of their existing customer base.
If you're looking for a technical breakdown of how these algorithms work or are applied there are dozens of other books that Eric recommends as followup (referenced in probably the best notes section of any book I've ever seen).
If you want a taste of the kind of information that you'll find in the book you should look on the Predictive Analytic World website for his keynote speech he did in Boston last year. It's a great book overview and convinced me to purchase the book.
If you already have a decent understanding of predictive analytics this book may not be what you are looking for but it does provide information on a number of sources do delve deeper into the subject. This book deepened my knowledge of predictive analytics and pointed me to a number of sources that I am checking out to learn even more.
Life isn't fair, and people certainly aren't. The ways that they react to things reflects this to a degree that would surprise even the coldest eyed cynic, and there it is- the thing that bothered me so much....but it's best if you face it. There are some pleasant discoveries in here too, but I think the most important aspect is illusion busting. Those sweet daydreams about how things should be, might be exactly what is holding you back.
Forewarned is forearmed, and the information in here is of a hefty caliber. Use it well.
Yes, I did actually buy this book, and it was worth every penny.
Eric Siegal's approach, scope of content and tone are highly accessible so as to invite readers new to the topic into an often confusing and intimidating world, while making good sense of it for us. What we walk away with is a solid understanding of why the topic of predictive analytics has become such a big deal in recent years, and why it will continue to dominate our attention as our collective reliance on data continues to grow.
Top reviews from other countries
Correlation is not Causation. The author debunks the use of judgement and intuition in certain decisions and argues the answer is what the data says, not why. Storytelling is in our nature and this is a difficult leap to make.
While Predictive analytics, in one or guise or another, has been continuing for decades, particularly in insurance, the author skilfully shows how today's computing power is the enabler for both simple, complex and multiple models.
And so what? The author continually links the analysis with an action. It is not analysis for analysis's sake, but a driver for change. This a powerful theme throughout the book.
It's a relatively easy read I strongly recommend the book as an introduction for those with some mathematical/computing background.