Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die Author: Eric Siegel | Language: English | ISBN:
B00BGC2WGQ | Format: PDF
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die 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.
- File Size: 4649 KB
- Print Length: 320 pages
- Publisher: Wiley; 1 edition (February 7, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00BGC2WGQ
- Text-to-Speech: Enabled
X-Ray:
- Lending: Enabled
- Amazon Best Sellers Rank: #13,610 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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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.
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?
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