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

3.6 of 5 stars 3.60  ·  rating details  ·  453 ratings  ·  57 reviews
"The Freakonomics of big data."
Stein Kretsinger, founding executive of; 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, g
Hardcover, 302 pages
Published February 19th 2013 by John Wiley & Sons (first published February 5th 2013)
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"Predictive Analytics" is a summary of the state of the art in using computer models to predict individuals actions. I work in the industry and have developed predictive financial models. This book isn't aimed at people like me, at least not ones looking for a more technical, how-to explanation. Instead, this is more a survey of the field, including plentiful real-world examples and some high-level definitions. The definitions of lift, ensemble modeling, and uplift modeling I found new and inter ...more

Having no previous knowledge of predictive analytics, I was a little afraid this book might leave me bewildered. How wrong I was! My eyes were opened, my interest caught and held throughout this fascinating book.

There are many questions that come to mind when reading this book, but as you read on they are all very effectively answered by the author.

Predictive analytics are rooted in everyone’s daily lives and can have a substantial effect on their future actio
Paul DeBusschere
This book is extremely introductory, which accounts for Siegel's 50,000-foot view of the topic. Yet, I came away feeling there could have been more details on the "how" of predictive analytics without destroying the book's aim of being an overview.

Rather than droning on about IBM's Watson, I thought Siegel could have spent a little more time explaining the logic behind building decision trees and preparing the training data. Instead, we get about 100 pages of fluff out of a 217-page text. A typ
Frank Doosey
Having taken (and taught) persuasion courses in college, it was very interesting to see how far the field has come and how openly it has embraced technology. PA is a much larger field than I had imagined. Its uses, for both good AND evil, offer a lot of insight on ourselves and others, and how we unwittingly give ourselves away more than we think.
Shawn Buckle
As more and more companies try to harness the power of 'Big Data' - the latest business buzz word - books like Siegel's are helpful to get a grasp on just what it is. This book is less 'how to' than an attempt to explain what it is, and how it can work for you, with the latter point venturing a bit too close to hucksterism at times (hey, it is Siegel's field). Siegel does a good job explaining how valuable data is and convinces us that with smart, predictive modeling, data can change how we mark ...more
Phil Simon
Disclaimer: I received a complementary media copy, but I wouldn't endorse this book if I didn't like it.


Far too many business books are boring, especially if they fall under the 'technology' umbrella. There. I said it. They're either dry or overly theoretical or deprived of meaningful examples.

Not this one.

In Predictive Analytics, Eric Siegel presents a powerful trove of real-world examples on how organizations are turning Big Data into meaningful metrics. This is the type of book you have t
Nov 26, 2014 Derek rated it 5 of 5 stars
Shelves: 2014
This book was full of great examples and was written in a humorous and approachable way. I am somewhat confused by the reviews that say The Signal and The Noise by Nate Silver was better - I found that book desperately in need of an editor. The focus of The Signal and the Noise was also broader, explaining basic statistics, correlation vs. causation, etc. Predictive Analytics was a focused book filled with examples of PA being used successfully.

The cover art is awful and the font size a bit too
Eka Aulia
It's an easy reading book for quite heavy topic. Even though most of the topics are not new to me but at least it taught me how to explain predictive analytics in easy term.

One thing that I learn most is the last chapter about uplift modeling: not predicting the response, but only focusing those who can be influence through contact.

Overall, it's a recommended book for business leaders who wants to double or triple their ROE using analytics.
This is a nice, entertaining book that gives someone an overview about what big data analytics are and how they can be used. it is written so that a novice can understand. I enjoyed the quirky quotes and the various case studies. That said, this book will not help someone who wants to delve deeper into how to actually create these algorithms.
I didn't like this book as much as Nate Sliver's The signal and the noise book. The two both cover the topic of prediction but this book is more narrowly focused on the idea of predictive analytics. Siegel's book discusses a lot of different applications but there is not enough meat to the examples to really understand what is being done to build the analytical model for the particular application. So this would be a good book for someone to learn of different areas to which analytics can be app ...more
I wish I could have predicted how much I would dislike this book. After reading just one chapter of Nate Silver's The Signal and the Noise this book comes across as amateurish. Too much noise, not enough signal.
Great layman's version of who is gathering what about YOU.
Serves to further Orwell and other genius dystopians who saw the writing on the wall before there was any writing on the wall.
Amazing introduction to the world of Big Data and predictive analytics. I didn't have much knowledge/experience previously, but this book provoked interest in me.
Fraser Cook
Accessible and interesting introduction into to predictive analytics/data mining.
Rusi Kolev
A bit overwhelming, but I plan on finishing it ;)
Correlation far more important than causation, so long as the correlations are good.

Machine learning proceeds from induction, reasoning from detailed facts to general principles.

Though not mentioned by name, the Canadian Tire spending analysis that included Sharky's was referenced!

Chapter on Ensemble models and IBM's Watson playing Jeopardy! was very interesting. The language and idiom barrier that had to be overcome to answer the questions was massive, researchers used many models in concert.
Mark Oppenlander
Here's an entertaining book on a trendy topic: predictive analytics. If you've heard the terms big data, data mining or data analytics and want to know what all of the hype is about, this is a good place to start.

Eric Siegel is one of the experts in this field and he clearly has a passion for this topic. Dr. Siegel takes the reader through a basic understanding of what predictive analytics. First, he describes how the incredible wealth of data generated by our ever increasing use of information
Hourann Bosci
The content is actually a good, high-level, non-technical overview of the field and the ways data can be used in business.

But the writing. Oh goodness, the writing. So many paragraphs feel like the work of a high-schooler just out of "essays 101". Chapters begin with word clouds and quotes (which normally make me shy from a book), and some of those quotes are from the author! I had to force myself to make it to the end.
Mad Hab
This is probably the best book i have read about Big Data. I love the way how the cases were organized and presented- "what is predicted, what is done about it" approach was really cool. Altought i am a statistican, i liked the fact that book is not overwhelmed with statistics and theory, whatever was given, was easy to read and understand.
A light read from a business user perspective. Eric Siegel talks about predictive analytics in the book. He took a technology agnostic approach, avoiding the buzz around Big Data but instead gave examples and business use cases of predictive analytics. Some examples include how the prediction of pregnant customers in Target turned into a data privacy nightmare by the media. How Chase segment their mortgages and selling off "bad" mortgages to other banks (of course this was before the sub-prime i ...more
A survey of how machine learning is used in a spectrum of industries and problem domains. It's not intended as a primer (which I would have been more interested in), though it does touch briefly on a couple approaches and gives a small example of a decision tree.

I was hoping for more technical depth. Instead, this book focuses on how businesses are currently using predictive analytics and machine learning. So in that sense it fills a niche - there are certainly lots of other books available on t
William Decker
Predictive Analytics has become a powerful tool that can be used to predict all sorts of behavior. Like any tool, PA in and of itself is neither good nor evil, but can be used for either. Eric Siegel spends most of his book building up a basic knowledge on how PA or machine learning actually works. He also touches on a myriad of ways that our lives are shaped and will be shaped in the future by predictive analytics, but he avoids delving into some of the moral issues that surround data collectio ...more
after first chapters I predicted it won't be my cup of tea. I was right. The book could be compressed to one quarter without significant loss of content.
Kevin Mackey
Good, accessible introduction to what may be the most underused tool in today's business world: predictive analytics
Okay at best. He clearly knows his stuff and has great experience to talk about. He also chooses interesting examples of predictive modeling that he hasn't worked on. But his style is self-absorbed and immature. If you are in the business, you will get something out of reading his book, but you probably won't enjoy it.
Garrett Mccutcheon
If you're at all experienced with data mining or analytics, skip this one. It may be a decent primer for upper-management-types with no experience or exposure, but for practitioners this book covers no new ground.

As far as the writing is concerned, the author seems to want to spend more time with useless sidebars and injecting tenuously-related quotations. He seems particularly enamored with his own poetry and "songwriting". Very little of the text actually involves explaining predictive analyti
Read in conjunction with Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart. Both discuss the use of data and analysis of large data sets. Lots of real world examples - some scary and most interesting ranging from health care to crime to game shows. Clearly written and easy to follow.
Alvaro Berrios
Really great book, you learn so many different ways in which predictive analytics is used. Ways in which you never imagined. On top of that, it's written in a fun, humorous and engaging manner. It truly makes you realize the direction of PA and how it will impact businesses and organizations. In the book's appendix the author also provides a plethora of resources to learn more about PA and how you can become an expert. Anyone who is at least mildly interested in PA should read this book, it just ...more
Heavy on flowery language, light on details; an exciting overview of the power of PA.
a fine introduction to analytics, but doesn't provide much useful depth or context for practitioners at any level
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