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

Very good for getting new sources of inspiration in the age of Big Data.

A great narrative into the field of Predictive Analytics, not much maths, perhaps for the first foray into this field. 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.

This book do a nice introduction to predictive analytics theme. Very well writen.

Excelent book.

PREDICTIVE ANALYTICS by ERIC SIEGEL 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 actions. I like the way Eric Siegel explains, giving examples that can be related to, so that even a total novice like myself has some insight into this fascinating subject. This book is a must for anyone working in marketing. Even if they have previously explored this area, this book will open their eyes to further insight and could prove to be invaluable. It is also a must for anyone wanting to understand how predictive analytics can work. I particularly liked the chapter on The Data Effect. Predicting the mood of Blog posts was fascinating, as a blogger myself this held my interest. As for the Far Out, Bizarre and Surprising Insights, well you simply have to read it! I devoured every word! Can early retirement really decrease life expectancy? What does your web browsing signify? This book will reveal all and it is written in such a way to hold the readers interest from start to finish. What effect do predictions have on the business world? What predictions do famous names such as Google, Facebook, Citybank and others make? There is so much to discover in this easy to read and understand book. Anyone interested in the world of analytics will find this fascinating. I was surprised at how much I enjoyed this book. Very well explained Dr Siegel! I think this deserves five stars.

Provides a good overview of applications, but doesn't really explain any of the technical aspects behind the subject in any real depth

One dimension of the "Information Age" is the extent to which those who offer a product or service know much more now than ever before about those who are most likely to buy or lease it. Meanwhile, prospective buyers know more now than ever before about that product or service as well as others with which it competes. The implications of this information have wide and deep impact on marketing initiatives to create or increase demand for the given offering. The challenge to those in marketing is to obtain the information they need. Moreover, it must be accurate and sufficient as well as current. Only then can sound predictions be made. According to Eric Siegel, however, "Learning from data to predict is only the first step. To take the next step and [begin italics] act on predictions [end italics] is to fearlessly gamble...Launching predictive analytics means to act on its predictions, applying what's been learned, what's been discovered within data. It's a leap many take - you can't win if you don't play." How then to improve one's odds? Read this book. These are among the questions to which Siegel responds: o Why must a computer learn in order to predict? o How can "lousy" predictions be extremely valuable? o Why a predictive model into a field operation? What are the potential benefits of doing that? o To what extent (if any) do predictive mechanisms place civil liberties at risk? o How does our emotional online (social media) chatter "flip the meaning of fraud on its head"? o What actually makes data predictive? o How does prediction transform risk to opportunity? o Why does machine learning require both art and science? o What kind of predictive model can be understood by everyone? o What key innovation in predictive analytics has crowdsourcing helped to develop? o Why is human language such a challenge for computers? o Is artificial intelligence really possible? o What is the scientific key to persuasion? o Why is trying to predict human behavior a bad idea? o How is a person like a quantum particle? Siegel answers these and other questions throughout seven chapters filled with valuable information, insights, and counsel that enable him to explain how and why predictive analytics possesses "the power to predict who will click, buy, lie, or die." In Appendix A, he cross-references "Five Effects of Prediction," then in Appendix B, he cross-references "Twenty-One Applications of Predictive Analytics." These two appendices will facilitate, indeed expedite frequent review of key material later. The best works of non-fiction are research-driven and that is certainly true of this one, as indicated by 61 pages of notes (Pages 228-289). Until reading this book, almost everything I knew about analytics was learned from Tom Davenport, notably in two of his several books, Competing on Analytics (2007) and Analytics at Work (2011). He wrote the Foreword to Eric Siegel and after noting that we live in a predictive society, suggests, "The best way to prosper in it is to understand the objectives, techniques and limits of predictive models. And the best way to do that is simply to keep reading this book." I agree.

The book gives a very good introduction on for predictive analytics. Anybody interested in learning what predictive analytics is this is the book to go. Takes various cases and various industry domains to explain what and how predictive analytics is used. Also, explains what machine learning is in simple terms to a novice.

I'm a student writing a thesis on Predictive Analytics and this is an amazing book for students and experts. Well written and easy to understand Must-read !

Amazing book, read it with my eyes wide open! It gives an excellent overview of how this technology could be applied to various domains. Highly Recommended!

Bei contenuti. Ben immerso nel mondo reale

Dr. Siegel seems to have written this book for those with limited math skills, but with a desire to better understand the techniques for extracting meaning from big data. Since this describes me, I found the book quite valuable and gave it my highest rating. If you already have a strong grasp of the tools for organizing and interpreting big data, the book will probably not meet your needs. While the author writes well, the Introduction and first chapter skipped around on topics and anecdotes which caused me some initial concern. However, keep going because once past this early stage, the book gained traction quickly. In chapter 2, the author considers ethical concerns arising from predictive analysis. Target's analysis of a woman's buying patterns for pregnancy and Hewlett-Packard's analysis of its own employees for those that may quit both raise thought-provoking issues of whether such analyses are, to use the author's phrase - insight or intrusion. Using predictive analytics to prevent online fraud probably isn't as controversial. The author describes the tools to undertake predictive analysis. Decision trees, ensembles, and ensembles of ensembles may all be used to draw meaning from data. He describes the IBM team's development of Watson for the famous contest on the game show "Jeopardy" when Watson beat two humans who had performed at championship levels on this show. The author details the challenges of natural language processing to enable a machine to derive meaning from spoken English. He goes through examples that illustrate the high-level challenge. The IBM team used the tools of ensembles of ensembles (read the book to understand this) coupled with statistical interpretation to determine the most likely correct answer to any question and to do so faster than the human contestants. This was machine learning at the currently highest level. One of the fascinating points is that art drives machine learning. Can predictive analytics be employed to forecast an individual's actions? The thought seems troubling to me, but the possibility that prediction could be so used must be recognized. Which persons are most likely to favorably respond to a cell phone renewal offer as opposed to interpreting the offer as an opportunity to seek another carrier could have meaningful financial implications to a telecommunications carrier. He describes a predictive modeling undertaken by Oregon to predict which potential parolee is more likely to commit another crime if released from prison. This, too, has real world implications for the potential parolee and society. Once again, predictive analytics challenged me on many levels. Dr. Siegel identifies five effects of prediction. These are: (1) the prediction effect; (2) the data effect; (3) the induction effect; (4) the ensemble effect; and (5) the persuasion effect. I encourage you to read the book to learn about these effects and consider their potential cumulative "effect" on society, for good and ill.