The skills and tools needed to improve the accuracy of predictions of what will – and will not — happen
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 about others with which it competes. The implications of this “blizzard” of 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 act on predictions 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’s book 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.