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

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.

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.

No--but Obama did. 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 make per-voter prediction. 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.

One of the most inspiration accomplishments of predictive analytics is IBM's Watson, which was able to compete against the all-time human champions on the TV quiz show Jeopardy! 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.

Here are just a few examples: