The Real Story Behind Obama's Election Victory
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By Eric Siegel,
The Fiscal Times
January 21, 2013

Elections hang by a thinner thread than you think.

By now you probably know that Barack Obama’s 2012 campaign for a second term “moneyballed” the election, employing a team of over 50 analytics experts.

You may also know that the huge volume of contentious and costly presidential campaign tactics – executed in the eleventh hour in pursuit of the world’s most powerful job – ultimately served only to sway a thin slice of the electorate: swing voters within swing states.

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But what most people don’t realize is that presidential campaigns must focus even more narrowly than that, taking micro-targeting to a whole new level. The Obama campaign got this one right, breaking ground for election cycles to come by applying an advanced form of predictive analytics that pinpoints rare gems: truly persuadable voters.

This is the new microcosmic battleground.

We’ve heard a great deal about Nate Silver lately. Silver has soared past the ranks of sexy scientist to become the face of prediction itself. If mathematical “tomorrow vision” has a name, it’s Nate. Even before his forecasts were vindicated by the election results, it was hard to find a talk show host who hadn’t enjoyed a visit from Silver.

But an election poll does not constitute prognostic technology – it is plainly the act of voters explicitly telling you what they’re going to do. It’s a mini-election dry run. There’s a craft to aggregating polls, as Silver has mastered so adeptly, but even he admits it’s no miracle of clairvoyance. “It’s not really that complicated,” he told late-night talk show host Stephen Colbert the day before the election. “There are many things that are much more complicated than looking at the polls and taking an average . . . and counting to 270, right?”

Moving beyond poll analysis, true power comes in influencing the future rather than speculating on it. Nate Silver publicly competed to win election forecasting – while Obama’s analytics team quietly competed to win the election itself.

This reflects the difference between forecasting and predictive analytics. Forecasting calculates an aggregate view for each U.S. state – but predictive analytics delivers action-oriented insight: predictions for each individual voter.

The concept of swing voters is ill-defined and subjective. The Democratic National Committee (DNC), in one approach, labels as “not very partisan” those voters who self-reported as independent, or for whom their party is (for any reason) unknown.

Despite this information about them, many such voters have indeed made up their minds and are unswingable.

Instead of mythical swing voters – or unicorns, for that matter – what matters to a campaign is concrete and yet quite narrow: Who will be influenced to vote for our candidate by a call, door knock, flyer, or TV ad? That is, who is persuadable?

With this in mind, Obama’s team predicted a completely new thing. Beyond predicting which way a constituent was destined to vote, they predicted whether each individual voter would be persuaded by campaign contact. Why? Because the best way to do persuasion is to predict it.

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Scientifically identifying persuadable voters empowered the Obama campaign to tweak more optimally and compete more ruthlessly. The notion of expending resources, such as a paid mailing or a campaign volunteer’s precious time, to contact a constituent who was already going to vote your way is abhorrent. Even worse, it’s known that in some cases contact will inadvertently change the voter’s mind in the wrong direction: It can backfire and cause them to vote for the other candidate.

It was only a matter of time before political campaigns began predicting their potential to influence each voter in order to optimize that influence – as achieved with a particular form of predictive analytics known as persuasion modeling (or uplift modeling or net lift modeling).

Enter Rayid Ghani, chief data scientist for Obama for America 2012. He was the man for the job. With a master’s degree in machine learning from Carnegie Mellon – the first university to have a Machine Learning Department – plus 10 years of research work at the labs of consulting behemoth Accenture, Rayid had rare and sought-after experience with persuasion modeling.