Political candidates aren't the only ones hoping to sway voters this election season; plenty of other groups are engaging in campaigns -- and those efforts are increasingly driven by big data, even at smaller organizations with limited resources.
The Sierra Club, for instance, doesn't have the resources of a national presidential campaign. Unlike the Obama for America campaign, it can't hire a small army of predictive analysts and data scientists to model every aspect of its election-year strategy. But the nonprofit is using data mining to identify swing voters who are most likely to be motivated by its environmental message -- and who are most likely to be moved to vote for the candidates the club has endorsed in the 2012 election.
For the Sierra Club and organizations like it, the objective isn't to win at any cost, but to win cost-effectively.
"We target both people who might sit the election out unless sufficiently motivated and folks who may be undecided with a message that will be effective," says Sierra Club political director Cathy Duvall. In this way, the organization doesn't waste resources reaching out to voters who are already on board or those who are unlikely to be persuaded. "We have a more clean shot at the voters we want, and in most cases the return on investment is immediate," she says.
It's a two-step process, Duvall explains. Analysts apply data mining techniques against a massive database that provides very detailed profiles of its own members as well as "look-alikes" who fit the profile of swing voters. From there they develop models that predict which voter profiles will be most likely to respond positively to a campaign message and which type of issue will be most likely to move them to action.
"In some instances, we can take this research a level deeper through real-world experimentation," Duvall says. To accomplish this, Sierra staffers try out a range of specific messages on test groups to determine which will be the most effective before launching the campaign to the target audience. "We can see which messages are moving the voters. Before we could do cross-tabs and see the broad categories of people who might be moving, but with data mining we can go much deeper."
The 2012 election is shaping up to be the year of the data-driven, big data campaign. Political operatives in virtually every campaign, and across the political spectrum, are applying data mining techniques to mountains of new information from online sources that offer unparalleled insights into voter interests and habits.
For example, armed with more data, analysts can predict more accurately how individuals are likely to vote and whether they are Republicans or Democrats.
As they combine online data -- including social media posts -- with traditional data sources such as consumer databases, analysts can target groups of voters that fit very detailed profiles and choose the messages that will be the most likely to achieve the desired response. This sort of analytics work, known as microtargeting, was already under way during the last presidential election cycle. But since then, the amount of information available about individual voters has exploded. Campaigns have become more sophisticated in its use, and the tent has expanded, with smaller advocacy groups and campaigns coming on board.
"That ability for niche groups, such as the Sierra Club, to communicate only with people likely to support their cause didn't exist four years ago," says Patrick Ruffini, president of Engage DC, a firm that handles online advertising and analytics work for the Republican National Committee and individual Republican candidates.
Big data, analytics and mobile apps are enabling smaller political campaigns and advocacy groups to be more effective when it comes to winning over voters and raising money. Is data mining by candidates a privacy concern?