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“Most voters are really complicated. So you need something nonlinear to figure out how to talk to them.”

work and tracked them all summer long.” Using that re- search, Jindal talked to voters in each segment of this cus- tom universe. And on Election Day, he hit the magical 42 percent and won the race. The flip side of talking to voters is talking to campaign

staff. In 2004, few top campaign staff knew what to do with raw microtargeting data. The young nerds who had time to play with the data were the ones in the remote field offices. In 2008, they’re now running the show and want to be able to get their hands dirty, so to speak. Grassroots Targeting is the first firm to create its own

software so campaigns can do just that. It lets a campaign manager select the voters he’d like to reach—like mar- ried men who are regular churchgoers who make above $100,000. Since the software is web-enabled, direct mail and phone vendors can go in and use the data as well. “If you’re spending this much time and money to put

this together, people should actually use the data,” Hazel- wood says. “I try to empower the campaigns as much as possible, because they know their campaigns the best.”

The Art of Persuasion

“When it comes to microtargeting, there’s all this hype out there, it’s all the rage right now,” says Brian Stults, a bit cynically. A GOTV expert, he now heads data services for the nonpartisan research firm Polimetrix. “It’s great in the weeks leading up to the election where you’re trying to get supporters to the polls, but in the months prior to the election when you’re trying to convince people, you’re still sort of guessing like you were four years ago.” His firm studies those elusive persuadable voters. A mil- lion people have volunteered to help, and every day Poli- metrix asks at least 5,000 of them about things both po- litical and nonpolitical. “Our premise is by watching how people’s opinions change over time, and learning about the drivers of those changes, we can learn about what types of people are susceptible to change and what kinds of stimuli can create that change.” The firm launched the survey to help companies sell prod-

ucts, but realized they were on to something much bigger. “If someone doesn’t change their preferences over time,

those are the kinds of people campaigns don’t want to waste their resources targeting, because they’re not going to move their opinion,” he explains. By figuring out how

22 Politics | Canadian Edition

to identify persuadables, their survey data has boosted the accuracy of political models up to 15 percentage points. And while all microtargeters are working at the household

level these days, Polimetrix has started looking at how the relationships within households affect persuadability. “If you are a Democratic female living with a Republican male, you may respond to a very different set of issues than a Republican female living with a Republican male,” Stults says.

Non-linear Clusters

Last summer, Amy Gershkoff reviewed all the targeting software on the market for her firm, MSHC Partners. She realized that corporate programs might work great for sell- ing magazine subscriptions, but that they force voters into narrowly defined categories, like slicing a pie into wedges. “In the commercial software, having big overlapping clus-

ters is okay, because if you have a consumer who’s in two different clusters, you’re on the fence for sending them two different catalogs,” she says. “But in politics, you could wind up with people who have a 40 percent likelihood of being in a young conservative cluster, and a 40 percent likelihood of being in an older, blue collar, liberal-leaning cluster.” Gershkoff ’s A-ha moment came when she stopped try- ing to fit a round political peg into a square corporate hole. Her SmartClus software identifies people who cluster to- gether naturally. So instead of a circle segmented into pie wedges, hers is broken into bunches of amorphous blobs— some big, some small, some containing smaller blobs of their own—that lasso together different groups of people. Voters who don’t fall into one of those blobs are set aside, rather than creating a less-accurate cluster. When she grabs my notebook and sketches a sample, the blobs look almost artistic, like a child learning how to draw circles. “Most voters, male or female, are really complicated. So it makes sense to me that you would need something really complicated, really nonlinear to figure out how to talk with [them].”

Artificial Intelligence

In 2004, political consultants turned to the commercial world to see what great ideas they could steal. In 2008, the business world is reaching out to people like Ken Strasma, whose firm Strategic Telemetry is using artificial intelligence to build bet- ter, faster models for Sen. Barack Obama’s presidential bid. There’s a scene in the 1980s flick Wargames where the computer, Joshua, plays tic-tac-toe. He gets faster as he fig- ures out how to play the game. Everything starts whirring at light speed as his learning curve shortens. That’s basically what Strasma’s computers do. They race through thousands of options, learning from previous models they’ve already built, until they develop the best possible formula. Strasma compares it to Darwinian evolution. “The model Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69
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