The Victory Lab (39 page)

THE SECOND-FLOOR CAPITOL HILL OFFICE
with the computers, in reality little more than a closet-sized apartment, was the official Washington address of Ken Strasma, whose black-box algorithms had become something of a legend in Democratic data circles but were almost entirely unknown outside them. His appearance made the case for anonymity. Tidy and plain-featured, the Wisconsinite looked like he came out of middle management in the middle of the country in the middle of the last century.

Strasma had worked as research director of the National Committee for an Effective Congress, which started mapping and scoring precincts in the 1970s to give Democrats their first resource for systematically targeting voters. In the late 1990s, Strasma had watched as the precinct was displaced by the individual as the essential unit of targeting, a shift manifest in the form of a personal rivalry between his boss, Mark Gersh, and Hal Malchow, played out in sparring memos that did little to mask the two men’s mutual resentment. Strasma may have worked for Gersh, but Malchow’s approach won his heart and mind. While working for state legislative candidates in Minnesota in 1996, Strasma conducted large-scale polls in each of the small districts, and used the results to find personal targets, based on voter-file attributes and Census-tract data, much as Malchow had done earlier that year in Oregon. “We were tiptoeing into the individual level,” Strasma says. “I was doing microtargeting before we had a name for it.”

After 2002, the ability to do that type of individual-level targeting improved significantly. Greater computer speeds made it easier to swiftly churn through millions of records. Perhaps most important, the release of data from the 2000 U.S. Census created a reservoir of free, up-to-date information unavailable elsewhere; tract-level figures that in 1998 were nearly
a decade old had been refreshed to account for years of movement and demographic change. “At the time, individual targeting was basically nonexistent, so anything we did would be an improvement,” says Strasma. But it threatened NCEC’s monopoly as a provider of targeting guidance, and Gersh took Strasma’s development of the new specialty as something of a betrayal, like a child abandoning his parents’ faith to join a cult. “It seemed like the same general field, just going into another niche,” says Strasma. “I tried my best to tiptoe very carefully around the politics of it.” In 2003, Strasma quit NCEC to open his own firm, Strategic Telemetry, which rightfully evoked a distant scientific frontier inaccessible to the naked eye and traditional tools. His firm’s products would be what Strasma called “virtual IDs.”

The “hard ID”—a voter who tells a caller or canvasser which candidate he or she supports—remained the truest currency in predicting support, a certain vote as long as the voter could be turned out. But no campaign had ever been able to hard-ID every voter in its universe, or even a majority of them. The costs or volunteer demands were almost always prohibitive, and it was getting much harder: the proliferation of cellphones and caller identification made it simply impossible to get through to a significant share of the population. Except in precincts where they had a strong partisan advantage, campaigns would often be forced to forget about those they couldn’t reach and turn out only those whom they had individually identified as supporters.

Strasma believed it would be possible to simulate IDs for the whole electoral universe, regardless of whether the campaign was ever able to talk to voters directly about their preference. By writing statistical algorithms based on known information about a small set of voters, he could extrapolate to find other voters who looked—and presumably thought and acted—like them. If he could identify enough matchable variables from one set to the next, the campaign could treat these virtual IDs as an effective replacement for hard IDs where it couldn’t get them.

Strategic Telemetry’s first client was John Kerry, and its initial project
was to develop a computer model that could virtual-ID participants in the Iowa caucuses. The differences between caucusing and voting were beyond semantics, and a unique information culture had developed around the distinctions. Campaigns approached the caucuses by developing a two-tier system for counting backers: there was the usual system of hard IDs, in which canvassers ranked voters from 1 to 5, the spectrum from a strong commitment to support Kerry to an equally strong commitment to one of his rivals. On top of that, Iowa caucus campaigns had a robust tradition of asking caucus-goers to sign supporter cards vowing their commitment, which were typically treated by both sides as an inviolable pledge. “If they answer on the phone ‘I’m supporting Kerry,’ that tells us that at the moment they felt they’re supporting Kerry,” says Strasma, who had first worked in Iowa in 1988, entering Dukakis supporter cards onto computers. “If they sign the card, they’ve actually done something for us.” But voting was not that simple: delegates were awarded proportionally from each of the state’s 1,784 precincts, and a candidate had to receive 15 percent at an individual caucus site to emerge from it with any. If a candidate failed to meet that threshold, he was effectively eliminated at that site. His supporters disbanded and were free to walk to another candidate’s corner. This complex system meant that campaigns had to build statistical models specifically for the Iowa caucuses. Strasma conducted a brief ten-thousand-person survey, a far bigger statewide sample than typically used by caucus candidates, asking voters how likely they were to turn out the following January and whom they supported.

The voter files that the Iowa Democratic Party sold to candidates are rich with historical information, including allegiances like membership in the party’s rural and gay-lesbian caucuses and past hard IDs that the candidates are required to return to the party after each year’s voting. In addition, Strasma collected some of his own data, such as a list of those who had applied for a tax benefit that Iowa extends only to military veterans. Instead of a two-sided prediction, Strasma had to develop multidimensional scores that would predict an individual’s likelihood of supporting
each of the top candidates, including Howard Dean, Dick Gephardt, and John Edwards—to calculate precincts where certain candidates, including Kerry, would fail to meet the 15 percent threshold, or where rivals would do so, allowing Kerry to claim their orphaned supporters before the second round of voting. “The Iowa caucus was the best possible petri dish for this stuff,” says Strasma.

He tuned his system to serve as a fulcrum between the campaign’s data processing and its field organization. Every evening by midnight, Kerry’s field staff was required to input the results of that day’s canvass, including supporter cards, into a computer system. Strasma would wake at 4 a.m. to see what his algorithms had done to the numbers. Based on the new predictions, Strasma would update vote totals for every precinct in Iowa, which had been established to keep Kerry on pace to meet the statewide delegate goals necessary for victory. By 8 a.m., that report would be sent to Kerry’s Iowa caucus director, Jonathan Epstein, so he could adjust resources daily to make sure they were being committed to precincts where Kerry stood to gain, or protect, the most delegates. Epstein would hassle Strasma if he was fifteen minutes late in delivering what the field staff referred to as their “crack sheet.”

Those spreadsheets, based on real and virtual IDs, gave Kerry’s campaign hope through the autumn of 2003, when Dean’s rise in the polls appeared to eclipse Kerry’s standing as front-runner in Iowa and nationally. “At the time, with my friends and family—when I told them I was working for Kerry, they would act like my dog had died or something,” Strasma recalls. But he was always far more confident about Kerry’s standing. Kerry was hitting his targets, while Dean’s support seemed to be slowly buckling in ways that polls, and his own strategists, were unable to pick up. First off, Dean backers seemed to be densely packed into precincts, especially those around college campuses, where they could overwhelm rooms on caucus nights but fail to materialize any extra delegates from it. More surprising to Strasma, Dean canvassers did not appear to be going back to people once they had been ID’d as supporters, banking them as a vote
even as the race’s dynamics changed. The Kerry campaign watched this phenomenon among its own supporters: they called it a “flake rate,” which Strasma quantified and monitored closely. He measured the speed at which voters peeled away from Kerry to support another candidate, and again as onetime supporters of other candidates switched over to Kerry. But Dean’s campaign, blinded by encouraging polls and press coverage claiming their candidate’s certain victory, didn’t seem to notice until too late.

The Dean experience hung over Obama’s Chicago high-rise headquarters and every one of
the three dozen field offices it eventually opened in Iowa. Shortly after Obama had announced his candidacy, Larry Grisolano and Pete Giangreco had gathered in Chicago to sketch early vote goals for Iowa. Neither man’s portfolio gave him specific responsibility for turning out caucus-goers—Grisolano was Obama’s paid-media and opinion research director, and Giangreco his lead mail consultant—but the campaign was still hiring its field staff and the two men had both worked in the Iowa caucuses since the 1980s and were familiar with its peculiar practices. They knew that Obama, competing with Hillary Clinton and John Edwards, would have a tough time cracking the insular pool of reliable caucus-goers. If turnout was around 125,000, as it had been in 2004, Obama would have no shot of breaking through. Total turnout, Grisolano and Giangreco concluded, would have to reach 180,000, far more Democrats than had ever before participated, a particular challenge in a year when Republicans had their own wide-open primary fight. (Iowans could participate in either party’s caucus, regardless of their registration before caucus day.) Obama would succeed only if he could enlist tens of thousands of new caucus-goers, many of them young people traditionally underrepresented there. This was sensible as a strategy, but it invited a comparison that bedeviled Obama’s advisers. They knew that after the 2004 outcome, the world was ready to dismiss the arrival of another antiwar candidate pledging to deliver a caucus coalition of liberal activists and young, first-time caucus-goers. “In the political community, it was ‘Obama’s got all this buzz but it’s just like Dean.’ We were hearing that and, to a certain extent, it was true.
Our challenge wasn’t how to pretend it wasn’t true. It was how to turn that into an advantage,” says Strasma. “We had almost the perfect blueprint. What we needed to do was avoid the pitfalls that had befallen Dean.”

The supporter cards that Wagner was processing in Des Moines were feeding into the computers at Strategic Telemetry’s Capitol Hill office. Those commitments, along with some traditional polling, had already helped to refine Obama’s back-of-the-envelope vote goals in Iowa. But the real power of Strasma’s black box, like all microtargeting models, was extrapolatory: the names of those who had signed supporter cards went in, and out came the names of other Iowans who looked like them. These algorithms were matched to 800 consumer variables and the results of a survey of 10,000 Iowans. Going a step further than the Kerry campaign, Strasma wanted to create a model that would help Obama’s advisers decide which topics they should use when communicating with its targets. Strasma’s polls asked voters for opinions on eight issues, and separately, asked for their top two concerns. Obama’s pollsters had realized that if they called likely Iowa caucus-goers in the summer of 2007 and asked what issue was most important to them, nearly everyone would say Iraq. When they asked for the top two, it opened a Pandora’s box of progressive worry: the environment, health care, civil liberties.

But Strasma was also looking for people who weren’t on the Democratic rolls, or even yet voters. Iowa residents who would turn eighteen by November’s election day were allowed to participate in caucuses, but no campaigns had ever gone after the population of eligible seventeen-year-olds, in part because no one knew who they were—since they weren’t registered and had no political history, they didn’t show up in the state voter file. The Obama campaign, desperate to reach its 180,000 target, created a “BarackStars” program to contact Iowa high school students, and it was Strasma’s job to help field organizers find them. “I had never before been involved in a campaign where that was such a rich vein to mine,” he says. Strasma acquired lists of high school seniors who had taken the ACT college admissions test, names typically marketed to college admissions
officers seeking to mail potential applicants. Separately, the campaign had student supporters gather school directories, but—fearing that it would look creepy if it had adult phone banks calling high schoolers—created a system for young backers to call their peers. “A massive program targeting seventeen-year-olds is fraught with peril,” says Strasma. His models treated most seventeen-year-olds in the correct birthday range as strong Obama targets, except for those with a Republican for mother and father. “We assumed kids wouldn’t necessarily go against both their parents,” says Giangreco. As the BarackStars initiative progressed, Obama’s Iowa team worked to avoid a mistake Dean had made with college-age supporters. When Strasma’s scores identified Iowa college students as targets, a field staffer would call to convince them that it was more useful for them to caucus at their home address than at their school, to disperse them in precincts across the state and not just pack them into those surrounding campuses. Strasma suggested this would also have a beneficial plan-making effect: talking aloud about the details of where they would vote was likely to increase their chances of following through on it.