The Victory Lab (40 page)

Strasma’s craft was in writing algorithms, and his currency was the scores that emerged from them to predict individual behavior. Each score, calculated out of 100, reflected the percentage likelihood that a person would perform a certain act. For every Iowan in Strasma’s database, including Republicans and unregistered seventeen-year-olds, Strasma produced two scores measuring the basic questions every campaign had when it looked out over the electorate. What were the odds someone would vote? And whom was he or she likely to back?

The first of these was known as a turnout score, calculating as a percentage the likelihood someone would participate in the Democratic caucus. The second was called the Obama support score, which indicated the probability that he or she would support Obama, even if it was unlikely he or she would show up in the first place. (For this reason, Strasma saw a lot of Republicans with turnout scores close to zero and Obama support scores near 100: the algorithms determined that they were very unlikely to
attend the Democratic caucuses, but if they did so would almost certainly end up in Obama’s corner.) Strasma also generated individual support scores for Obama’s opponents, which allowed field organizers to change their tactics for each precinct. These scores all ended up on the voter file, so field organizers putting together local walk lists or call sheets could just call up names within certain score ranges for persuasion or GOTV contact. Strasma liked to analogize the scores to precinct averages. If 100 people with 75 percent Obama support scores went to the polls, he would get 75 votes. Among a group of 100 people with 40 percent turnout scores, there would be 40 voters.

Because Strasma had generated predictive scores for every voter, and not only those whom the campaign had directly identified or solicited for supporter cards, Obama’s team had remarkably good intelligence on where the opposition’s support was located and could plug it into their turnout projections. With that information, Obama strategists knew where it made most sense to call Hillary Clinton backers—in the hopes that converting a small number of them to Obama’s side could keep Clinton under a threshold that would grant her campaign an additional delegate—or where John Edwards was uncertain to prove viable and his supporters could be persuaded to consider Obama as a second choice. While Obama’s voter file did not initially include support scores for Ohio congressman Dennis Kucinich, a liberal gadfly unlikely to contend for delegates, Strasma added one after seeing in polls that Kucinich’s strongly antiwar supporters would almost unanimously default to Obama when they had to make a second choice. Based on that information,
the state director, Paul Tewes, lobbied Kucinich to issue a statement endorsing Obama as a fallback, which Obama’s campaign was then able to get to everyone whom the scores predicted to be a likely Kucinich supporter.

The arrangement with Kucinich, and a similar deal with New Mexico governor Bill Richardson’s campaign to swap second-round backing in cases where one of the two failed to be viable, represented an odd moment for Obama, who often renounced transactional politics as old-guard
tactics. His campaign had a conflicted relationship with the things one has to do to win elections. Obama, the former community organizer, believed in a certain purity of grassroots politics, equal at least to the contempt with which he dismissed opponents’ political activity as craven or the media’s interest in the contest as superficial. At the same time, Obama and his spokespeople bragged incessantly about the campaign’s mechanistic accomplishments: how many volunteers they had enlisted, dollars raised, text messages sent. It is little coincidence that these were all numbers. Early on, campaign manager David Plouffe had insisted that the campaign try to measure everything it did as a method of gauging its effectiveness, and that sense of data-intensive empirical rigor quickly moved into all corners of a campaign that would become the largest in history. “We had a lot of money, but we were incredibly efficient with our spending, and that came from Plouffe every single day,” says Link. “He just didn’t like spending money. For a guy who spent a lot of money in the election, it killed him to authorize a check.”

But as money continued to come in, Obama’s campaign was able to create increasingly specialized roles around data and technology. Wagner’s job as the one-man IT team for the Des Moines headquarters disappeared, its component roles spun off; after a month of processing piles of supporter’s cards, Wagner’s data entry duties fell to someone else, as did the need to fix the office computers. His schedule freed up, Wagner committed himself to building a software program that could guide tactics for Obama representatives at each caucus location. He went about it by rewriting each of the unusual rules and protocols of the Iowa nominating process—the viability thresholds, the multiple rounds of voting and subsequent realignments, the proportional allocation of delegates—as a series of interlocking game-theory problems. When did it make sense to release some of Obama’s supporters to a rival to keep him in play for another round? Or how many of a no-longer-viable candidate’s supporters would Obama need to pick up to qualify for an extra delegate? The program Wagner wrote, the Caucus Math Tool, was loaded onto laptops that campaign
representatives could bring to their precincts. Its straightforward interface required only entering each candidate’s tallies after every round of voting and would deliver practical instructions on how to adjust for the next one. (The broad objective of every move was to block Hillary Clinton from accumulating delegates, regardless of who won them instead.)

At headquarters, campaign officials knew well before results from the first round of votes were typed into Caucus Math how things would go. As voters arrived at precincts across the state for the six-thirty caucus start time, Obama volunteers would call a hotline to report the number who had checked in. The numbers on Wagner’s computer were rising faster than anyone had anticipated. By the time local supporters wrapped up their speeches, it was clear to Obama’s strategists that they had easily met their goal of delivering 180,000 Iowans at the caucuses. In fact, the final number ended up being 239,000. News reports relied on exit polls to describe who they were: half were participating in a caucus for the first time, and
the share of voters under the age of twenty-five had tripled from Kerry’s win four years earlier. Very late that night, Wagner went to sleep, barely budged from his bed for two days, and then drove straight east to Chicago.

AS SOON AS
he arrived back at Obama’s national headquarters, Wagner was pulled aside by Jon Carson, who had been charged with handling the campaign’s preparations for February 5. Twenty-four states would vote that day across four time zones, a combination of simultaneous primaries and caucuses heretofore unparalleled in its scope and influence on the nominating contest. Obama had begun paying attention to the delegate count well before Clinton, and starting in the summer of 2007 Chicago had become attuned to the varied and byzantine ways that delegates were awarded around the country. While Clinton appeared to be ignoring those states she was unlikely to win outright, especially those with caucus systems, Carson had developed distinct tactics for each state with the goal of maximizing
his delegate haul nationwide. Some campaign departments were well prepared for this shift. Obama had begun in early 2007 to develop a robust volunteer and fund-raising network in all fifty states to exploit his popularity among Democratic activists. But Strasma’s microtargeting work during that period was focused almost exclusively on Iowa, with little effort to formalize efforts elsewhere. As a result, the data team’s early delegate projections were based on simple demographics: the numbers of African-Americans and Democratic voters under thirty in each district. Analysts tallied them on a whiteboard as “Group A” and “Group B,” to keep the simple classifications opaque to reporters tramping through headquarters hoping to pick up hints of campaign strategy.

Obama’s data operation was forced to grow quickly to meet the circumstances of a national race. In late 2007, Strasma’s scores showed up for the first time on the state voter files maintained in Obama’s New Hampshire, South Carolina, and Nevada field offices. In Nevada, the VAN was the province of Ethan Roeder, a graduate of the School of the Art Institute of Chicago who had learned how to use databases while preparing donor reports for Lambda Legal, a gay rights advocacy group. When he won a job with the Obama campaign, that experience was enough to earn him an assignment to Las Vegas as the state voter-file manager. Until Strasma placed microtargeting calls into his state, Roeder had assembled target universes without help from modeling scores. He would look at polls, find population segments that were inclined to support Obama—mostly young whites and African-Americans of all ages—and use geography and basic demographic categories on the voter file to compile walk lists and call sheets for the state’s growing crew of field organizers. When Strasma’s support and turnout scores showed up in the VAN, Roeder saw how much more finely the electorate could be broken down. “We were dealing with chunks that big, and he comes to us with these small slices,” says Roeder.

In campaign offices around the country, a generation of political data experts was being born, forced by exigency to learn how to manipulate voter files and invited by a decentralized campaign to improvise with
them. Jim Pugh, who worked in Chicago on online analytics,
was still finishing his dissertation on robotics at the École Polytechnique Fédérale de Lausanne, in Switzerland. Matt Lackey had worked on nuclear reactor design for Westinghouse before starting his own comic book business; he ended up managing the Indiana voter file. John Bellows was a graduate student in econometrics at Berkeley and yet another campaign neophyte before he walked into a California field office during the primary season. Because Obama was not aggressively contesting California, Strasma had never built a model for the state. Instead, the campaign decided to open up access to the VAN to anyone who registered as a precinct captain. Bellows used the opening to assemble his own statistical models to identify likely Obama supporters, but the campaign’s state director, Buffy Wicks, was at a loss for what to do with them. She put Bellows in touch with Wagner. “I don’t know what you two nerds are talking about,” she said.

Strasma’s national projections were sharpened after Iowa, when he ordered his first calls into the February 5 states. In many of them, self-organized volunteer cells had already been canvassing voters for months. Strasma took their hard IDs and the new results of his large-sample polls and fed them into the algorithms to develop state-specific scores and assign them to voters. Carson built a small February 5 team to work through all the new information coming in from around the country and recruited Wagner to be its targeting analyst. In the month before February 5, Wagner would arrive at headquarters at seven each night for a twelve-hour shift, processing the numbers that came out of Strasma’s computers overnight and assembling them into a daily report that could help Carson move resources among the states. No longer would the campaign rely solely on an outside consultant for an interpretation of the microtargeting models he had developed. “The analyst has to be inside, so that the campaign manager can look to his right and say ‘What’s going on?’ ” says Wagner. “He has to be able to answer two questions: Are we going to win or are we going to lose? And what the hell are we going to do about it?”

The answers to those questions shifted regularly, as Strasma’s support
scores recalibrated to account for a changing race. Previous campaigns that had used microtargeting usually ran their numbers a single time. They conducted the polls, gathered individual-level data, performed the analysis, and gave everyone a score or a segment that stuck with their voter-file record for the whole campaign. It was treated as an inflexible personal attribute, like gender. There were perils in doing a onetime microtargeting project, however. When Bush’s team began its large-sample surveys in late 2003, a year before the election, many of the “anger points” questions had been drafted to prepare for an anticipated contest against Dean, who at the time led Kerry considerably in polls. One tested attitudes toward recitation of the Pledge of Allegiance in schools, the subject of a case just accepted by the Supreme Court that Republicans thought could offer a successful wedge issue against Dean.

Primaries presented an even bigger need for fresh microtargeting information. Unlike in a general election, a voter’s partisanship was rarely predictive of how he or she would vote, because most of those who cast ballots in a primary were members of the same party. In those cases where it was predictive—in his presidential campaigns John McCain repeatedly did better with non-Republicans who voted in Republican primaries—it was not always intuitive. Because the major fault line of party didn’t exist, lots of little ones (demographic, ideological, geographic, issue-based) emerged, intersecting in complex geometries that required nimble analysis of many variables. More than ever, the basic poll subsamples that could handle only three or four overlapping voter characteristics at once were insufficient. Microtargeting solved that problem, but a single-shot approach couldn’t keep up with the fluidity of primary-season opinion—voters seemed quicker to change their minds when parties weren’t hemming them in. It was possible to have a very good microtargeting algorithm that gave the wrong answer to the most important question of all, just because the which-types-of-people-support-whom data went stale before it could be used.