How Microtargeting and extrapolatory algorithms helped Obama become president?

?This article explores how technology and sophisticated algorithms helped Obama's campaign create a targeted and precise GOTV effort, choosing issues to talk about, reaching out to 17 years old, and making game theory decisions regarding opponents to win Iowa caucuses with historic turnouts which is the first and necessary step to become the president of the United States.

Hearty thanks: The Victory Lab by Sasha Issenberg

Background information:

?To simplify, both the democratic and republican party usually choose their presidential candidates through a system of internal elections called primaries /caucuses. There can be a separate system of allotting ‘delegates’ from each state (some states require voters to give their preferred order and eliminate a candidate who gets less than 15%..). Those who get higher delegates(+ superdelegates which consist of party officials and position holders) nationwide secure the nomination. While anyone can vote in a Democratic primary, registered Democrats usually vote in democratic primaries. The primaries start with Iowa State. Winning in Iowa is considered to be paramount as it sets the stage for the rest of the primaries. Obama, a senator at that time, was fighting as an underdog candidate against Hilary Clinton, the “establishment candidate”. The consultants for Obama 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.

It 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.

How did the Obama Campaign look like in 2008?

The 2008 Obama campaign would become, in a sense, the perfect political corporation: a well-funded, data-driven, empirically rigorous institution that drew in unconventional talent ready to question some of the political industry’s standard assumptions and practices and emboldened with new tools to challenge them. “It was like the old Bell Labs,” Larry Grisolano, a senior Obama strategist, says of the analytics teams assembled at Chicago headquarters. “They had a lot of ability to create and innovate without being concerned about what the outcome was. There was a laboratory attitude with those guys. It was the overwhelming culture of the campaign.

Increased scope for targeting:

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.

?Algorithms to identify likely voters who have never been visited personally:

?For context, parties love knowing who is more likely to vote for them so that they can focus their efforts (volunteers, money, ads, turnout efforts..) on those areas. However, the surveys on who a person will vote for can be conducted only for a limited number of people due to resource constraints.

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 of volunteer demands were almost always prohibitive, and it was getting much harder: the proliferation of cell phones and caller identification made it simply impossible to get through to a significant share of the population.

By writing statistical algorithms based on known information about a small set of voters, he(political consultant) 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.

Where did the team get the data from?

Iowa caucus campaigns had a robust tradition of asking caucus-goers to sign supporter cards vowing their commitment, which were considered inviolable. Strasma (Obama's campaign staff) 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.

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.?

They also created a turnout score and an Obama support score.

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.

These scores helped to focus the turnout efforts for likely Obama voters.

Issues to talk about to each voter:

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.

The extrapolatory algorithm was used to determine which issues matter to a much larger number of people rather than just the people surveyed.

17 year old "voters":

?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. 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.

Data aided Precise operations on ground and game theory decisions:?

The unique design of the primaries called for several interesting game theory decisions.

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. (There arose) 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.)

The rest is history, the history of the first black president of the United States.

Microtargeting has advanced a lot in the past decade and spread across countries including India. The lack of institutionalized consumer data and the nascent stage of political consulting in India makes this a bit challenging. Nevertheless, the field of political consulting is advancing at a rapid pace, especially in India and the next few decades could shape the future of electioneering in India. This has its own advantages and risks. The players should have ethical implications and social well-being in mind while advancing the field.

What do you think about this? Fascinating? Concerning? Comment your valuable views.?

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