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Inside The World of The Data Crunchers and Modelers Who Shape Campaign Strategy
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Inside The World of The Data Crunchers and Modelers Who Shape Campaign Strategy - November 7, 2012, 05:02 PM

I am sure Romney had a team of MIT number crunchers who were equally as adept, working their numbers and models in the exact same fashion.

And therein is the state of our political system; essentially a game of Moneyball.
Out with political analyst and listening to people at town hall meetings.
In with the Developers, PhD Quants, and Statisticians.



In late spring, the backroom number crunchers who powered Barack Obama’s campaign to victory noticed that George Clooney had an almost gravitational tug on West Coast females ages 40 to 49. The women were far and away the single demographic group most likely to hand over cash, for a chance to dine in Hollywood with Clooney — and Obama.

So as they did with all the other data collected, stored and analyzed in the two-year drive for re-election, Obama’s top campaign aides decided to put this insight to use. They sought out an East Coast celebrity who had similar appeal among the same demographic, aiming to replicate the millions of dollars produced by the Clooney contest. “We were blessed with an overflowing menu of options, but we chose Sarah Jessica Parker,” explains a senior campaign adviser. And so the next Dinner with Barack contest was born: a chance to eat at Parker’s West Village brownstone.

For the general public, there was no way to know that the idea for the Parker contest had come from a data-mining discovery about some supporters: affection for contests, small dinners and celebrity. But from the beginning, campaign manager Jim Messina had promised a totally different, metric-driven kind of campaign in which politics was the goal but political instincts might not be the means. “We are going to measure every single thing in this campaign,” he said after taking the job. He hired an analytics department five times as large as that of the 2008 operation, with an official “chief scientist” for the Chicago headquarters named Rayid Ghani, who in a previous life crunched huge data sets to, among other things, maximize the efficiency of supermarket sales promotions.

Exactly what that team of dozens of data crunchers was doing, however, was a closely held secret. “They are our nuclear codes,” campaign spokesman Ben LaBolt would say when asked about the efforts. Around the office, data-mining experiments were given mysterious code names such as Narwhal and Dreamcatcher. The team even worked at a remove from the rest of the campaign staff, setting up shop in a windowless room at the north end of the vast headquarters office. The “scientists” created regular briefings on their work for the President and top aides in the White House’s Roosevelt Room, but public details were in short supply as the campaign guarded what it believed to be its biggest institutional advantage over Mitt Romney’s campaign: its data.

On Nov. 4, a group of senior campaign advisers agreed to describe their cutting-edge efforts with TIME on the condition that they not be named and that the information not be published until after the winner was declared. What they revealed as they pulled back the curtain was a massive data effort that helped Obama raise $1 billion, remade the process of targeting TV ads and created detailed models of swing-state voters that could be used to increase the effectiveness of everything from phone calls and door knocks to direct mailings and social media.

How to Raise $1 Billion
For all the praise Obama’s team won in 2008 for its high-tech wizardry, its success masked a huge weakness: too many databases. Back then, volunteers making phone calls through the Obama website were working off lists that differed from the lists used by callers in the campaign office. Get-out-the-vote lists were never reconciled with fundraising lists. It was like the FBI and the CIA before 9/11: the two camps never shared data. “We analyzed very early that the problem in Democratic politics was you had databases all over the place,” said one of the officials. “None of them talked to each other.” So over the first 18 months, the campaign started over, creating a single massive system that could merge the information collected from pollsters, fundraisers, field workers and consumer databases as well as social-media and mobile contacts with the main Democratic voter files in the swing states.

The new megafile didn’t just tell the campaign how to find voters and get their attention; it also allowed the number crunchers to run tests predicting which types of people would be persuaded by certain kinds of appeals. Call lists in field offices, for instance, didn’t just list names and numbers; they also ranked names in order of their persuadability, with the campaign’s most important priorities first. About 75% of the determining factors were basics like age, sex, race, neighborhood and voting record. Consumer data about voters helped round out the picture. “We could [predict] people who were going to give online. We could model people who were going to give through mail. We could model volunteers,” said one of the senior advisers about the predictive profiles built by the data. “In the end, modeling became something way bigger for us in ’12 than in ’08 because it made our time more efficient.”

Early on, for example, the campaign discovered that people who had unsubscribed from the 2008 campaign e-mail lists were top targets, among the easiest to pull back into the fold with some personal attention. The strategists fashioned tests for specific demographic groups, trying out message scripts that they could then apply. They tested how much better a call from a local volunteer would do than a call from a volunteer from a non–swing state like California. As Messina had promised, assumptions were rarely left in place without numbers to back them up.

The new megafile also allowed the campaign to raise more money than it once thought possible. Until August, everyone in the Obama orbit had protested loudly that the campaign would not be able to reach the mythical $1 billion fundraising goal. “We had big fights because we wouldn’t even accept a goal in the 900s,” said one of the senior officials who was intimately involved in the process. “And then the Internet exploded over the summer,” said another.

A large portion of the cash raised online came through an intricate, metric-driven e-mail campaign in which dozens of fundraising appeals went out each day. Here again, data collection and analysis were paramount. Many of the e-mails sent to supporters were just tests, with different subject lines, senders and messages. Inside the campaign, there were office pools on which combination would raise the most money, and often the pools got it wrong. Michelle Obama’s e-mails performed best in the spring, and at times, campaign boss Messina performed better than Vice President Joe Biden. In many cases, the top performers raised 10 times as much money for the campaign as the underperformers.

Chicago discovered that people who signed up for the campaign’s Quick Donate program, which allowed repeat giving online or via text message without having to re-enter credit-card information, gave about four times as much as other donors. So the program was expanded and incentivized. By the end of October, Quick Donate had become a big part of the campaign’s messaging to supporters, and first-time donors were offered a free bumper sticker to sign up.

Predicting Turnout
The magic tricks that opened wallets were then repurposed to turn out votes. The analytics team used four streams of polling data to build a detailed picture of voters in key states. In the past month, said one official, the analytics team had polling data from about 29,000 people in Ohio alone — a whopping sample that composed nearly half of 1% of all voters there — allowing for deep dives into exactly where each demographic and regional group was trending at any given moment. This was a huge advantage: when polls started to slip after the first debate, they could check to see which voters were changing sides and which were not.

It was this database that helped steady campaign aides in October’s choppy waters, assuring them that most of the Ohioans in motion were not Obama backers but likely Romney supporters whom Romney had lost because of his September blunders. “We were much calmer than others,” said one of the officials. The polling and voter-contact data were processed and reprocessed nightly to account for every imaginable scenario. “We ran the election 66,000 times every night,” said a senior official, describing the computer simulations the campaign ran to figure out Obama’s odds of winning each swing state. “And every morning we got the spit-out — here are your chances of winning these states. And that is how we allocated resources.”

Online, the get-out-the-vote effort continued with a first-ever attempt at using Facebook on a mass scale to replicate the door-knocking efforts of field organizers. In the final weeks of the campaign, people who had downloaded an app were sent messages with pictures of their friends in swing states. They were told to click a button to automatically urge those targeted voters to take certain actions, such as registering to vote, voting early or getting to the polls. The campaign found that roughly 1 in 5 people contacted by a Facebook pal acted on the request, in large part because the message came from someone they knew.

Data helped drive the campaign’s ad buying too. Rather than rely on outside media consultants to decide where ads should run, Messina based his purchases on the massive internal data sets. “We were able to put our target voters through some really complicated modeling, to say, O.K., if Miami-Dade women under 35 are the targets, [here is] how to reach them,” said one official. As a result, the campaign bought ads to air during unconventional programming, like Sons of Anarchy, The Walking Dead and Don’t Trust the B—- in Apt. 23, skirting the traditional route of buying ads next to local news programming. How much more efficient was the Obama campaign of 2012 than 2008 at ad buying? Chicago has a number for that: “On TV we were able to buy 14% more efficiently … to make sure we were talking to our persuadable voters,” the same official said.

The numbers also led the campaign to escort their man down roads not usually taken in the late stages of a presidential campaign. In August, Obama decided to answer questions on the social news website Reddit, which many of the President’s senior aides did not know about. “Why did we put Barack Obama on Reddit?” an official asked rhetorically. “Because a whole bunch of our turnout targets were on Reddit.”

That data-driven decision making played a huge role in creating a second term for the 44th President and will be one of the more closely studied elements of the 2012 cycle. It’s another sign that the role of the campaign pros in Washington who make decisions on hunches and experience is rapidly dwindling, being replaced by the work of quants and computer coders who can crack massive data sets for insight. As one official put it, the time of “guys sitting in a back room smoking cigars, saying ‘We always buy 60 Minutes’” is over.

In politics, the era of big data has arrived.



“Any man who tries to be good all the time is bound to come to ruin among the great number who are not good. Hence a Prince who wants to keep his authority must learn how not to be good, and use that knowledge, or refrain from using it, as necessity requires”.

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November 7, 2012, 05:24 PM

This stuff excites me, mainly because this is what i do within organizations (e.g., like money ball). Id be interested in learning more about hte statistical models they used, and more importantly the research approach (its easy to get false positives or over look true negatives, with especially that many variables!)

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November 8, 2012, 03:37 AM

I work on this stuff too.

You would be surprised 1) how much of it is just PR, 2) how much sway personal politics/connections play regardless of what the data guys say, 3) how little ROI you get on these models. It is the way of the future, but it's also glamorized into being something that it's not. PM if you have specific questions.
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November 8, 2012, 09:11 AM

I just want to know where I can get the green lights from....shit is cool.


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November 8, 2012, 09:33 AM

Just read.....wow $1 billion...I had no idea it was that high now days. I was tripping when I heard Hundreds of million$.

I do love the efficiency they are doing...No more guess work, hunches and following old wore out trends. Knowing who, what and when is great.

Side note:
I always love the feel of My City right after elections. People are more polite, you see more smiles....
You can just feel the vibe.
The first part I notice is cars/drivers letting you cut in or out and more hand waves.

THis shit will end late January though.


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November 8, 2012, 10:35 AM

People in DC are pleased because a number of Council Members were voted out and they changed the law governing how DC Council members can be terminated from service for ethics violations.

Surprisingly one of the least corrupt officials Michael Brown, was voted out and the candidate did so by mostly focusing on a few speeding tickets which led to license suspensions and a personal financial problems.



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November 8, 2012, 12:28 PM

Interesting read, thanks.

“Because a whole bunch of our turnout targets were on Reddit.”

Not sure they needed intense data mining to discover that the typical redditor is all over the presidential D.
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November 8, 2012, 02:18 PM

Quote:
Originally Posted by Heist View Post
People in DC are pleased because a number of Council Members were voted out and they changed the law governing how DC Council members can be terminated from service for ethics violations.

Surprisingly one of the least corrupt officials Michael Brown, was voted out and the candidate did so by mostly focusing on a few speeding tickets which led to license suspensions and a personal financial problems.

I voted for him.
what do you mean? im unfamiliar with all of this.


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November 8, 2012, 03:02 PM

Quote:
Originally Posted by vonstallin View Post
I voted for him.
what do you mean? im unfamiliar with all of this.
He had his drivers

Michael Brown, D.C. Council's advocate for tax increase, owes back taxes on home

While Brown Claims He's the Victim of Theft of Campaign Funds, Opponents Say He's Still to Blame: DCist

D.C. Councilmember's Driver's License Suspended Multiple Times for Traffic Citations: DCist

#352: 08-27-97 - Michael A. Brown Agrees to Plead Guilty to Campaign Financing Violation



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November 8, 2012, 03:58 PM

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Originally Posted by scarab View Post
Interesting read, thanks.

“Because a whole bunch of our turnout targets were on Reddit.”

Not sure they needed intense data mining to discover that the typical redditor is all over the presidential D.
A lot of data-mining comes down to making stupidly obvious strong conclusions like this, or finding very tiny correlations (very low value/usefulness).

I don't know the finally tally on how much was spent on the election, but keep in mind that most of that money is simply passed on to TV/print/Internet/DM.
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November 8, 2012, 06:21 PM

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Originally Posted by Witold View Post
I work on this stuff too.

You would be surprised 1) how much of it is just PR, 2) how much sway personal politics/connections play regardless of what the data guys say, 3) how little ROI you get on these models. It is the way of the future, but it's also glamorized into being something that it's not. PM if you have specific questions.
You bring up a good point, and I will say the data guy will have a very big part regarding point 2 as well. The reason being is, we know people get swayed by political sway/connections, the question is WHO is more susceptible in becoming swayed, and we should focus on that kind of person. Its the same thing, you gotta make ap redictive model and then target those people. Not only that, the degree to which tehy will be swayed, based on what kind of stuff u do. Ur pretty much making profiles for different kinds of people, and what kind of stuff works on them. its pretty sickening, but htats what marketing is. The big companies use all of htese statistical tactics lol.


I agree, if you can't tie in ROI into these models, its pretty useless. That being said, although I do not have a background in campaigning, I think determining a ROI figure of sorts could be done, although more difficult than an organizational context. (keeping in mind, i think the ROI in this context would be more in terms of increase voters rather a $ ammount). Based on your results, you adjust your campaign strategy to target a specific variable in your analysis (e.g., demographic), and based on the voting results, you determine that it did increase voting in that demographic for a candidate.

Question becomes, how do you know whether it was due to the strategy or other reasons? The analysis should control for other variables, buteven then you still will not know for sure since u cannot control for everything. U can use archival data to see the extent to which voting demographics differ from previous elections. But i agree the reality is, you cant be sure with your ROI figures becuase u arent hypothesizing in a controlled environment where u can rerun tests, BUT you should be able to come up ith something at least. and yes please pardon the example, it get smuch more complex than that in regards to the statistical models and more importantly the research approach before u analyze the stuff so u dont make overly simplistic conclusions based on false results.

In an organizational context, with the right data, its very easy to tie in ROI to the results, which I do regularly along with mission fulfillment (more important for government or nonprofit, however, a little bit hard to get ROI in that context), otherwise the C-suite just doesnt care what i have to say or do.

In terms of significance of results, Its really easy to come up with significant results, how relative and valid htey are, is a completely different ball game . It just depends on how rigirous and statistically sound their research/analysis was to come up with that conclusion, which is the most important thing when it comes to work like this.

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November 8, 2012, 08:06 PM

On point 2, I meant internal politics. Data guys often present seemingly the only logical path, but the candidate still decides to go with something stupid because his closest adviser says so.

There is an ongoing effort to figure out ROI for each contact. It's pretty hard to do. Too many fluid variables. But many people are trying to crack this nut.

The other issue is that when you have 50 million in the bank 3 days before the election, people don't look at models all that much. They just spend the 50 million to the point of saturation on whatever options they have left. If they have 0 in the bank, they spend nothing and models don't matter. In a campaign, every department is begging for more money to do something useful. Each of these departments has lots of power. To suddenly put the data modelers on top and deciding who gets to do what is a very big task.

That said, things are moving fast right now. In two cycles, things will be unrecognizable.

BTW, you know how Facebook had that big banner urging people to vote, flag themselves as having voted, etc? Well, not everyone got that message. They were split testing various scenarios to see what effect Facebook can have on turnout. Data guys will be pouring over this data in the next few months. Pretty neat.
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