Barack Beane Obama
One of my favorite books of the last decade was Michael Lewis’s Moneyball. It’s about how Oakland A’s general manager Billy Beane used statistical analysis to offset the advantage of more heavily funded teams and make his small-market team into a contender. I’ve written about it here and here.
I was reminded of that book when I read Michael Scherer’s article, “Inside the Secret World of the Data Crunchers Who Helped Obama Win.” Obama’s campaign had an incredibly sophisticated data-crunching operation for raising money and targeting ads.
An excerpt on fund raising:
For the general public, there was no way to know that the idea for the [Sara Jessica] 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
An excerpt on getting out the vote:
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.”
It probably goes without saying, but I’ll say it anyway: Dick Morris and his ilk are dinosaurs. Like co-blogger Bryan, I bet on various issues to discipline my thinking and to learn. I’ve learned. Losing the bets helped, and the Scherer article helps.
Having credited the sophistication of the Obama campaign, though, I want to point out that the overall message still matters and that Romney had essentially 90 minutes of good campaigning in the midst of a few-months campaign. I’m reminded of a campaign I was involved in against a sales tax increase in Monterey County, one that my band of brothers and I won. After it was over, I found out just how sophisticated the losers were. Of course, it helped that our side needed only one third of the vote to stop the tax increase. Here’s an excerpt from an article I wrote after the campaign about my discovery of the losing side’s sophistication:
We then walked in the other direction and John showed us the headquarters of the Yes on Q campaign, which had already been vacated and was for rent. The For Rent sign stated that the building had 20 phone lines. We peered into the building and saw that it was almost the size of a high-school gymnasium. John explained that during the campaign the building was a hive of activity. They had had maps of the voting area, block by block, and had obtained data on who had voted and who hadn’t, so that they could target their efforts. So this is part of how they had spent their $450,000, I said to myself. I felt a quiet awe. We had taken them on and beat them. It was as if we had fought our way across no-man’s land between the two enemy trenches on the assumption that although there were more of the enemy, they had the same kind of weapons. Then when we got there, we discovered that while we had M-16s, they had machine guns. Of course, there are two problems with the analogy. First, our weapons were our words and ideas and, compared to their words and ideas, ours were machine guns and theirs were M-16s. Second, I never regarded them as the enemy. But you get the point.