Editors' Cuts

Can Insurers Learn from the Winning Campaign's Data-driven Ground Game?

Pat Speer
Insurance Experts' Forum, November 9, 2012

I happened across a story posted by Time’s White House Correspondent Michael Scherer, who lauded President Obama’s “ground game” for its ability to exploit data and predictive analytics to help nail the election’s outcome.

Using statistics derived from data to predict outcomes is becoming big business. Consider statistician and New York Times blogger Nate Silver, who correctly predicted presidential results in 50 out of 50 states. In the 2008 presidential election, he predicted 49 out of 50.

We don’t know if President Obama’s IT team took into account some of Silver’s predictive models. We do know, based on Scherer’s report, that on November 4 on the condition of embargo until after the election results were confirmed, senior campaign advisors told Scherer about “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.”

This massive data effort is worth studying. Back when Obama’s 2012 re-election campaign was formed, campaign manager Jim Messina went on record promising a metric-driven kind of effort. “We are going to measure every single thing in this campaign,” he was quoted as saying. To do so, notes Scherer, Messina established an analytics department for the Chicago headquarters roughly five times larger than that of its 2008 operation, and hired an official "chief scientist" to make sense of the numbers and keep them in play.

We all know that expanding the IT department and hiring a “chief scientist” doesn’t guarantee anything except a bloated budget—unless results are seen that meet objectives and fuel rapid success. Obama had learned this the hard way in 2008 when campaign officials created too many data bases that didn’t integrate, causing duplication and reconciliation problems, Scherer added.

But some of the IT group’s systematic efforts to resolve this are worth noting. For the 2012 election, the IT department created a single massive system that could do more than slice and dice demographic data. It was designed to integrate data collected from pollsters, fundraisers, field workers and consumer databases, and blend that data with social media and mobile contacts. Once that was accomplished, the data was merged with the main Democratic voter files in the swing states.

By employing predictive analytics, the data revealed voters who would be likely persuaded by certain types of appeals. The first voters identified as prime candidates for special attention from their local volunteers were those who had unsubscribed from Obama’s 2008 campaign e-mail lists. They tested this assumption, then, based on confirmation, quickly and successfully aligned volunteers to implement.

Using the same intelligence derived from its initial targets, the group employed a metric-driven donation effort, tracking results in real time and making adjustments to the methodology based on results. For example, the campaign’s Quick Donate program, which allowed repeat giving online or via text message without having to re-enter credit-card information, resulted in these targets giving about four times as much as other donors. The IT group in Chicago responded quickly, expanding the effort, which netted the campaign effort an extra $1 billion, according to estimates.

Data stores were also tapped for use on a Facebook “get out the vote” effort. Late in the campaign, Facebook users who had downloaded an app received messages with pictures of their friends in swing states, encouraging the users to take certain actions to help the cause. The campaign found that roughly one in five people contacted by a Facebook “friend” acted on the request, in large part because the message came from someone they knew, notes Scherer.

These are but some of the ways Obama’s IT team leveraged data in order to reach their objectives; I recommend reading Scherer’s entire piece.

I believe there’s a lot to be said for organizations that recognize the value technology and processes play in accomplishing objectives, whether corporate or political. And whether you approve of Obama’s election, his record, policies or agenda for the future, you have to hand it to him for employing a scientific means to an end.

Pat Speer is an editorial consultant for Insurance Networking News.

Readers are encouraged to respond to Pat by using the “Add Your Comments” box below. Shealso can be reached at patricia.speer@sourcemedia.com.

This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.

Comments (2)

Great post Pat - thanks. Really shows the power of BI. Much we can learn in insurance.
Steve

Posted by: Steve Robins, FirstBest Systems | November 23, 2012 10:53 PM

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Good article Pat - great minds think alike and I've already drafted a 3-part series on the Obama campaign use of technology and its application to the insurance community. It will appear at http://www.marketluminary.com

Posted by: Chester G | November 9, 2012 1:16 PM

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