Prior to President Obama’s reelection campaign, standard practices for persuading voters—that is, changing their minds—were unscientific and driven by long-standing assumptions and hunches. Campaigns targeted broad categories of typically “independent” voters and assumed that these voters would respond to a persuasive message. That all changed with the Obama reelection. Campaign leadership knew that 2012 would be different from 2008. Turning out likely supporters was not enough; the campaign had to persuade voters that President Obama was a better choice than Mitt Romney. Daniel Porter, Director of Statistical Modeling for the Obama Campaign, will discuss how his team used the results from a large-scale randomized, controlled experiment to model which individual voters were most likely to be persuaded, and how this model served as the basis for targeting decisions across many aspects of campaign.
Daniel Porter is the cofounder of BlueLabs, a Washington DC based analytics, data and technology company whose clients include political campaigns, nonprofits and corporations.
Prior to founding BlueLabs, Daniel was Director of Statistical Modeling for the 2012 Obama reelection campaign. His team developed individual level statistical models that were used throughout the campaign for fundraising, media buying and state strategy. These models served two primary purposes: to pinpoint which voters were most likely to take an action or hold a belief (i.e. support he President or turn out to vote) as well as to measure the influence a campaign contact had on an individual’s likelihood to take such actions or change their beliefs. Combined, these measures helped the campaign optimize their targeting to maximize their return on investment.