Consultant Case Study
ogy, rather than just their parti- sanship, yielded some surprising results. Voters who said that they always voted Republican, and who described themselves as “very lib- eral” were overwhelmingly pro- Bloomberg. The fact that they supported the mayor made sense. What was surprising was that there were still solid Republican voters who described themselves as “very liberal.” This was just another ex- ample of the unique challenges in microtargeting New York City’s voters.
Targeting The Issues As with any campaign, determin- ing which voters to target was only half the challenge. We also needed to know what issues these voters cared about. In most campaigns, microtargeting is used to maximize resources. That is to save money by targeting the voters most likely to be persuadable. In the case of the Bloomberg campaign, budget was not as much of an issue (although
were persuadable. We also developed a “shifter” model. Each week we would re-ID a subset of voters. The shifter model predicted the likelihood that a voter would change their can- didate preference over time. There were other specialty models that we built to help find specific types of persuadable voters. One example is the “job not reelect” model. This helped to find voters who gave Bloomberg high job approval ratings, but did not believe that he deserved reelection. Because New York City is so overwhelmingly Demo-
cratic, party registration was not particularly useful. Ideol- ogy however, was extremely useful. The more liberal a vot- er was, the more likely they were to support Bloomberg. Also, knowing a voter’s ideology helped us determine the appropriate messages to motivate them. Liberal voters re- ceived messages about Bloomberg’s progressive positions on social issues and the environment. Conservative voters would receive messages about Bloomberg’s record bring- ing down crime and improving schools. The first round of IDs included a question about ideology. From that, we built our “liberal” model that predicted the likelihood that a voter would describe themselves as “somewhat” or “very” liberal. This model was used extensively through- out the campaign, and was routinely tested with another round of IDs right after the primary to confirm that the model was accurately predicting voters’ ideology, and that there had not been a sudden shift in the ideology of New Yorkers.
Looking at voters based on their self-described ideol- 16 Campaigns & Elections | Canadian Edition
the campaign did make a point to be as efficient as possible). One resource that was as limited for Bloomberg as it is for any other candidate was the voters’ attention. Voters are constantly bombarded with messages not just from political campaigns, but from all the other advertisers competing for their attention on television, radio, the internet and in the mail. By targeting voters based on the issue that they cared about we were able to maximize the chances that a mes- sage would catch the voters’ attention. The key to this was a series of models predicting the voters’ likelihood of picking any one of 13 issues as one of their top two concerns. We asked voters about their top two issues because the econ- omy was the top issue for the vast majority of voters. We already knew that the economy would be the cornerstone of Bloomberg’s message. The issue models allowed us to tar- get communications about other issues to the voters most likely to be interested. On Election Day, Bloomberg was able to defy the na-
tional trend, becoming one of the few incumbents to win reelection, in part because he was able to successfully ma- neuver around the issues of partisanship, term limits and anger towards Wall Street by emphasizing his independence, and highlighting issues that were more important to voters than term limits.
Ken Strasma is the president of Strategic Telemetry. He was the head microtargeter for Michael Bloomberg’s 2009 campaign and Barack Obama’s campaign in 2008.
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