Consultant Case Study
than I once was because he changed the rules in the middle of the game to let himself run for a third term.” We also found that the anger over term limits cut across all geo- graphic, partisan and racial lines. Even among those who said that they strongly supported the mayor’s reelection, 48 percent still said they viewed the mayor less favorably as a result of the term limits issue. Part of the rationale to repeal the limits was that an ex-
perienced hand was needed to navigate New York City through the economic crisis. But the campaign had to be careful when making this case. As someone who made his fortune on Wall Street, Mike Bloomberg had instant cred- ibility on financial issues. But history has shown that New York City has a love-hate relationship with Wall Street. Vot- ers clearly understand how important the financial sector is to the city’s economy, but they also feel that city govern- ment caters too much to Wall Street, and that Manhattan is favored over the outer boroughs. In addition to the term limits issue, Bloomberg also had
to contend with what was probably the worst electoral en- vironment for incumbents since 1994. On the right, there was a generic anti-government, anti-incumbent sentiment visible in the “tea parties” and healthcare town halls. On the left, there was a pro-change euphoria coming off of President Obama’s win in 2008. At the start of the campaign, 58 percent of New York
City voters said they valued change over experience, and a full 82 percent felt it was important for the city’s mayor to be a strong supporter of President Obama. Even among Republicans, 76 percent wanted a mayor who supported the president. It’s one of the reasons the campaign decided to highlight Bloomberg’s close ties to the president. Al- though we knew that Obama would likely endorse the Democratic nominee, we hoped it would be a pro-forma endorsement, and that Obama would not campaign in New York City.
Only In New York As the Democratic primary sorted itself out, we faced down another of Bloomberg’s major challenges—the partisan and demographic makeup of New York City. City Comptroller Bill Thompson, one of two likely Democratic candidates for mayor is an African-American who was hoping to capi- talize on an African-American voter base newly energized by Obama’s election. And whoever ended up being the Democratic nominee would enjoy a large party registration advantage. Democrats in New York City hold a six-to-one advantage over Republicans. Bloomberg’s decision to switch his party affiliation to independent, and his progressive posi- tions on most issues was what led many Democratic con- sultants, including my firm, Strategic Telemetry, to support him. Nevertheless, Bloomberg would be running on both the Republican and Independent lines, and his main chal- lenger would be running on the Democratic line. There were certainly some broad generalizations that could be made about the election, including the fact that
Bloomberg was stronger among Republicans and inde- pendents than he was among Democrats. But with 69 per- cent of New York City voters registered as Democrats, that wasn’t anything to build a strategy around. The answer was an unprecedented microtargeting program. Although Bloomberg had the resources to attempt to call
every voter in NYC, not every voter was reachable. Many had unlisted numbers, lived in cell phone-only households, or were not interested in answering political surveys. The microtargeting models allowed us to predict how voters we weren’t able to reach would have responded to the surveys if they had been contacted. It is one thing to predict a response. It’s another to do so accurately. Luckily we were able to conduct IDs each week throughout the campaign and could test the microtargeting models continuously. We found that the models were con- sistently correct in predicting voters’ ID responses. Although the secret ballot makes it impossible to know if the IDs and models correctly predicted actual voting behavior, there is strong evidence that they did. In fact, the modeled Bloomberg
Looking at voters based on their self-described ideology, rather than just their partisanship, yielded some surprising results.
support the weekend before the election was actually more strongly correlated to the Election District (precinct) results than our hard-IDs were. This was because the hard IDs were available only for the voters who we were able to reach. The microtargeting models were applied to every voter in the city, so we were able to predict the candidate preference of vot- ers even in areas where there were large concentrations of unlisted numbers or security locked apartment buildings that could not be canvassed. We began by modeling all of the standard voter behaviors
that go into any good microtargeting program: candidate sup- port, turnout likelihood and likelihood of being persuadable. The definition of persuadable is somewhat nebulous. It’s fairly simple to predict the likelihood that a voter will say that they are undecided when called, but that could just mean that the voter is not yet paying attention to the race, or that they don’t feel like answering the question. So, while we did build an undecided model, we also built a number of other models designed to more precisely identify true persuadables. The first of these was our “soft voter” model. Soft voters were defined as those who said that they were undecided on whether or not Bloomberg deserved reelection, or who had an opinion, pro or con, but not a strong opinion. Because these voters were not strongly for or against the mayor’s reelection, we felt that they
June 2010 | Campaigns & Elections 15
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80