By Bradley Honan & Elisabeth Zeche | January 17, 2024 | Original Article
The confluence of three important factors provides campaigns with new ability to analyze micro-level election return data and predict future voting behavior with far greater certainty than ever before.
Factor Number One: Americans are increasingly self-segregating themselves by cultural, social, and political beliefs in terms of where they are choosing to live. Much is made of the national political map of the United States, and how the country is divided into blue states and red states and much has been made of the urban versus rural divide.
But zooming into neighborhoods across the country, it’s clear that Americans are increasingly sorting themselves block-by-block and street-by-street by their political beliefs, ideology, and indeed their political identity.
Democrats are increasingly residing near fellow Democrats and Republicans are choosing to live next to fellow Republicans.
This reality means that political targeting is both easier and more efficient to do than ever before. Knowing exactly where somebody lives, provides a pretty good idea of how they voted and will vote in the future, showing that micro level political data is a gold mine of rich political insights.
Factor Number Two: Independent voters, as Bruce E. Keith noted in his book, Myth of the Independent Voter, have strong partisan leanings. Most Independents are not really all that “independent” after all. Keith’s work is confirmed by the most recent Gallup polling data from December. While 40 percent of American voters initially describe themselves as Independents (the largest voting bloc), when a follow-up question is asked about which party they lean towards, all but 7 percent lean towards either Democratic or Republican party, suggesting the number of truly Independent voters is less than 1 in 10 American voters.
Given their partisan leanings, it’s therefore not surprising that Independent voters, too, are self-segregating themselves by the micro-neighborhood they live in.
And given that Independent voters are casting their ballots in ways that track the partisan makeup of their neighborhood, campaigns must far better understand which Independent voters they are targeting and pursuing as many will likely never vote for your candidate. As an example, Independents living in neighborhoods that are predominantly Republican, largely vote as their GOP neighbors do.
There is a very clear implication here. Democrats communicating with these kinds of Independent voters who lean towards the GOP risks “waking them up” and ensuring they cast a ballot against the Democrat.
Far better to know exactly who these voters are and let “sleeping dogs lie” as the expression goes, and hope these voters stay home and don’t cast a ballot.
Election district-level voting data analysis can also help pinpoint the DINO (Democrats in Name Only) and RINO (Republicans in Name Only) voters who routinely stray from voting for the party they are registered with, again improving targeting insights. A Democrat who targets the supposedly “base” DINO voters risks waking up voters who will ultimately cast a ballot for the Republican.
Beyond a general election context, we have also seen the power of election district data in guiding primary campaigns and indeed we have found that ideological self-segregation exists there, too. Election Districts that consistently support middle-of-the-road Democrats in primary elections vote the same way ideologically election after election – they support the middle-of-the-road Democrat. By contrast, certain Election Districts consistently vote for the most progressive Democrat in primary election after primary election – and this is relevant for voter targeting and deploying the right message to the right audience.
Taken together, the confluence of these factors means that predicting voting behavior is made easier with the right kind of data analysis of past election returns at the most micro level possible. Gathering, sorting, and analyzing election return data at the ward, precinct, or election district level provides information that is proving to be as fundamental to political campaigns in terms of strategic value as what a campaign learns through a benchmark poll.
To paraphrase former President Barack Obama: it’s not just about red and blue states, it’s the neighborhoods, and specifically the streets.
Bradley Honan and Elisabeth Zeche are partners at the Democratic polling and data analytics firm Honan Strategy Group. Honan is also Co-president of the New York Metro Chapter of the American Association of Political Consultants and serves on the Board of Directors of the Public Relations Society of America’s New York Chapter.
Campaigns & Elections Magazine Op Ed: Why Targeting Micro Neighborhoods Is Key To Winning In ’24
The confluence of three important factors provides campaigns with new ability to analyze micro-level election return data and predict future voting behavior with far greater certainty than ever before.
Factor Number One: Americans are increasingly self-segregating themselves by cultural, social, and political beliefs in terms of where they are choosing to live. Much is made of the national political map of the United States, and how the country is divided into blue states and red states and much has been made of the urban versus rural divide.
But zooming into neighborhoods across the country, it’s clear that Americans are increasingly sorting themselves block-by-block and street-by-street by their political beliefs, ideology, and indeed their political identity.
Democrats are increasingly residing near fellow Democrats and Republicans are choosing to live next to fellow Republicans.
This reality means that political targeting is both easier and more efficient to do than ever before. Knowing exactly where somebody lives, provides a pretty good idea of how they voted and will vote in the future, showing that micro level political data is a gold mine of rich political insights.
Factor Number Two: Independent voters, as Bruce E. Keith noted in his book, Myth of the Independent Voter, have strong partisan leanings. Most Independents are not really all that “independent” after all. Keith’s work is confirmed by the most recent Gallup polling data from December. While 40 percent of American voters initially describe themselves as Independents (the largest voting bloc), when a follow-up question is asked about which party they lean towards, all but 7 percent lean towards either Democratic or Republican party, suggesting the number of truly Independent voters is less than 1 in 10 American voters.
Given their partisan leanings, it’s therefore not surprising that Independent voters, too, are self-segregating themselves by the micro-neighborhood they live in.
And given that Independent voters are casting their ballots in ways that track the partisan makeup of their neighborhood, campaigns must far better understand which Independent voters they are targeting and pursuing as many will likely never vote for your candidate. As an example, Independents living in neighborhoods that are predominantly Republican, largely vote as their GOP neighbors do.
There is a very clear implication here. Democrats communicating with these kinds of Independent voters who lean towards the GOP risks “waking them up” and ensuring they cast a ballot against the Democrat.
Far better to know exactly who these voters are and let “sleeping dogs lie” as the expression goes, and hope these voters stay home and don’t cast a ballot.
Election district-level voting data analysis can also help pinpoint the DINO (Democrats in Name Only) and RINO (Republicans in Name Only) voters who routinely stray from voting for the party they are registered with, again improving targeting insights. A Democrat who targets the supposedly “base” DINO voters risks waking up voters who will ultimately cast a ballot for the Republican.
Beyond a general election context, we have also seen the power of election district data in guiding primary campaigns and indeed we have found that ideological self-segregation exists there, too. Election Districts that consistently support middle-of-the-road Democrats in primary elections vote the same way ideologically election after election – they support the middle-of-the-road Democrat. By contrast, certain Election Districts consistently vote for the most progressive Democrat in primary election after primary election – and this is relevant for voter targeting and deploying the right message to the right audience.
Taken together, the confluence of these factors means that predicting voting behavior is made easier with the right kind of data analysis of past election returns at the most micro level possible. Gathering, sorting, and analyzing election return data at the ward, precinct, or election district level provides information that is proving to be as fundamental to political campaigns in terms of strategic value as what a campaign learns through a benchmark poll.
To paraphrase former President Barack Obama: it’s not just about red and blue states, it’s the neighborhoods, and specifically the streets.
Bradley Honan and Elisabeth Zeche are partners at the Democratic polling and data analytics firm Honan Strategy Group. Honan is also Co-president of the New York Metro Chapter of the American Association of Political Consultants and serves on the Board of Directors of the Public Relations Society of America’s New York Chapter.
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