Campaigning in the Age of Big Data

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In the past, politics had little targeting to individual voters. Where this existed, it was achieved by local groups that understand the locality such as churches. While locally successful, these efforts could not share insights upward in the campaign or easily with other local efforts in the nation. And these efforts lacked the data analysis to learn where their assumptions were wrong and correct them.

A few years ago, as Big Data took hold of the private sector, it was a story of economic margins. Companies knew that modeling individual consumers, machines, and employees was a revenue-positive proposition but the costs of collecting and analyzing that data were prohibitive. Big Data is changing the story and enabling companies to do this modeling, for example tailoring experiences of individual customers, by providing tools that can work with new volumes of data and do so in a cost effective way.

Campaign managers are now learning a similar story: Big Data can empower campaigns to understand, target, and customize content to individual voters. The Big Data of voting includes surveys, web traffic, voter history, social media, varied consumer data, and more. Here are four ways it will change campaigning:

1. Swing Voters: One technical definition of a “swing voter” is a voter whose ratings of two candidates on the issues are sufficiently similar. In a world where voters are increasingly likely to publish their opinions on social media, share information with campaigns through internet surveys, and traffic news websites sharing cookies with one another, campaigns have many sources of information about individual voters. The data reveal which issues are most important to the election as well as which voters are likely to have values-alignment with multiple candidates. Campaigns will use this information to target these voters and improve their campaign margins.

2. Custom Messaging: Candidates are known for spinning different messages by region. From gun laws to “cheesy grits” candidates know that voters differ in values. Campaigns will use Big Data to identify these differences and leverage them. When you and your cousin down the street log into Youtube, you will both see different messages from the same campaign; all to ensure the most relevant information gets to each of you. Moreover, this content will be tested by showing slight differences to similar voters and evaluating their responses. Everything that could be mentioned in the cable news cycle will be rigorously tested and optimized, then customized for you.

3. Unlikely Voters: While almost 90% of elderly, highly-educated persons vote, they are atypical for the country. The overall numbers increased again in 2012 but left over 40% of voters as untapped potential, with the number not dropping much below that in the last century. Campaigners understand that voters are social, seeking to act like those around them. They also understand that a latent demand of 40% is game-changing. They will use Big Data to target messages explicitly to voters whose values are aligned with the candidate but are unlikely to vote. They will target messages of an opponent attacking values and of similar others standing up to vote and defend the group. While “Rock the Vote” is an intriguing idea, not everyone wants to be a rockstar. Campaigners will start showing voters that people like them, and those in their social group, are voting for their candidate through tools like Facebook and Youtube.

4. Identifying Influencers: Voting is known as a “normative decision”, meaning that voters unconsciously attempt to vote as they feel their friends would. Influencing voting, whether who to vote for or whether to vote at all, is largely a matter of influencing how a voter feels their peer group would act. By leveraging social media, campaigns will target key influencers to shape these perceptions. Unlike traditional media, influencers will not just be those that are vocal but will be those seen as representative of a social clique. By doing so, campaigners will leverage the politics of small groups to win national elections.

Data science cannot change the underlying psychology of voting. Voter behavior is still largely explained by familiarity bias and normative decision making, two concepts that can be summarized by the questions: “Which name and face seems most familiar to me?” and “Who would my friends vote for?” But data science can change the way politicians garner votes and how effective they are in campaigns.

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