A Deeper Look at Big Data and the 2016 US Presidential Election

on October 10th, 2016
Big data

American voters fall within three basic categories: those who will vote Republican no matter what, those who will vote Democrat no matter what, and the rest who can be swayed by the candidates. While this third group is tremendously varied and comprise a wild variety of ideologies, methodologies, and philosophies, they are the targets for candidates. After all, nobody works to earn the buck you’ve already got. It’s the dollars you don’t have you’re working for. Same with presidential candidates.

Candidates have always had bags full of tricks for winning votes, only during the course of the past couple of presidential election cycles have both the candidates and the masses watching the mêlée had access to big data analytics. This particular US election cycle has been notoriously nasty, but data has and will continue to play a prominent role, right up until the media begins predicting a winner — most likely in the wee hours of Wednesday, November 8.

How Candidates Use Big Data to Sway Voters

Long before it’s time to call a winner, campaigns use big data to determine which of the voters are likely to be swayed by a particular candidate, what messages are most likely to garner that voter’s support, and where those voters are likely to see and pay attention to campaign ads and messages. For example, will this voter follow us on Facebook or read our emails? Will that voter show up to elite $5,000 per plate fundraising dinners or log into our website to make a $5 donation? Some potential voters want to hear how a candidate supports their faith, or their freedom of religion. Others like to hear how a candidate will build military might or work toward social liberties like LBGT rights or women’s equality.

Not all campaigns levy big data to its fullest extent. Word on the street has it that the Trump campaign has taken a traditional approach to campaigning, getting their message out via social media, TV and radio advertisements, email campaigns, and good old grassroots “boots on the ground” strategies, like town hall meetings and signs in front yards. The Clinton campaign has relied more heavily on big data, looking for voters that could potentially be swayed by messages of faith, love of country, and signs of dissatisfaction with the current state of the opposition’s party.

How Data Analytics Predicts Who Wins

The candidates have their uses for big data, the rest of mainstream America has their own, as well. Some researchers make names for themselves by attempting to predict the winners of presidential elections before the fact, and a few are quite good at it. The outcomes are predicted based on numerous variables derived from previous election cycles. The difficulty this time around is, well, this election is so darn strange. The campaign trail began some two years ago, with Republican rosters filling up fast to include some seventeen potential candidates at one point. Democrats had a total of six runners at one point. Both Trump and Clinton had strong contenders right up until their respective national conventions met to nominate official candidates.

Do big data analytics trends hold this cycle, as strange as the race and the runners happen to be? Do any past elections bear enough resemblance to this one to hold meaningful insight? Only time will tell. For now, pop some popcorn, pull the tab off your favorite canned beverage, and climb into a comfy seat. It’s going to be a ride.

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