Obama’s data-driven strategy has been credited by many as integral in his 2012 election victory, and set the blueprint for the majority of today’s political campaigns. Trump was dismissive, however, telling AP “I’ve always felt it was overrated. Obama got the votes much more so than his data processing machine. And I think the same is true with me.”

Trump’s strategy has instead been to use social media, and the conventional media coverage which follows it, to gain exposure through controversial opinions. He has publicly referred to his social media accounts as being worth “$2 billion in free advertising” to him.

In an analytically-driven election campaign the focus is on targeting swing or undecided voters. Why waste time campaigning to those who are definitely going to vote for you, or those who never will in million years? This technique was pioneered by the Obama campaign in 2012 when a team of over 100 data analysts were tasked with running over 66,000 computer simulations every day.

First, Obama’s analysts in The Cave collected and amalgamated all the data they could from voter registration data, donations, public records and bought-in third party commercial data (including data mined from social media). Next, everybody who had been identified was evaluated on their likelihood of voting for Obama, based on how well their data profile matched that of known supporters.

The team then identified three variables which they believed could influence the election, and which could in turn be influenced by their campaign. These were voter registration numbers, voter turnout and voters’ choices.

Armed with their sophisticated demographic information, targeted campaigns were then launched. These had the aim of increasing voter turnout and registration amongst sectors where the likelihood of backing their candidate was high, and influencing voter choice in sectors where the support metric influenced voters could go either way.

This meant that targeted messages could be despatched – via email, social media posts and browser display ads – depending on whether an individual needed to be convinced to register, vote, or pick the correct candidate.

Doing this is relatively simple – you simply look at what has worked in the past. For example, the activities involving increasing turnout focused on how individuals had previously been encouraged, and whether this had impacted the probability of them leaving the house to vote on election day. One study recently indicated that very simple cues such as informing people who have said they will vote that they will be re-contacted after the election to see if they actually did can have a measurable influence. Do this on a massive scale involving millions of people across a nation and you have a Big Data election campaign.

In the years since then, all parties and candidates – with the one notable exception discussed previously – have enthusiastically launched their own analytics strategies. Platforms such as NGP VAN’s Votebuilder are used by the Democratic Party and their analyses are made available to all candidates. The Republican national congress in response launched their own startup – Para Bellum Labs – to assist their candidates.

On an individual level, too, candidates have publicly entered into partnerships and arrangements with data strategists and lent their endorsement to political analytics platforms.

Clinton’s campaign was backed by technology spun out of that which was developed by Obama’s groundbreaking analytics teams. And those technologies have not stood still. Now commercially packaged and operated by independent consulting and vending entities, they have spent the last four years absorbing cutting edge advances such as machine learning and natural language processing into the analytical mix.

We will find out tomorrow whether this data-driven strategy has worked and maybe some months later whether the extent to which Donald Trumps campaign did use big data analytics, despite his initial dismissive comments.