Some of you are about to get your wish, well, most of it, anyway. I’m going to devote this week to El Paso elections. Some of you have been asking – some have demanded – that I get back to writing about El Paso politics. It’s not that I’m back on El Paso issues, but rather, as I have written before, El Paso is a petri dish for border politics and general election topics. I’m going to use the upcoming election to discuss big data and how it can be used to potentially change the assumed outcome of the March primary.
As you know, the marquee election in El Paso is the open Congressional seat being vacated by Beto O’Rourke. For El Paso, that means that either Norma Chavez, Veronica Escobar, or Dori Fenenbock – in alphabetical order – will be the city’s new Congresswoman. This is because whomever wins the Democratic nomination will be the automatic presumptive congress person in November. The El Paso Votes APP provides us with access to election analytical data that allows me to demonstrate to you how big data analytics can be used in the election. Early voting starts next week, therefore there is a small window to change the dynamics of the election.
By the time we get through the week, I believe that I can demonstrate how the outcome of the election between Chavez, Escobar and Fenenbock is far from determined, regardless of what the various polls show. If one, or more candidate takes the lessons on big data I’m sharing here, and makes a move to change the election dynamics, the outcome might be different from what the polls are showing now. The election of Donald Trump proves how an election can be swayed, regardless of the polls.
I believe that with the use of big data, the outcome can be skewed enough to change the assumed outcome. As we go through the process, you’ll also understand the underlining allegations about the Russian manipulation of the 2016 elections.
So, buckle up and hold on because I’m going to introduce you to data mining, data analysis and psychographic profiles. Say what? Psycho what?!? Let’s not get too ahead of ourselves.
Don’t forget that early voting starts next week on Tuesday.
We should all be familiar with the technique campaigns use to mobilize voters to cast votes in their favor. We are also familiar with the dictum that there is a small core of voters, usually labelled as the “likely voter,” i.e., the voter that is likely to go out and cast a vote, whom campaigns use to get their candidates elected. Many believe that the “likely voter” is the older and usually retired voter, who is active in the election process. At the same time, it is assumed that young people do not usually vote, thus they are mostly ignored by the campaigns.
The campaigns have limited time and limited resources to target the whole voter roll, so they narrow their lists down to “likely” voters so that they can better utilize their resources. In the United States, about 50% of the eligible voters cast a vote each election cycle. Right off the bat, 50% of the voter rolls are discarded. In El Paso, voter turnout is usually worse, so the campaigns have a much smaller voter list to target.
Traditionally, the campaign voter lists are compiled from the voter lists produced by the county election’s department of people who voted. The Chavez, Escobar and Fenenbock campaigns have likely compiled lists from the last four primaries of voters who voted in those primaries. Some, may have included on their lists, voters who cast votes in the previous general elections or may have combined all of them together to create their target lists.
The assumption is that the more votes cast by the voters in the selected lists, the more likely they are to cast a vote in March. The campaigns can then focus on targeting these voters with messaging designed to sway them to vote for their candidate.
That was the old way of messaging voters. There is now a better way.
Extracting data from large data sets is known as data mining. What does data mining have to do with elections? We’ll delve deeper into that later this week. For now, it is important to understand that there is a huge data set about voters on the Internet in the form of social media profiles. For example, your Facebook tells us much about your likes, dislikes, and more importantly, the issues that are most important to you. But, that’s just the tip of the iceberg, that is your online data profile.
Imagine, for a moment, what your initial reaction would be if a campaign operative were to knock on your door and immediately tell you how their candidate will make a difference in your specific life. It won’t be the cookie-cutter superfluous public policy, but a specific solution to a specific problem you are concerned about at that moment.
Combining your social media profile with your voter record allows a campaign to build a profile on you that gives them the tools that are likely to convince you to vote for their candidate. Some of you may feel compelled to pretend that you are above it, that you are too intelligent to be manipulated in that way, but in the end, the big data profile will break down your resistance and make you a believer of the future of electioneering.