Over the last few days we’ve gone over how campaigns generally target the voters they try to influence to vote for their candidates. We’ve looked over how the old technique was to create a list of voters from the county’s voter rolls to build a list of likely voters. The idea being that if a voter cast a vote in the last few elections, as selected by the campaign, then they were to be targeted with campaign messaging.
The problem is that other than the basic demographics and voting history, there was little else that the campaigns can use to craft a focused message intended to influence a specific voter. Some campaigns augmented their data with generalized issue traits gathered from door-to-door, face-to-face meetings with the targeted voters and candidate forums. They also rely on robocalls and internal polling data to get a snapshot of issues and voter trends. But, at most, the information is limited to the target list and has significant inherent biases.
That is why the voter turnout is so low, the messaging only focuses on those likely to vote leaving a larger universe of voters out of the game. But, even the small pool of likely voters can be influenced if the messaging is targeted to their specific needs or wants.
Each election cycle there are the numerous get new voters to register and get-out-the-vote campaigns and every time they fail to achieve their goals. That is because they do not address the specific triggers each individual needs to be engaged into voting. Until now, that data was not available to the campaigns.
Today, I’ll introduce you to how we can build a psychographic profile for our voter universe. Once we have the profile, we can then build a campaign strategy to generate votes for our candidate.
Before we can begin the psychographic profile, we need to understand how we gather the data sets we need to build it. For that we use a concept called data mining.
Data mining is the collection of large data sets and applying algorithms to them to see if patterns exist. There is lots of data about each of you that is public information. Marketers use data mining to send you advertisements each day. In our case we are harvesting data sets from government sources and analyzing them to build a profile for each voter.
The federal government publishes millions, upon millions of data sets each year as part of its e-government initiatives. In 2011, the Texas Legislation adopted Senate Bill 701 and it went into effect on September 1, 2011. The legislation encourages Texas government agencies to make their information available to the community for accountability and government responsiveness. These data sets contain vast amounts of data that various Texas agencies hold in their computers.
For example, there is a data set of every real estate agent licensee. There is another one of registered engineers in the state. Obviously, criminal records are public information, as well as bankruptcies. Each dataset contains vast amounts of information about Texans.
By harvesting the federal and state data, the data can be linked to voter rolls. Now that we know a voter is a registered engineer or a lawyer, we can start to build a financial profile about our voter. By adding the value of their house or how much they owe on their mortgage, we start to fill in the blanks on their financial wellbeing. This is called data mining.
There are also sets of data that can be purchased. For example, credit companies sell data about their account holders. Stores sell data about their shoppers. That’s why the cashier asks you for your zip code. The cellphone companies sell their data as well as the insurance companies and so on. Basically, if you subscribe to a magazine, drive a car, or shop, we can get data on your regular habits.
But that is just the tip of the iceberg. There is another technique called data scraping. With data scraping we look at public-facing social media channels or other websites and we scoop up that data into our data set. For example, we may come across a voter’s business profile on a company website. Immediately we have a picture of the voter, their business title and the company they work for. We may also have an email address and a telephone number to add to our data sets.
Social media channels bring in a trove of data about our voters in that they tell us how many kids, if any, a voter has and what the voter’s trigger issues are, i.e. medical problems, financial issues, or simply whether they care about politics, or not. The data is out there and with data mining and data scraping we can collect it all and dump into our voter lists.
So now that we have vast amounts of data, what do we do with it?
It’s called psychographic profiles, or audience segmentation. Marketers have been perfecting this for some time now for marketing purposes. Psychographics is making a prediction about an individual by extrapolating data about a voter. Basically, the more information we know about a voter, the more likely we are to predict not only what we can use to trigger a response from them – vote for our candidate – but also know how best to get them to go to the polls and bring their family and friends along.
In 2016, Cambridge Analytics CEO, Alexander Nix explained to a seminar’s audience why marketing has been wrong all this time. According to Nix, marketers make assumptions that messaging to all women is the same because of their gender. Nix added that race is used to message Blacks and Hispanics on the assumption that race defines what we want.
Nix proposed that instead of demographics as the messaging strategy, the real strategy lay in targeted messaging through data. Nix argued that the reason Trump seemed so “uncertain about his message” on the campaign trail and why Trump continues to contradict himself is because Trump is used to delivering messaging to specific groups, i.e. he tells one group what they want to hear, and another what they want to hear.
The idea behind psychographic profiles is to build a messaging scheme targeting voters specifically using their trigger issues to persuade them to vote for the candidate. It’s called discreet targeting, and as proved by Trump’s election, discreet targeting allows campaigns to bypass the wide net messaging concept and build specific messaging for voters that can be persuaded to vote for the candidate.
Its discreet nature means that if your profile is not inclined towards a border wall or for Republicans then you didn’t see the Trump advertisement. It also explains why the polls were so wrong as to Trump’s victory. Simply, they were polling the wrong voters.
These concepts are not new. Barack Obama used a rudimentary system that became the Democratic Party VAN system that Escobar and Fenenbock are using today. Donald Trump used Cambridge Analytics to get elected.
This is also precisely the nexus over the controversy of the Russian interference in 2016. Dark messaging, i.e. messaging targeting a specific segment of the population can be used by anyone, including the Russians, to persuade people to do things. Marketers do it all the time.
Politics is just a marketing scheme where the “product” is the candidate and you are the sheep.
Tomorrow, I’ll continue with what types of data is available and how you can experience it for yourself. I’ll also share a few more examples of what the El Paso Votes APP has in its repertoire.
On Monday, I’ll tie it all together by laying out an example of how we can use what we have today to force a change in the elections results between Chavez, Escobar and Fenenbock, even at this late stage in the election.
