your data, the cost to them pales in comparison to how much the data is worth when they sell advertisers access to you. Your data allows anyone, anywhere, to purchase digital advertising that targets you for whatever purpose—commercial, political, honest, nefarious, or benign—on the right platform, with the right message, at the right time.
But how could you resist? You do everything electronically because it’s convenient. Meanwhile, the cost of your convenience is vast: you are giving one of your most precious assets away for free while others profit from it. Others make trillions of dollars out of what you’re not even aware you are giving away each moment. Your data is incredibly valuable, and CA knew that better than you or most of our clients.
When Alexander Tayler taught me what Cambridge Analytica could do, I learned that in addition to purchasing data from Big Data vendors, we had access to our clients’ proprietary data, aka data they produced themselves that was not purchasable on the open market. Depending on our arrangements with them, that data could remain theirs or it could become part of our intellectual property, meaning that we could retain their proprietary data to use, sell, or model as our own.
It was a uniquely American opportunity. Data laws in countries such as the United Kingdom, Germany, and France don’t allow such freedoms. That’s why America was such fertile ground for Cambridge Analytica, and why Alexander had called the U.S. data market a veritable “Wild West.”
When Cambridge Analytica refreshed data, meaning updating the locally held database with new data points, we struck a range of agreements with clients and vendors. Depending on those agreements, the data sets could cost either in the millions of dollars or nothing, as Cambridge sometimes struck data-sharing agreements by which we shared our proprietary data with other companies for theirs. No money had to change hands. An example of this comes from the company Infogroup, which has a data-sharing “co-op” that nonprofits use to identify donors. When one nonprofit shares with Infogroup its list of donors, and how much each gave, it receives in return the same data on other donors, their habits, fiscal donation brackets, and core philanthropic preferences.
From the massive database that Cambridge had compiled from all these different sources, it then went on to do something else that differentiated it from its competitors. It began to mix the batter of the figurative “cake” Alexander had talked about. While the data sets we possessed were the critical foundation, it was what we did with them, our use of what we called “psychographics,” that made Cambridge’s work precise and effective.
The term psychographics was created to describe the process by which we took in-house personality scoring and applied it to our massive database. Using analytic tools to understand individuals’ complex personalities, the psychologists then determined what motivated those individuals to act. Then the creative team tailored specific messages to those personality types in a process called “behavioral microtargeting.”
With behavioral microtargeting, a term Cambridge trademarked, they could zoom in on individuals who shared common personality traits and concerns and message them again and again, fine-tuning and tweaking those messages until we got precisely the results we wanted. In the case of elections, we wanted people to donate money; learn about our candidate and the issues involved in the race; actually get out to the polling booths; and vote for our candidate. Likewise, and most disturbing, some campaigns also aimed to “deter” some people from going to the polls at all.
As Tayler detailed the process, Cambridge took the Facebook user data he had gathered from entertaining personality surveys such as the Sex Compass and the Musical Walrus, which he had created through third-party app developers, and matched it with data from outside vendors such as Experian. We then gave millions of individuals “OCEAN” scores, determined from the thousands of data points about them.
OCEAN scoring grew out of academic behavioral and social psychology. Cambridge used OCEAN scoring to determine the construction of people’s personalities. By testing personalities and matching data points, CA found it was possible to determine the degree to which an individual was “open” (O), “conscientious” (C), “extroverted” (E), “agreeable” (A), or “neurotic” (N). Once CA had models of these various personality types, they could go ahead and match an individual in question to individuals whose data was already in the proprietary database, and thus group people accordingly. So that was how CA could determine who among the millions upon millions of people whose data points CA had were O, C, E, A, N, or even a combination of several of those traits.
It was OCEAN that allowed for Cambridge’s five-step approach.
First, CA could segment all the people whose info they had into even more sophisticated and nuanced groups than any other communications firm. (Yes, other companies were also able to segment groups of people beyond their basic demographics such as gender and race, but those companies, when determining advanced characteristics such as party affinity or issue preference, often used crude polling to determine where people generally stood on issues.) OCEAN scoring was nuanced and complex, allowing Cambridge to understand people on a continuum in each category. Some people were predominantly “open” and “agreeable.” Others were “neurotic” and “extroverts.” Still others were “conscientious” and “open.” There were thirty-two main groupings in all. A person’s “openness” score indicated whether he or she enjoyed new experiences or was more inclined to rely on and appreciate tradition. The “conscientiousness” score indicated whether a person preferred planning over spontaneity. The “extroversion” score revealed the degree to which one liked to engage with others and be part of a community. “Agreeableness” indicated whether the person put others’ needs before their own. And “neuroticism” indicated how likely the person was to be driven by fear when making decisions.
Depending on the varied subcategories in which people were sorted, CA then added in the issues about which they had already shown an interest (say, from their Facebook “likes”) and segmented each group with even more refinement. For example, it was too simplistic to see two women who were thirty-four years old and white and who shopped at Macy’s as the same person. Rather, by doing the psychographic profiling and then adding to it everything ranging from the women’s lifestyle data to their voting records to their Facebook “likes” and credit scores, CA’s data scientists could begin to see each woman as profoundly different from the other. People who looked alike weren’t necessarily alike at all. They therefore shouldn’t be messaged together. While this seems obvious—it was a concept supposedly already permeating the advertising industry at the time Cambridge Analytica came along—most political consultants had no idea how to do this or that it was even possible. It would be for them a revelation and a means to victory.
Second, CA provided clients, political and commercial, with a benefit that set the company apart: the accuracy of its predictive algorithms. Dr. Alex Tayler, Dr. Jack Gillett, and CA’s other data scientists constantly ran new algorithms, producing much more than mere psychographic scores. They produced scores for every person in America, predicting on a scale of 0 to 100 percent how likely, for example, each was to vote; how likely each was to belong to a particular political party; or what toothpaste each was likely to prefer. CA knew whether you were more likely to want to donate to a cause when clicking a red button or a blue, and how likely you were to wish to hear about environmental policy versus gun rights. After breaking people up into groups using their predictive scores, CA’s digital strategists and data scientists spent much of their time testing and retesting these “models,” or user groupings called “audiences,” and refining them to a high degree of accuracy, with up to 95 percent confidence in those scores.
Third, CA then took what they had learned from these algorithms and turned around and used platforms such as Twitter, Facebook, Pandora (music streaming), and YouTube to find out where the people they wished to target spent the most interactive time. Where was the best place to reach each person? It might be through something as physical and basic as direct paper “snail”
