Targeted: My Inside Story of Cambridge Analytica and How Trump, Brexit and Facebook Broke Democracy

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” mail sent to an actual mailbox. It might be in the form of a television ad or in whatever popped up at the top of that person’s Google search engine. By purchasing lists of key words from Google, CA was able to reach users when they typed those words into their browsers or search engines. Each time they did, they would be met with materials (ads, articles, etc.) that CA had designed especially for them.

At the fourth step in the process, another ingredient in the “cake recipe,” and the one that put CA head and shoulders above the competition, above every political consulting firm in the world, they found ways to reach targeted audiences, and to test the effectiveness of that reach, through client-facing tools such as the one CA designed especially for its own use. Called Ripon, this canvassing software program for door-to-door campaigners and phone bankers allowed its users direct access to your data as they approached your house or called you on the phone. Data-visualization tools also helped them determine their strategy before you’d even opened your door or picked up your phone.

Then campaigns would be designed based on content our in-house team had composed—and the final, fifth step, the micro-targeting strategy, allowed everything from video to audio to print ads to reach the identified targets. Using an automated system that refined that content again and again, we were able to understand what made individual users finally engage with that content in a meaningful way. We might learn that it took as many as twenty or thirty variations of the same ad sent to the same person thirty different times and placed on different parts of their social media feed before they clicked on it to act. And knowing that, our creatives, who were producing new content all the time, knew how to reach those same people the next time CA sent something out.

The even more sophisticated data dashboards that CA set up in campaign “war rooms” provided project and campaign managers with metrics in real time, giving them up-to-the-minute reads on how a particular piece of content was working and how many impressions and clicks that content was getting per dollar spent. Right in front of their eyes, they could see what was working and what was not, whether they were getting the return on investment they wanted, and how to adjust their strategy to do better. With these tools, those watching the data dashboards were able to monitor up to ten thousand different “campaigns within campaigns” we were running for them at any given time.

What CA did was evidence-based. CA could provide clients with a clear picture of what they had done, whom they’d reached, and, by scientifically surveying a representative sample, what percentage of the people they had targeted were taking action as a result of the targeted messaging.

It was revolutionary.

When I learned these things from Alex Tayler, I was dumbfounded but also fascinated. I had had no idea of the reach of data collection in America, and although it made me think back to Edward Snowden’s warnings about mass surveillance, Tayler explained everything to me in such a matter-of-fact way that I saw it as just the “way things were done.”

It was all so no-nonsense; nothing was dark or troubling. This was just how the data economy flowed, I imagined. Soon, I came to understand that I had been naïve to think I could achieve my goals with anything less than a big database. Didn’t I want to be heard? Didn’t I want to be effective? Yes, I did. At the time, I couldn’t think of anything I wanted more.

As successful as this five-step approach had been, I learned in 2015 that it was about to change, when Facebook announced that as of April 30, it would, after so many years of openness, be closing its user data to “third-party app” developers, companies like CA. At that point, according to Dr. Tayler, a critical piece of CA’s data gathering would be jeopardized. No longer could Tayler freely gather data from Facebook through the Friends API.

No longer could he use the Sex Compass or the Musical Walrus.

He had just a short time to grab whatever data he could before that window closed, Dr. Tayler told me.

And CA wasn’t alone. Around the world, everyone else was rushing. Facebook was becoming a walled garden. After April 30, Tayler told me, it would allow data-gathering companies to use the data they had already harvested from it, and to advertise on its platform and use its analytics, but the companies wouldn’t be able to harvest any new data.

Tayler showed me lists of thousands of categories of user data still up for grabs, if not from Facebook itself then from one of its developers. Somehow, other app developers were selling data they had gathered from Facebook, so even if CA couldn’t collect it directly, Tayler could buy it easily from any number of sources. So easily, he said, that I didn’t question it.

And there was so much to choose from. There were groupings of people according to their attitudes about everything from the food brands they preferred to their fashion choices to what they believed or didn’t believe about climate change. All this information was there for the taking. I looked at the list and marked the groups I thought would be most interesting, based on clients I imagined we might have in the future. Tayler gave the same lists to other CA employees and asked them to choose groups, too.

The more the better, he said.

I now know this was against Facebook’s policies, but one of Tayler’s final purchases of Facebook data would occur on May 6, 2015, a whole week after Facebook said this was no longer possible. Strange, I thought. How did we get the data if the API was already closed?

After an extensive time with Dr. Tayler, I sat down and put together my Cambridge Analytica pitch, borrowing from Tayler and Alexander freely, using some of their slides but also adapting them and adding my own so that I would feel more comfortable with the way I personally explained the company to clients.

In the Sweat Box one afternoon, I finally pitched Alexander. When I had finished, he told me I had done a very good job, but that I needed to work on some of the details in order to demonstrate more clarity and more confidence.

“The most important thing is to sell yourself,” he reminded me. The data sell will come naturally once the clients love you, he said, and he sent me out to pitch to every single person in the office. It was in that way that I gained greater knowledge about the company but also got to know my colleagues better.

Krystyna Zawal, a Polish associate project manager new to the company who accepted chocolates as currency, helped me fine-tune the part of my presentation using the case studies that had come from the John Bolton super PAC and the North Carolina midterms.

Bianca Independente, a fun-loving Italian in-house psychologist, helped me understand the larger context of OCEAN modeling, explaining that CA’s expertise in it had come from the nonprofit out of which SCL had grown: the academic research center at Cambridge University called Behavioural Dynamics Institute, or BDI. As Bianca explained, BDI had been affiliated with more than sixty academic institutions, and that’s what had given the SCL Group its academic bona fides. She was working diligently to add to the body of knowledge through experiments.

From Harris McCloud and Sebastian Richards, who were a messaging expert and a creative, respectively, I learned better ways to frame complex technical concepts for laypeople. And Jordan, who worked in research, provided me with visuals that could help me better explain those concepts in a slide show. Kieran literally helped me mock up new slides.

My colleagues provided me with their expertise, which was an embarrassment of riches. They clarified so much for me, and when I approached Alexander again to pitch him in the Sweat Box, I felt ready.

I made sure that I was perfectly dressed, as though for a real client. I wore bright red lipstick. I lowered the lights. Then I began.

“Good afternoon.”

On the wall was the Cambridge Analytica logo, an angular abstract depiction of the human brain and the cerebral cortex, composed not of gray matter but of simple, short mathematical segments printed in white on a crimson background.

“Cambridge Analytica is the newest and most cutting-edge company in the political space in America,” I said. “We specialize in what we call the science of behavioral change communication. What that means is that we’ve”—I pulled up another slide, one showing two equal-size puzzle pieces that fitted together perfectly—“taken behavioral and clinical and experimental psychology and combined that with world-class data analytics.”

I pulled up another slide.

“We have some of the best data scientists and PhDs in this space, working with psychologists to put together data-driven strategies—that means that all your communications strategies are no longer guesswork. All your communications are based on science,” I said.

Next, I discussed how blanket and informational advertising was useless and how the SCL Group had moved on from the old Mad Men