This is the third post in the Attention Economy series. Here are the first and second posts.
In 2006, the Harvard Business Review magazine published an article titled "Evidence-Based Management." It opens like this:
A bold new way of thinking has taken the medical establishment by storm in the past decade: the idea that decisions in medical care should be based on the latest and best knowledge of what actually works. Dr. David Sackett, the individual most associated with evidence-based medicine, defines it as “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients.” Sackett, his colleagues at McMaster University in Ontario, Canada, and the growing number of physicians joining the movement are committed to identifying, disseminating, and, most importantly, applying research that is soundly conducted and clinically relevant.
I don't think this will be a controversial stance among my readers. Science, i.e. the systematic search for knowledge and truth via testable hypotheses and reproducible experimentation, is pretty cool. Doctors should absolutely rely on science when making decisions about healthcare.
In the two paragraphs after this, the authors argue that, like doctors, managers should rely on science and evidence to make their decisions. They discuss various findings by researchers that go against standard practice in both medicine and organizational management, but then they do a very tricky, perhaps accidental sleight of hand: they conflate science done by scientific practitioners with "science" made using data internally within a corporation. It starts with this anecdote:
When it comes to setting the tone for evidence-based management, we have met few chief executives on a par with Kent Thiry, the CEO of DaVita, a $2 billion operator of kidney dialysis centers headquartered in El Segundo, California. Thiry joined DaVita in October 1999, when the company was in default on its bank loans, could barely meet payroll, and was close to bankruptcy. A big part of his turnaround effort has been to educate the many facility administrators, a large proportion of them nurses, in the use of data to guide their decisions.
To ensure that the company has the information necessary to assess its operations, the senior management team and DaVita’s chief technical officer, Harlan Cleaver, have been relentless in building and installing systems that help leaders at all levels understand how well they are doing. One of Thiry’s mottoes is “No brag, just facts.” When he stands up at DaVita Academy, a meeting of about 400 frontline employees from throughout the organization, and states that the company has the best quality of treatment in the industry, that assertion is demonstrated with specific, quantitative comparisons.
A large part of the company’s culture is a commitment to the quality of patient care. To reinforce this value, managers always begin reports and meetings with data on the effectiveness of the dialysis treatments and on patient health and well-being. And each facility administrator gets an eight-page report every month that shows a number of measures of the quality of care, which are summarized in a DaVita Quality Index. This emphasis on evidence also extends to management issues—administrators get information on operations, including treatments per day, teammate (employee) retention, the retention of higher-paying private pay patients, and a number of resource utilization measures such as labor hours per treatment and controllable expenses.
And now I have objections. Science is hard. So hard, in fact, that trained and dedicated scientists struggle with it. This is perhaps best exemplified by the replication crisis, which calls into question the credibility of much of our scientific corpus.
The claim that the company has the "highest quality treatment" is obviously intermingled with the company's profitability. That is bad science. The 400 nurses that Mr. Thiry "educated," had we their side of the story, would probably tell you that spending less time with patients will generally decrease the quality of care while increasing profit.
I am not saying that corporations should avoid using metrics — I don't even think corporations as we know them should exist. Instead, I hope to argue that with this emphasis on being "data-driven" and "scientific," the business community has created a tradition much like those derided in this very HBR article. They mime the aesthetic of science without its substance. Self-declared data-driven businesses are a cargo cult, performing empty scientific rituals in the millenarian belief that their businesses will be transformed into a unicorn. The attention economy exists not necessarily because it is economically rational, but because it satisfies this cultural need.
The Rise of the Engagement Metric
When you set up a website, users' browsers make a request to get the website's data from your server. Servers routinely log these requests to help website owners troubleshoot problems, but these logs also expose how many people come to the site. To a data-driven business, this number is quantitative, concrete, and measurable — it is the platonic ideal of a metric. In a world filled with complex economic situations for the modern data-driven business, such pure metrics are few and far between. Naturally, our ultra-rational, scientific businesses immediately seized upon engagement, and from that day forth set out to do what a business does when it has a metric — they maximized it.
As the web grew, so too did the technologies on the web. Javascript — a programming language that can be executed by the browser — was introduced, and websites became dynamic. Javascript allowed companies to not just render a website, but to write code that modified the website without reloading the page. This code necessarily requires access to what is on the page and how a user is interacting with it. It must know what users are hovering over, clicking, highlighting, typing, etc. Businesses harnessed this new power to log every motion of the mouse, click of a button, or press of a key. Entire javascript libraries were written for the sole purpose of extracting this information.
As companies developed more advanced tracking packages, they started paying other companies to load their tracking packages onto websites they didn't even own, and thus the modern trackers were born. As we covered in part 2 of this series, companies became so addicted to engagement data that they are actively convincing users to move the web from websites to native apps on their phones for the sole purpose of tracking them better.
These trackers follow us around our entire technological life, from Facebook to the smallest independent app (well, except for this site). Googling "how to monetize user engagement" — an uncommon search term in 2004 whose popularity has steadily inceased since — will give you infinite results for online courses, consultants, blogs, and business articles. They will give advice like that in this Forbes article to "[m]onetize audience data—but get consent first."
App companies that have the inside track on data around user activity in and with their apps are sitting on a goldmine. This is because they are the sole owners of valuable first-party data, data that is owned, unique, accurate and—above all—current. Easy to understand why first-party data that is becoming what Maribel Adams, Head of Digital at MediaMax, over in her blog at Street Fight calls “the core ingredient to driving customer acquisition and retention.”
In that search, developers will also find ready-made software packages to simply add to their project from major players like Google that will let them instantly start monetizing their engagement. These ready-made libraries pay the independent developers for both advertising on their app and for harvesting every detail of every minute of its usage.
The attention economy exists because user engagement is the single easiest thing for tech companies to measure. It is not necessarily most profitable for tech companies to focus on spying on its users. One could probably argue that making users feel violated and outraged is bad for business. Perhaps it is. I don't know, and I don't really care. I do argue our obsession with engagement is a product of our cultural values to appear rational and scientific, not a product of rational and scientific decision making. If this is true, then we must ask ourselves: how is tech so profitable?
How Is Tech So Profitable?
It's not. "Successful" tech companies regularly lose billions of dollars. Snap, the creator of Snapchat, is a publicly traded company worth $13.4 billion and has posted a net loss of $360 million in Q3 of 2022. This is typical for them since they've gone public. In 2018, they were worth $21.7 billion, but had lost $1.3 billion that year.
TikTok operates at a loss. YouTube was a money pit for years, finally turning a profit in the late 2010s, only to quickly start missing targets and losing market share to TikTok. Twitter was constantly struggling to stay afloat until it was given new life, when the United States elected a barely literate president for whom the characater limit is a necessary crutch. Now that Musk owns it, it is again hemorrhaging money.
You can even find articles titled "Does It Matter If Tech Companies Are Profitable?" I am not saying that all tech companies aren't profitable, but I am saying that many of them, despite being "worth" tens of billions or more, operate at a loss. Tech companies are valuable because investors like them, and how could they not? Investors, like tech companies, self-identify as being "rational" and "data-driven." It's a perfect match.
Data-Driven Businesses Need Data
In the last 20 years, there have been heaps of articles written on creating data-driven businesses in HBR alone. I purposefully started this post with one from the early days of tech in 2006. Now that we have seen how engagement works, let's look at a much more recent one from 2020 titled "10 Steps to Creating a Data-Driven Culture" to understand how that fits into mainstream business advice.
1. Data-driven culture starts at the (very) top. Companies with strong data-driven cultures tend have [sic] top managers who set an expectation that decisions must be anchored in data — that this is normal, not novel or exceptional. They lead through example. At one retail bank, C-suite leaders together sift through the evidence from controlled market trials to decide on product launches. At a leading tech firm, senior executives spend 30 minutes at the start of meetings reading detailed summaries of proposals and their supporting facts, so that they can take evidence-based actions. These practices propagate downwards, as employees who want to be taken seriously have to communicate with senior leaders on their terms and in their language [emphasis mine]. The example set by a few at the top can catalyze substantial shifts in company-wide norms.
Note the bolded part of that excerpt. HBR says employees should only be taken seriously when they bring data, and that this is a good thing. It is the result of properly creating a data-driven culture. If this is the case, then employees seeking their own career advancement will focus on the measurable. If a project manager works for a company that claims to want to make users's lives better, how do they quantify the success of their projects? We don't have to speculate, because Mark Zuckerberg likes to talk about how Facebook's mission is to "bring people closer together." In a 2018 post on their corporate blog titled "Bringing People Closer Together," the company I will continue to refer to as Facebook writes:
Facebook was built to bring people closer together and build relationships. One of the ways we do this is by connecting people to meaningful posts from their friends and family in News Feed.
Their stated goal is to bring people closer together, but that is hard to measure. How do you quantify how close together people are? I'm sure that neuroscientists and psychologists have spilled much ink determining exactly that, but Facebook is not doing science. They don't actually care about finding the truth. When they need data, they turn to the metric they have: engagement.
With this update, we will also prioritize posts that spark conversations and meaningful interactions between people. To do this, we will predict which posts you might want to interact with your friends about, and show these posts higher in feed. These are posts that inspire back-and-forth discussion in the comments and posts that you might want to share and react to – whether that’s a post from a friend seeking advice, a friend asking for recommendations for a trip, or a news article or video prompting lots of discussion.
They needed a number, so they found a number. Is it a good proxy for their stated mission? No. Do they actually care? Of course not. The cultural need for data is satisfied.
This same process repeated time and time again throughout the entirety of the tech industry is what built the attention economy. Companies and projects might have stated goals, but in a data-driven culture, each employee must justify their job to their boss with a number. Most people are not scientists; they do not have the skills, expertise, or resources to seek the truth in a scientifically rigorous way. Instead, they rely on the numbers available to them, and there is no more readily available number than engagement.
The entire attention economy — the largest spying apparatus ever constructed — doesn't even exist to maximize profits. It's so much more banal. It exists to impress bosses, investors, and the public with how rational and data-driven everyone is in tech. When tech companies have obviously dumb ideas, like Facebook burning billions of dollars on the metaverse, Netflix introducing ads, or every other tech company launching some cryptocurrency shitcoin, it is because they have built their companies around spying on you, they did that because they think that's what their science told them to do, and they have no idea what they're doing.
This post was greatly influenced by Karen Ho's wonderful book "Liquidated: An Ethnography of Wall Street." In it, she argues that Wall Street's obsession with being "smart" and "hardworking" influenced Wall Street culture more than anything else, including economic rationality. Without her, I would never have considered evaluating tech in this kind of cultural framework.