This post originally appeared in the January 9, 2020 issue of The Content Technologist with the email subject line "Be more like Inigo Montoya, without the bloodlust."
Update 8/3/22: Light edits made for clarity and style. Cut down the run-on sentences by about half.
My first college lit seminar presented me with Derrida and Gertrude Stein’s “Tender Buttons,” and I muttered angrily: “I’ll never need this pretentious bullshit in real life. Ever.” At the time, the uselessness of poetics and semiotics seemed deliciously indulgent. Which of my business school classmates would ever need this?
I also believed I’d never need geometric proofs or any advanced math in my career, but here I am! Working in the content mines combines post-structuralist semiotics theory and algebra on the daily.
In 2020 western digital culture has clearly established that words do not mean what you think they mean. “Harassment” and “fake” and “misinformation” and seemingly innocuous concepts like “self-care” are lobbed at audiences as if they have any standardized meaning across individuals.
Yes, words mean things, but they are also fluid, shifting from protective devices to weapons before our brains can comprehend what has been said. The speed of digital conversation exacerbates this condition, but it’s by no means new.
Even when we believe information to be true or false, professionalism, operations and basic human psychology propagate narratives of disinformation. As I’m not qualified to explain further, I recommend you read this new paper from Samuel Woolley and Katie Joseff that describes how and why this happens.
The disinformation phenomenon doesn’t just apply to politics. It applies to any digital news, marketing and entertainment content.
UPDATE: In the two and a half years since this essay was originally written, the impact of disinformation has grown, spread in mass and digital media, from outright lies about unproven COVID treatments to questionable social media narratives around celebrity libel trials. It's more important than ever to ensure your audience understands exactly what you mean.
A method for clarifying our slipping signifiers is to define our terms before setting about an explanation.
Both technologists and marketers are poor at clearly defining terms, as transparency can be bad for business. Transparency slows things down and encourages conversation and requires you to be accountable for the words you say.
So, we see breathless descriptions of innovations that are not novel at all but just… standard programs for things that have already been done.
*whispers* you can just do this in excel https://t.co/4JS8LVALQB
— Mike Barthel (@michaelbarthel) January 9, 2020
Mike’s right… that’s not a hard problem to solve! But companies see the signifier “AI” and just throw money in that direction.
Part of the problem is also the Halt and Catch Fire* premise: many companies are working to solve the same problem at the same time, so they invent their own environments and vocabularies, then sell those vocabularies instead of a product or service. Which is how we get to today’s problem of Bounce Rate.
*Knowing the genius of Mike Judge and having seen approximately ten minutes of the first season, I assume that Silicon Valley is also about this problem, although I have not yet watched. Yes I know I should.
UPDATE 8/3/22: I have now seen three seasons of Silicon Valley and this does not seem to be a plotline. It has other merits, like project management frameworks!
What is bounce rate?
You think you know, but you’re going to assuredly and confidently give one of two answers:
- Bounce rate measures how frequently users leave a landing page immediately after visiting from search results or another referral. A low bounce rate is preferable.
- Bounce rate measures how often your emails hit an invalid inbox or email address and cannot be delivered. A low bounce rate is preferable.
The first is a description of human behavior and describes web traffic; the second is a technical error and describes email health. Both are correct descriptions of Bounce Rate. They are also completely different measurements of completely different activities and have entirely different meanings in context.
You’re also still an informed digital marketer if you use one or both of these definitions. Like me, you may not have even realized that these are completely different metrics with the same exact name until you are forced to see them side by side.
Especially in digital analytics, the target continues to move — mostly because if we take the time to focus on the target we may realize that there was never one target in the first place.
Marketers have spent the past fifteen years claiming that one metric is better than another and why we should measure this instead of that and why impressions are bad and why pageviews are bad and dammit which is the single metric that we need to use to make the business decision dammit.
Performance marketers reporting on content or paid media data often don’t tell their clients what metrics they’ve chosen for reporting, usually because most have never been asked to explain what each metric means.
Many don’t understand that the standards for “good” change more frequently than they did in the past. Adequate space and work time needs to be given for ongoing education on shifting standards. Terms that were once explained need to be reexplained and clarified. The standards will not stop shifting. Change is inherent to our cyborg condition.**
Please remember: No one’s ignorant for using Bounce Rate or any other single metric the wrong way. Both tech and marketing make money from being opaque, all the time.
Often the changes to standards of "good" aren’t nearly as big as you expect. So please, whether you’re junior or senior in your organization, take the time to ask and answer questions about what you’re looking at and why. Ensure that the narrative you’re telling about your content actually fits the problem you are trying to solve. You’ll quickly understand whether a lack of transparency indicates a lack of time to explain a solution adequately, or a general lack of expertise.
**EDIT 8/3/22: As we all learned in the past two weeks when mass media reported on platform interface changes because of one celebrity's complaints, iterative tech business models designed for growth don't always match audience preference. In content and entertainment, people like what is comfortable.
If you’re making business decisions based on a report, you need to know whether you’re measuring a Shiba Inu or a bison. What we describe as valuable shifts significantly from context to context, across audiences and interactions. Digital marketing and digital culture is in no way singular! Language works differently across the internet, different company cultures, different technologies!
The best thing you can do for your content business is to understand the conversations you’re having about your content business. Understand that “more/less” and “lower/higher” are not actually stories without a comparison and certainly do not indicate short-term or long-term impact. Data stories require close reading, the same way that Gertrude Stein does. It takes years of understanding to glance at a report and see a story.
The Bounce Rate problem is endemic in content measurement and across marketing. In your content analytics, start by defining your terms. Seriously: make a glossary and write it at your own company. Then, you have content ready to explain your terms the moment they are presented. Don’t just trust your vendor’s storytelling. (But trust your vetted vendor to do good work on your behalf.)
Fellow content miners, it’s on you to make sure the words mean what you think they mean. Define your terms.
Hand-picked related content
Deborah Carver
Deborah Carver
Deborah Carver