It's officially the busiest month I've ever had since going freelance five years ago, and I am annoyingly joyful about it. I'm deep into audience research, user interviews, audits, reports, competitive analysis, all the activities, and it feels glorious.
A full slate of client work means that original content will likely be limited for the remainder of the year, but I'm aiming to update the newsletter all-timers, collect past content in guides like the one below, and add one new review per month. I'm also allowing myself unplanned off weeks, so if you're wondering where the newsletter is, I'm resting, dammit.
I doubt you've been paying close attention to The Content Technologist's editorial calendar, but I share where I'm at, like a girl apologizing to her diary, on the off chance a reader is following along like a disgruntled Yelper.
If you want an update: before the end of the year, I aim to finish what I've started as far as video courses, which includes returning to a reconfigured Let's Build a Website and revising Understanding GA4. More than (key)Words and Your Content Is Your Marketing are now available, but the measurement and content strategy chapters are no longer in the production schedule. As I noodle on meaty client challenges, I don't have the brain space to cook up much of anything new. Also, I admit that I went a little overboard in my embrace of video. (One reason I am joyful: I'm not actively in sales or promotional mode, and neither am I constantly editing videos of myself. I can just kinda be.)
So! Let's start the show: A guide to everything The Content Technologist has ever written about content analytics, a subject near and dear to my heart that I encourage you to explore, even if you think you hate "metrics."
A guide to content analytics: Measurement for people who deliberately chose careers that weren't supposed to involve math
One of the core tenets of this newsletter is that nothing about internet content is particularly sophisticated, and even the most advanced researchers and practitioners are still galaxy-braining what it all means. This newness goes triple for data science, a comparatively recently invented discipline rooted in monolithic 20th century assumptions of behavior across cultures.
I advocate for content professionals to understand and engage with measurement because most of the statistical models and measurement methods out there are either extremely basic or highly over-engineered, with not much middle ground. The tech industry continually undervalues practices related to "meaning" and "words" and overvalues advertising and market manipulation as a means to business growth. Like it or not, it's up to us, the people who enjoy making things, to advocate for what we value and to value what we create. We must, on a base level, understand our numbers. Otherwise, an adversarial "they" will short us because "they" can.
Senior editors in legacy publishing are responsible for understanding their performance analytics. Digital content strategy is an editorial discipline that distributes the same types of information on giant calculators. Therefore, it's only natural that we content people should have the same ownership of our inputs. Understanding analytics gives content professionals a seat at their table, a way to converse with the math machines, and argument fodder to make better choices about what works and why.
The content analytics I advocate don't require a higher level than eighth grade math skills. The real key is knowing where the numbers come from and knowing how to read a basic graph. (Frankly, people in all information production professions need to be better at reading and producing graphs.)
As a rule of thumb for all digital measurement: if you can't correlate the metric with a human action, it's probably not worth your time to track.
Without further ado, here are far too many newsletters about content metrics and where they come from:
The basics: Learning to measure like a professional
Google Analytics (GA/GA4) and Google Search Console (GSC) are essential tools for all digital publishers and will be the standards for the forseeable future. Yes, there are other analytics tools, but GA is by far the most accurate in zooming in on actual human behavior.
Intermediate content analysis: When you're ready to notch up that performance
Got a handle on your basic analytics? Awesome. Stretch your muscles with these exercises.
Traffic sucks: If you measure pageviews you're doing it wrong
I've written at length at why I hate pageviews and the entire idea of measuring traffic without context. Pageviews do not indicate anything meaningful about audience behavior and encourage lowest common denominator pandering. Meaningless traffic also has nearly zero correlation to successful business performance—quality lead gen, subscriber acquisition, etc.—no matter how many marketing bros tell you otherwise.
If you don't believe me or the below posts, read Traffic by Ben Smith, which I believe is about how traffic is a bad way to measure media performance (I think... I couldn't get through more than the intro because Dottie, I lived it).
The theory behind the analytics practice: Deep thoughts on measurement and analysis
I enjoy waxing poetic about the origins of measurement models and whether they relate to actual human behavior. The essays below are this newsletter's most popular traffic-wise (it does very little for business performance!), followed by two of my all-time favorites.
Content measurement remains an incredibly necessary but widely misunderstood discipline, and I've clearly no shortage of commentary on the practice. There will be more! But in the meantime, if you're looking to gnaw on the numbers, the above should suffice.
Content tech links of the week
- Academic researchers found that one of the best ways to combat misinformation is to educate people about how algorithms work, in NiemanLab. Yes! Media literacy! Combat folklore!
- Speaking of folklore, intelligence researchers are advising tech marketers and the industry in general to critically examine metaphors used to describe AI, per Tech Policy Press. Loud applause from my corner, especially around what the article names as the Productivity Myth.
- Content Technologist alum Sam Thielman reminisces about the millennial glory that is/was Homestar Runner, over on the very cool Flaming Hydra.
- "Journalist is bewildered and cynically enthralled at a tech conference" is a trope that gets old fast, especially if you've attended more than a few tech conferences or watched Silicon Valley, but Laura Dattner navigates the convention hilariously for n+1 at what sounds like a batshit AI tradeshow featuring an abundance of specious behavioral science and applied statistics.
- Zuck had all the advantages of going to Harvard but never took a class from Homi Bhabha, and as a result his company failed to meet basic thresholds of cultural understanding required to "connect the world." Of course it has consequences for the product and the people who study the product. Here's a study from the Center for Democracy & Technology (via Everything in Moderation) on Colonialism in Content Moderation Research.
- "Choosing a brand to buy is a deeply social process, regardless of whether you are buying sauces or SAASes" is the most delightful B2B sentence I've read all week, in this excellent explanation of why brand stories matter, in Attention Matters, a newsletter from the fine folks at Storythings.
The Content Technologist is a newsletter and consultancy based in Minneapolis, working with clients and collaborators around the world. The entire newsletter is written and edited by Deborah Carver, independent content strategy consultant, speaker, and educator.
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Cultural recommendations / personal social: Spotify | Instagram | Letterboxd | PI.FYI
Did you read? is the assorted content at the very bottom of the email. Cultural recommendations, off-kilter thoughts, and quotes from foundational works of media theory we first read in college—all fair game for this section.
Threads is extremely strange and confusing, but internet journalism legend Katie Notopolous has been trolling the algorithms to brilliant effect.