Media

RoboEditor: The Strange New Editorial Machine You Need to Become

For years now, we’ve been hearing a lot about how editors at young media companies are using predictive algorithms and analytics tools to figure out what audiences want. This past month, we’ve been hearing about the inverse: Tech giants like LinkedIn and Apple hiring human editors to supplement their content algorithms. As Amanda Walgrove wrote here on The Content Strategist, in the debate over whether humans or algorithms are more effective for content creation and curation, the answer is both. Cue the Turtles:

Working on that piece with Amanda got me thinking about what all this means for those of us who work in editorial, either at media companies or newfangled brand publishers. To me, there’s one obvious takeaway. We need to become a very specific creature: half editor, half data scientist—someone whose data analysis skills fuse seamlessly with editorial judgment. Like RoboCop, but with more creativity and less killing.

It isn’t just a matter of being analytically literate. Understanding your dashboard in Google Analytics or Chartbeat or (shameless plug) Contently is a solid start, but you still need to reach a more sophisticated level. You need to get into the habit of digging deep into key audience metrics—like how and where things are being shared, the average finish percentage of each piece, email rate conversion, etc.—and then immediately brainstorming ideas for how to adjust your approach. RoboEditors not only identify specific characteristics of successful stories that can be duplicated, but also know what caused a piece to underperform. Was there a sudden drop-off because of a lede? Is the copy bloated with semi-interesting and tangential details you probably should have cut while editing? Is this writer just boring as hell?

Also, being that hybrid editor means recognizing when the data is hinting at the wrong conclusions so you know when to let your editorial instincts take over. Those who are constantly and creatively iterating need to make sure “doubling down on what works” doesn’t mean “just doing the exact same thing over and over.”

This stuff is fairly obvious. I’m far from the first person to suggest that editors train themselves to think like this, and there is an army of these young editors being incubated at fast-growing digital media companies like Vox, BuzzFeed, Mashable, and Vice, which have combined to add more than 5,000 new editorial jobs over the past several years. These editors are able to access much-hyped technologies like BuzzFeed’s Pound and Mashable’s Velocity, and they’re ingrained in a culture where human brains and algorithms work in concert to win the web.

It’s impossible to ignore this trend any longer. If you work in editorial and data isn’t a big part of your life, I would join the chorus and politely suggest you get your shit together. Yes, someone with superb editorial instincts is still incredibly valuable. No algorithm will ever beat a great writer, editor, and thinker. But the smart use of data will make that great editor even better, and not leave him playing at a disadvantage against lesser talents.

Plus, it’s really not that hard to step up your approach. Tools like Google Analytics, Chartbeat, BuzzSumo, and (again, shameless plug) Contently Analytics are pretty easy to understand if you put in enough effort. (Trust me, I last took a math class when I was in high school and I’ve been able to figure it out!) And once you have a grasp of those platforms, it’s simply a matter of putting aside a little time each week to brainstorm story ideas and strategies while digging into your data. Like any mental skill, it requires practice, and, eventually, it starts to become second nature.

I’m on that journey myself, and the biggest help has been my weekly one-on-one meetings with Contently’s data scientist, Alex Combs. He’s guiding me in the right direction as I try to figure out what makes our readers return and what unseen factors lead to reader loyalty and social sharing. He’s showing me insights that can help our publication grow. And he’s teaching me to think about data in a way that will make me a smarter editor-in-chief. I’m still a work in progress, but it seems to be working: Our traffic on The Content Strategist has more than doubled this year.

This is particularly important for those entering the new frontier of brand publishing who haven’t had experience working in data-smart media companies, or freelancers who write from home, shielded from the metrics that matter. Gain access to these tools, teach yourself to use them, and seek help when you need it—either from within your company, or from an external content strategist who knows how to think the right way.

Remember, you’re the future of the content: part editor, part data scientist. And if you embrace the role of RoboEditor, you’ll succeed.

Image by Inozemtsev Konstantin
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