NEW YORK, United States — At major publishers of fashion and lifestyle magazines, data has become more important than the content itself and the talent who produce it. We are addicted to data and this addiction is dangerous.

The problem stems from the underlying platform shift from print to digital, and a digital advertising-driven business model that publishers can’t seem to escape: advertisers demand scale, but attracting a large enough audience is exceedingly difficult when you competition is Facebook and Google.

These two giants, as well as digital influencers, continue to siphon advertising spend away from publishers. And while media companies have found new revenue streams — such as earning a small slice of e-commerce sales triggered via their sites — nothing has been profitable enough to right the industry, and even their newfound advances are themselves under attack from developments like Instagram’s new shopping functionality.

Digital subscription revenue has worked well for publications like The New York Times and The New Yorker, but it's unclear if it will perform for lifestyle publications, although this hasn't stopped Condé Nast from saying it will put all its titles behind paywalls by the end of the year.

In the meantime, many fashion and lifestyle publishers are anxious for a quick fix. If they can use data to grow their audiences and net more advertising, next quarter could be better, so the logic goes. But the plan can come with collateral damage: The instincts and talents of editors have been washed away by a flood of data in a desperate scramble for more clicks.

We are addicted to data and this addiction is dangerous.

At many publishers, story ideas are now rooted in traffic data. If a publisher is having luck with Google traffic, for instance, they will probably want to find more search terms about which to create posts. Then, someone on the editorial team has to spend time creating a post about something unremarkable like “When Is Meghan Markle Giving Birth?” even though there’s nothing particularly interesting or differentiating about this sort of article.

It doesn’t stop with traffic data. Publishers can also see how much they’re earning in affiliate shopping revenue and try to create similar posts based on what’s working. The same is true of their Facebook videos, their Instagram posts, et cetera.

The danger with running a publication that reacts too tightly to data is that you stop employing instinct, skill and taste, making it less likely that you are telling interesting and differentiating stories. The British royal baby’s birth is a great recent example. Few outlets found angles around the story other than “this happened, here are photos, HEARTMELT.”

My social media feeds were flooded with commodity photos and information, even though there was a lot to say about this royal birth, which was unlike any other owing not only to Meghan Markle’s background but also the manner in which her son with Prince Harry was revealed to the press. To be sure, the issue is rooted in a wider problem with the social media-powered, knee-jerk enthusiasm of celebrity culture, but blindly following data plays a key role.

When editors are pressured to grow traffic and given the "tools" with which to do so, they start to constantly chase spikes. "X story did really well, so let's do X story 50 more times," the thinking goes. The problem with this strategy is that it rarely works. Many of the biggest traffic spikes are driven by unique stories that do well precisely because they are unique. For instance, Vanity Fair's famous 2015 cover story on Caitlyn Jenner, which drove 6 million unique visitors in hours, was an extremely rare news event that's impossible to replicate.

A click on a royal baby photo does not create a customer.

Constantly trying to repeat that which did well easily dissuades editors from pursuing or dreaming up new, creative ways to engage their audiences. And if editors and their writers aren’t doing that, it won’t be long before they’re replaced by machines.

Disturbingly, in today’s data-obsessed times, potential editorial talent is increasingly evaluated less on actual talent, but rather on their social media numbers. Many people I know who work as editors have told me this only complicates the hiring process because often the person who has a great Instagram feed isn’t also a great blogger. If you’re running a media brand rooted in looking at a chart to figure out what post to create next, it might not matter if your employees have little more to offer than a follower count. But if media brands are going to survive, their content has to do more than regularly float to the top of Google News.

“With all the trendiness around data and analysing audience behaviour, a lot of times people do get lost,” said John Chan, co-founder of data analytics and content amplification company Native Lift. Chan suggests analysing data to try and prove or disprove a hypothesis, rather than using data as a starting point and “trying to make sense of a mess.”

Data can also be highly useful in informing the distribution of content, especially when it’s closely meshed with publishing technology. A smart content management system, armed with the right data, should be able to tell what kinds of articles people will be most likely to read next. For instance, if you know that people who like reading about the royal baby also like travel content, you can promote travel articles when they’re on a royal baby piece.

Publishers need smarter data science teams to help editors test new ideas. They also need more sophisticated and responsive CMS technology. And perhaps more crucially, advertisers need to rethink the way they use data. They have to find a better way to evaluate the strength of a media brand’s connection to its audience than simply looking at clicks. A click on a royal baby photo does not create a customer.

Related Articles:

[ Magazines Are Dead. But Media Isn’t.Opens in new window ]

[ Instagram Killed the Fashion Magazine. What Happens Now?Opens in new window ]

[ Redesigns Won’t Save MagazinesOpens in new window ]