Newsroom Analytics And Content Intelligence

    Fact checked by Vahe Arabian
    Vahe Arabian

    Founder and Editor in Chief of State of Digital Publishing. My vision is to provide digital publishing and media professionals a platform to collaborate and promote their efforts, my passion is to uncover talent and… Read more

    Edited by Vahe Arabian
    Vahe Arabian

    Founder and Editor in Chief of State of Digital Publishing. My vision is to provide digital publishing and media professionals a platform to collaborate and promote their efforts, my passion is to uncover talent and…Read more

    analytics business chart

    Em Kuntze is Content Insights’ Content & Community Manager. You can find her on Twitter @emkuntze where she’s found usually thinking about the future of...Read more

    Today’s topic is the evolution of newsroom analytics. Now, it’s not that any of the following is particularly complicated, but since these days our attention is being pulled in multiple directions, let’s start with an analogy.

    Think about your car.
    Odds are that you bought it, and that you didn’t build it (but if you did, kudos).

    Here’s the thing: not being able to tell the difference between a camshaft and a carburetor doesn’t preclude you from being able to drive it. In fact, when we talk about driving and the driving experience, the language used is completely different to those discussing the finer points of automotive engineering. Very few of us focus on much beyond the user experience.

    That industry understood long ago that the products must ultimately serve their user, not their developers.

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    What’s this got to do with newsrooms? Well, everything, actually.

    The pivot to user experience

    “There’s a wave of data coming from customers and the social media. And as the internet of things rolls out, there will be even more information on customers. Businesses are scrambling to figure out how they can extract value from that information.”

    That’s Richard Gordon, an analyst from Gartner, talking. What’s he’s describing is the shift to something the business fraternity like to call ‘business intelligence’.

    Simply put, while analytics is a tech-aided process in which software retrieves data, business intelligence is a process which goes a step further by interpreting and presenting that data in a digestible form before it reaches the intended recipient.

    The reason why this is valuable? Well, unless you’ve asked precisely the right questions of that data, it doesn’t matter how good the numbers look or how attractive the interface is. Without relevant interpretation, it’s still not going to be a great use to you and your business even if you understand the data.

    Business intelligence is the driver experience of the business world. It’s developed for the user and the end use. Since it’s been embraced by the business world, it’s been a bit of a game changer.

    Too much data, not enough insight

    So, on to newsrooms and no doubt you’ll see where we’re going with this.

    Analytics are, of course, commonplace in newsrooms. Analytics packages abound. We’re all likely aware of the problems and limitations of single metrics in the industry; and though the cult of the page view appears to be diminishing a little, it’s still a predominant force because quite simply it’s a convenient and apparently universal measure of ‘success’ – whatever that means.

    The problem with universal, ‘simple’ solutions to complex problems, is that they are unlikely to be able to deal with the kind of complexity that each individual scenario requires. Sure, it would be lovely to have to deal with a binary measure of failure or success, but in the real world, there are just too many variables, too many nuances for this to be convenient to anyone. Save the people trying to market those ‘solutions’!

    Analytics are undoubtedly better now on the UX front than they have been, but all the prettification in the world can’t change the fact that if all you’re doing is presenting raw data, you’re going to be no closer to what it actually means without some serious background in data analysis. And, although there are undoubtedly exceptions to this rule, most editors have neither this kind of skill set nor this kind of training – and certainly not the kind of time that’s required to do this properly.

    When we hear about the resistance to ‘data culture’ in journalism, it’s hard not to empathize.

    While the data – the raw material – is, of course, essential, it’s the context and insight that data reveals which’s absolutely key. The value comes from aligning data and information with a frame of reference and thus presenting it. It doesn’t ask editors to understand every detailed nuance and nor should it. Better to put editors’ and journalists’ skills where they’re most valuable, which surely makes better business sense.

    The missing link

     We’ve witnessed an evolution of analytics. From having almost no information about genuine patterns of consumption from our readers, we now have potentially more data than we know what to do with – and, more often than not, we don’t know what to do with it.

    The problem has been that because there’s historically been no way to effectively incorporate and instill data culture into the newsroom workflow, there has been no opportunity for editors and journalists to shape its evolution. It has been left to those outside the editorial world – namely advertisers – to develop an analytics tool, but because that tool was designed to enhance advertising efficiency, it does nothing to aid editorial and journalistic practice.

    Content Insights’ VP for Latin America, John Reichertz, has said: “the best way to get this data culture flowing through our newsrooms is to get everybody involved” and he’s right: improving access can and does have a transformative effect on the newsroom. If journalists understand the effectiveness of their own stories within the context of their own departments and specific audiences, they’re more likely to produce more efficient content. Similarly, with access to nuanced information, editors are increasingly able to make smart choices about where to place which resources.

    That’s not to say that editors can – or should – be expected to become data experts. At Sueddeutsche Zeitung, Audience Editor Christopher Pramstaller made this clarification:

    “We don’t want to cause data pollution: we think it’s more important to get the right information to the right people at the right moment.”

    They’ve searched for the correct balance between data insights and the editorial and journalistic workflow to find a balance that works for their staff, the organization, and its goals. For them, it meant dispensing with real-time analytics and working closely with the news teams to relay important data insights. These reports might assist with under-performing articles, highlight formulas for success, or minor changes that could be made to elevate articles’ visibility.

    Editorial Intelligence

     Ultimately, we need to get to the point where analytics aren’t just a by-word for data presented in graphs and charts. They should do more than that because – now – they can. They must provide insight, context, and meaning, and do so in line with the needs of not only each news organization but each journalist in each department of that news organization.

    Part of this is finding a solution that works for you, but much of it starts with questions:

    • What do the impressive figures circulated actually mean? When a report talks of a million page impressions, how is this calculated? If you’re relying on key metrics, find out how those calculations are performed.

    Here’s why it’s important.

    Take the page view. It’s a browser event. This has nothing much to do with retail therapy – though it can be just as fleeting. Page views occur whenever the code on a page is loaded, so even if it loads in the background it counts. Yes, even if it’s triggered by a bot it counts. Even if it’s just for mere seconds. At Content Insights, for example, we have something we call an article read. It sounds the same, but it isn’t.

    One article read = someone opened a page, spent at least 10 seconds on it, the page was in focus and there was an actual person behind the screen.

    So the same article, viewed with these two measures, is likely to yield very different indications of success. The first will return higher numbers, massage the ego nicely, and look more impressive. The second might look much more modest, but is infinitely more useful and actionable. Knowing the difference in the way these things are calculated can make a massive difference.

    • Think in terms of ratios, not single numbers – blended metrics are most insightful and because they’ve been processed for you, they provide an at-a-glance insight into how your content is performing.

    Above all, analytics have reached the point now where they have the capability to inform quickly, succinctly, and on-point. If you’re wading through pages of data, you need to stop. Answers from data are only as good as the questions asked, and if you don’t know what or how to ask them, well, then you’re going to be spending a lot of extra time getting lost in a sea of numbers.

    • How’s your business structured and what information do you need to move forward? The best approach is one that enhances workflows, not disrupts them.

    If you’re switching to subscriptions, you’re likely to need different insights to publications with a strong ad foundation. Using the same measurements of success is ludicrous and frankly unnecessary in today’s marketplace of niche solutions.

    • Do you use an analytics package? Talk to the people behind the screen.

    Tech companies are iterative. Feedback and suggestions help improve services and scope, so it’s advantageous for you and the company to communicate. It’s in working with newsrooms and agencies that we’ve been able to bring out new versions and tools and we wouldn’t have been able to do that without those conversations. Feedback can inspire innovation.

    The next stage in the evolution of editorial analytics

    When you’re working with an analytics approach that’s designed with the specific needs, abilities, and requirement of the newsroom in mind, the reports you generate can only sharpen editorial instinct, they can’t undermine it. It’s about integrating useful insights into the day to day workflows of the newsroom so that these kind of analytics are as user-friendly and commonplace as opening an email or uploading an article. What this looks like in practice will vary from newsroom to newsroom. You may still have a dedicated analytics department whose responsibility is to alert sections to the success or problems of certain pieces. You might be a much smaller team, where the responsibility for this kind of monitoring falls to editors and sections. There is no one right combination. The only thing that’s right is to find an approach that enables you to move forward into a data-informed mindset, where decisions are guided by data, not driven by them.

    We call it Content Intelligence and we think it’s the paradigm shift the industry needs.



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