Content Insights’ Dejan Nikolic on editorial analytics, actionable insights and what’s next at CI HQ

Right then, Dejan. How’s things? When we spoke before [back in the winter 2015/6], the concern in the industry was around the obsession with clicks and likes. Where are we with that now?

What we see happening is that newsrooms with the resources to plan long-term see that they need to go beyond clicks and likes when evaluating content performance in order to be able to find new business models and stop being completely dependent on the broken display advertising model. Unfortunately, most of the industry can only afford to look at things in short-term, still pressed by the business model that is dragging them down. They also know that it’s broken and that page-views as currency is at the root of the problem, but they just can’t afford to look beyond it, fighting for survival literally quarter by quarter.

So, it’s not about the problem anymore, I think most of the industry is very well aware of that. It’s about the solution. Those who can look beyond short-term survival tactics are the ones who will survive.

How’s Content Insights evolved and evolving?

It’s in Content Insights’ nature to evolve constantly. Its engine, the Content Performance Indicator, is an algorithm that constantly learns how content consumption and ultimately content performance drives and influences audience behavior. Knowing that ‘WHY’ allows our customers to manage success and repeat what works and avoid what doesn’t. Since it’s not a prescriptive algorithm – meaning it doesn’t tell editors and journalists what to do but rather informs them of what works and why – it is uniquely positioned to help create data-informed newsrooms which are not obsessed by numbers and metrics like page-views, visits and shares. It’s a thin edge to walk on, and learning from massive amounts of data about how content performs over time and how to interpret that will be a constant at Content Insights.

So that brings us on nicely to actionable insights and the idea of content intelligence. What’s the deal there?

The deal is that finally we have a set of metrics and information that works directly for editors and journalists in making their jobs easier in today’s overworked and understaffed newsrooms. You just can’t build proper editorial intelligence, meaning knowing what and why influences your audience to behave the way it behaves while consuming the content your produced, on 20 year old metrics and systems originally built for completely different purpose. No matter how you package it – whether you observe it in real-time or historically, whether you put it on layers – it can’t help editorial in its primary job, creating content that works.

When you know why, not only what happened, then you have intelligence upon which you can make decisions in editorial context.

How’s it meeting the pivot to paid trend?

From the very beginning our position was that audience should be media’s customers not its inventory, and Content Insights was built and continues to evolve around that idea. Our greatest contribution to newsrooms and content creators aiming to switch to a subscription model is showing what actually creates loyalty in readers, which is the main behavior you want when building this model. Most of the subscribers to any kind of content come from the pool of loyal content consumers.

That said, the issue in the industry is finding a cohesive definition of what loyalty is. In e-commerce or content marketing with funneled customer journeys and CTAs it’s defined by RFM (recency, frequency, monetization) and similar models. But, when we are talking about content consumption, it gets vague. This is where CPI for loyalty comes in and the definition for loyalty that it finally brings to industry. A loyal reader is the one that is fully engaged with content and returns to the brand to consume it with certain recency and frequency (learned by our CPI for every domain we track). Some other parameters influence this definition as well but this is the simplest definition possible.

By having this definition in place, our labs were able to learn how to calculate what sort of content, which writers or topics, contribute to the creation of loyal readers and how much. To me, this is the most exciting breakthrough in editorial analytics so far and it allows us to go further with empowering newsrooms to build subscription models based on content performance and not on guesswork or expensive trial and error.

We’ve always been at the forefront of movement championing data-informed journalism, rather than data-driven. Why’s that so important?

The difference between being data-driven and data-informed is that rather than allowing real time traffic data forcing your hand in what to write about, you actually learn about how your content performance is influencing your audience behavior and what kind of content actually works or doesn’t work in that context. Being data-driven is of utmost importance if your newsroom’s goals are represented in page-views or ad-clicks. But let’s stop calling that journalism or audience centric. Stopping being data-driven is the prerequisite to stop treating your readers as inventory and start treating them as your audience, your customers. Being data-informed, knowing what and why is something happening gives you the platform to build old-school journalism, if you like, but one that utilizes knowledge of content performance and audience behavior to create better and more engaging stories.

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