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Effective Editorial Analytics: How Creators can Improve their Content

By NativeAI / October 2, 2018

Most writers, editors, and strategists who work for digital publishers do not suffer from a lack of data: content analytics are seemingly omnipresent, continually providing countless insights on everything from site visits to social shares.

However, while many of these metrics can be informative, when it comes to improving content they usually aren't all that helpful at deriving any actionable steps.

Why? Because all too often creators and publishers look at the wrong things: they focus on the editorial analytics that have traditionally been most accessible rather than on the editorial analytics that are most effective.

What does that mean? What exactly are effective editorial analytics? How can they help improve the quality and reach of content?

Here's what you need to know:

What Are Effective Editorial Analytics?

The core problem with editorial analytics is that publishers often start with the numbers rather than the strategy: they look at data that has always been most accessible — such as visits — rather than examining the data is truly valuable in helping to achieve their goals.

An in-depth study on how publishers utilize metrics conducted by The Reuters Institute found the best-in-class organizations tailor their analytics strategies to optimize for 3 factors:

  • Alignment: Are the metrics aligned with the editorial and organizational priorities of the news organization?
  • Tactical & Strategic Fit: Do they inform both day-to-day decisions and long-term planning
  • Flexibility: Can the metrics capture & be applied to evolve with the rapidly changing media environment

In other words, effective editorial analytics are rooted in not what can be measured but what should be measured.

Another key element is of effective editorial analytics is that they are not only limited to editorial. In today's environment platforms cannot be siloed; to have a far-reaching impact on content, various groups beyond editorial — ad ops, branded content, etc. — must be accessing the same data.

This enables teams to work in tandem to achieve organizational goals. For example, if the audience development team is able to see that target audiences are gravitating towards a certain topic, it can then flag that to the editorial team for more coverage. If that data is not accessible cross-functionally, the optimization is never made.

What Are Some Examples of Goals-Focused Analytics?

All of that sounds nice, of course, but what does it look like in action? What are specific ways to shift focus towards goals-oriented analytics?

Some examples include moving towards measuring:

  • Engagement, not just traffic: Are pieces maintaining the interest of audiences and encouraging further action, not just attracting click-and-exit visits?
  • Real-time performance, not just past performance: How is a new piece performing compared with other pieces and past content? If it is lagging, why?
  • News Topics/Audience Interests, not just individual pieces: Which topics, not just individual stories, are audiences drawn to? Are there clusters of interests and related interests?
  • Engagement Quality by writer/creator: Do the content pieces posted by certain writers, authors or creators underperform or overperform? If so, why?
  • Engagement Quality by News Topic and Audience Interests: Do stories about a certain set of topics (perhaps Donald Trump, Super Bowl etc.) have a higher engagement quality than other topics?
  • Cross-platform behavior: Are audiences engaging differently with content on different platforms? Are visits driven by one source more or less valuable?
  • Competing content: What is the content mix of peer publishers? Which topics and interests are they targeting?
  • Conversions, metered paywall visits, etc.: Are content pieces helping to meet organizational goals such as new subscriptions? Which pieces are and are not? Why?

That's just a sampling of what can be tracked; the specific editorial analytics of most value will vary from organization to organization. What matters more than individual metrics is that the focus moves broadly to insights that can truly help make content better.

How Can Editorial Analytics Help Improve Content?

So how can editorial analytics actually help publishers improve the quality, reach, and effectiveness of their content?

By concentrating on metrics such as those covered above, it's possible to:

  • Understand which pieces drive quality engagement and develop similar content
  • Identify how pieces are performing in real-time and make on-the-fly improvements
  • Discover which topics/interests appeal most to audience and shift coverage
  • See which writers/creators are struggling and alter approaches if needed
  • Determine how audience behaviors vary across channels and adjust accordingly
  • Discern what helps most with subscriptions, etc., and focus on similar pieces

These sorts of insights may sound pie-in-the-sky, but they're not. With tools such as our own NativeAI platform it's possible for writers, editors, and strategists to easily access effective editorial analytics. Ultimately, publishers of all sizes can now quickly see the metrics that truly matter and use these to continuously improve the quality of their content.

Further reading: Editorial Analytics - How you can increase Audience Attention to your Stories

Written by NativeAI / October 2, 2018