On using analytics to iterate on editorial decisions | Ritvvij Parrikh Humane ClubMade with Humane Club

On using analytics to iterate on editorial decisions

Published Jul 16, 2021
Updated Dec 27, 2022

Previously when discussing editorial decisions and analytics, we studied how BBC uses user needs to refine its product. In this post, we will delve into leading and lagging data points and why it is important to document leading data points.

Back when I was running Pykih, we did a data analytics project for a large FMCG company. In that project, I learned the concept of leading and lagging parameters. 

My client, the national sales head, explained, “Higher oil prices eventually result in food inflation. To put it simply, if your goal is to lose weight, you shouldn’t obsess on weight because weight is a lagging parameter, i.e., an outcome of things you do. Instead, what you should measure and track are calories in and calories out. Then, you can control and work on these leading parameters.”

Problem with editorial analytics, today

In most editorial analytics, we focus on lagging parameters like page views, users, and average time on page. Once we have these numbers, we correlate these lagging parameters with other dimensions we capture, e.g., sectionsbyline, topics.

While this gives us macro trends like “articles on coronavirus are doing well”, it does not give granular insights back to the editorial team. After all, an article is a collection of editorial decisions (the leading parameters) by the editor, reporter, desk, social media desk, and SEO. Which of those decisions resulted in this article doing well. That isn’t known.

Interestingly, The Times (UK) runs/ran a content audit project to help their editorial team transition to leading parameters. First, they broke down the entire editorial process from idea definition to editorial goals and format options into 16 dimensions. Then, correlating these 16 dimensions against audience metrics, they identified specific actions that editorial teams could take to serve particular audiences better.

Below are some other leading data points from the editorial workflow that could help:

Brand-fit: Why the editorial team thinks that the audience expects our publication to cover this?

  • What is the topic and sub-topic of the story? Does the audience expect our publication to cover this topic? If yes, in what depth or breadth?
  • What is the user need that our publication serves? For example, no one expects to be entertained by academic papers.

Editorial decisions on why to cover the story

If we publish the story, will the audience find it relevant and click on it?

  • Why will the audience care about this story? Does it have a direct utility in their life? Is it a topic of civic importance? Can the data communicated within it give professionals a competitive information advantage over their peers?
  • Why will the audience care about this story now? Is it because it is newsy? Is it overlapping with an event or anniversary?
  • When the story will be published, how exclusive is it? There could be no coverage of this story in mainstream media. Alternatively, it can be an emerging story, or it already has widespread comprehensive coverage.
  • Why this byline: Beyond the byline, why should the audience get this story from them? Are they an expert on this topic? Or do they have mass appeal?
  • Where does the story occur? Are we covering that geography because our audience is from that geography? Or because our audience cares about that geography?

Editorial decisions on how to produce the story

How should the story be produced such that they find it most engaging?

  • How should this story be written? Should it be an opinion, explainer, analysis, curation, profile, data investigation, or reportage?
  • How should this story be produced, i.e., digital format? For example, articles, podcasts, videos, listicles, and photo stories.
  • How many micro-elements (photos, audio, quotes, data points, charts, or videos) did you add to this story?
  • Can we bring in more dimensions or intersections to the story? For example, if it is a policy story, then can we cover profiles of everyday people?
  • How was the story marketed? In what channels?

Was consideration put in for long-term impact?

  • What was the cost of producing the story?
  • Is this story a perishable commodity? What is the shelf-life of this story? How often will this story need to be updated for it to be evergreen?
  • What is the potential of this story? Is it a one-off story, or can it scale into a series? Can we do a follow-up on the topic later on?

These decisions when augmented with lagging parameters like page views, users, and average time on page can give insights into how to iterate on editorial decisions.