Before we get into how you can use Editorial Analytics to improve audience engagement, let's address the elephant in the room - data and analytics are essential cogs of business today; the key is to collect and process data responsibly and with appropriate consent.
A step back: Internet, the Attention Economy & Data Privacy
The internet has irrevocably changed how media works - social media and the handiness of smartphones, the band of product managers have created the 'attention economy' where the content is free, we the users are the product and our attention is the price we pay to consume the content.
Data is the key to unlocking and holding Audience Attention - but for social media platforms, the data was too opaque in collection, personally identifiable and therefore 'creepy' to many users. Following the widespread attention on Facebook around Cambridge Analytica and the public revelation of the extent of information that platforms such as Facebook and Google have on users, these platforms are now building features that allow users a measure of control over their data.
Google has built features in its latest Android P OS that show you how addicted you are to each app on your phone and to your phone itself. It even has a Wind Down mode that lets you go to sleep uninterrupted by those tempting notifications. Facebook is pondering launching a paid subscription where it does not collect our data, and also a delete user data option.
What is Editorial Analytics?
Editorial Analytics is the practice of content editors using data about their audience, competitors and the performance of their previously published content to help make decisions in the editorial process.
So, Editorial Analytics acts as an objective resource that the editorial team can use to prioritize their editorial calendar and achieve their goals.
An editor at a newspaper or digital news publication uses a wide variety of inputs to evaluate the stories that should be worked on and the scheduling of publication of these stories. The inputs usually include the importance of the topic of the story itself, relevance of the story to the major audience segments of the publication as well as list of authors / resources available who can work on it and of course editorial intuition gained from experience.
Here's how Audience Interests can be obtained on NativeAI Editorial Analytics:
How is Editorial Analytics different from generic web analytics?
Editorial analytics is tailored to the needs of an editorial team - in data velocity, in types of metrics delivered, in dimensions that you can pivot the data by and with a view to simplifying reporting. Generic analytics tools such as Google Analytics are not built for newsrooms and do not always feature real-time data reporting, or pivots based on authors.
Since news is an industry where time-to-publish as well as accuracy are highly critical, every editorial analytics platform is custom-built for agility. Besides the immediacy of the data, editorial analytics also simplify the ease with which an editorial team can react to the insight by tapping on past behavior. Delivering actionable insights, at the soonest possible moment is the calling card of an editorial analytics tool.
For example, if a particular story that was published and is currently trending is experiencing low engagement quality, an editorial analytics tool would highlight that on the real-time report and any additional information that can help improve the story as more information becomes available.
In fact, this segment of analytics tools is not new and has seen tremendous growth over the last 2-3 years. In a 2016 article on Nieman Labs, noted journalist and researcher Federica Cherubini identifies 3 key differences between generic and the tailored approach to analytics - customized for editorial priorities, short & long-term strategic use of data, and evolution with the changes in the media industry.
How can Editorial Analytics help gain & retain Audience Attention?
Although it is primarily built with the editorial team in mind, the features of most purpose built editorial analytics platforms such as NativeAI can be used to great effect by Audience Development, Marketing as well as Ad-operations teams. Listed below are some applications of Editorial Analytics for each of these teams.
Audience Development & Marketing:
An Audience development team's primary role is to identify new channels to bring in more readers and also nurture current readers to engage better with content and convert (whether the business objective is advertisements, newsletter list additions or paid subscriptions).
Any new content distribution campaign that Audience Development runs is a definite item that Editorial Analytics can track. That data, when merged with audience interests, engagement quality and affinities of specific segments or traffic sources to convert better enables AudDevs to visualize performance and draw accurate insights to fine tune their campaigns.
The same goes for Marketers who love the real-time dashboard view that lets them view their campaign's impact as it unfolds and allows them to react and steer it towards their goals.
Sales & Ad-ops:
If digital advertising is the primary revenue source for your business, it is critical that your advertisers are able to target the right segments of your audience, on the right stories with the right creatives.
Advanced editorial analytics tools such as NativeAI provide you with the additional layer of information by classifying content into categories and quantifying user engagement levels by segment. This will help you identify your audience personas and segments much better and allow your advertisers to target each distinct set and grab their attention.
It goes without saying that the editorial team is the primary consumer of Editorial Analytics, features such as:
- Historical performance comparisons
- Filtering analytics reports by Authors, Topics, Tags or Audience Interests
- Comparing audience behavior across different formats of content - video vs. listicles vs. long reads
- Identifying gaps in your topic mix - categories where the appetite of your audience to read may not be served by enough volume of stories
- Engagement Quality reports for stories that indicate audience attentiveness
Each of the above features allows editors to understand their readers even better and recognize the various factors that help attract readers and hold their attention.
It is a no-brainer that Editorial Analytics is a must-have in the current digital publishing industry scenario as it provides an unmatched competitive edge of awareness in your ability to engage your audience. We at NativeAI provide a AI-powered Editorial Analytics platform that has been built ground-up for publishers. Try our demo today and experience the difference!