Digital Publishing Metrics in the Publishing Industry

Digital Publishing Metrics

Digital publishing has two main goals: 1) Articles must be well-written and well-read and 2) Must be profitable…not necessarily in that order of importance. So much of the success of an online publisher’s success is presumed to be driven by digital publishing metrics such as the number of visitors to their site that the additional information derived by evaluating the other visitor elements is often ignored.

Evaluating the performance of online articles by simply counting clicks puts you out-of-step with leading-edge data analytics practices. By limiting your data to the number of clicks only, you don’t even look for the additional data that’s available from multiple sources. When you examine other measurable data points, you’ll have the opportunity to view the article’s performance with a broader perspective. Often, the results indicate that not all clicks are good clicks.

Parse.ly, in February of 2017, surveyed 270 of the best digital publishers, publishing sites, brands, and agencies about their use of content analytics, highlighting major themes from the results in a series of posts. Clare Carr’s post titled, “The Metrics That Should Matter vs. the Metrics That Actually Matter to Publishers,” focuses on relevant metrics to digital publishing companies.

Measuring Customer Engagement

There are numerous analytics tools available that focus on article impact, customer engagement, monetization strategies and increasing revenue and profitability. The value of examining each visitor’s engagement from all sides can’t be overstated in the quest for quality journalism…

Digital Publishing Metrics - Digital PublishingDigital Publishing metrics might include:

  • Number of visits to an article
  • Actual engagement time
  • Share of social media traffic
  • Bounce rate (percentage of visitors who leave the site after viewing just the first page)

Counting clicks is the most elemental measurement, but it certainly has value because without the clicks, the other measurements don’t exist.

The more engagement time spent by a visitor, the greater likelihood of their returning. This metric would be critical if the publisher is considering a paid subscription. In that case, returning visitors are more valuable than those who click and leave.

Social media ranking also provides information on the quality of a reader. How often an article is shared on Twitter is a strong indicator of rank.

Examining the bounce rate gives digital publishers and journalists a clearer picture of interest and engagement than just counting clicks.

Data Analytics

Ideally, data analytics should be used to gain the best audience insights by combining data teams with marketing. First-party data compiled by your own company includes such data streams as logins, loyalty programs, and mobile interactions. By partnering with an organization whose data complements your own, you can supplement your existing data to include consumer behavior habits and shopping inclinations.By integrating company-held data with second and third-party data, teams that formerly had limited exposure to each other can work toward their shared goals. By integrating people, processes, and technology, you’ll be able to improve your audience analytics impact on the bottom line.

Audience Metrics

In the past, audience metrics included circulation numbers and Nielsen ratings. From there, it changed to page views. With the advent of the various platforms, like Twitter, Facebook, YouTube, etc., measures of page views have become imprecise audience metrics.

Content views of online items like videos, articles, and illustrations are a more accurate reflection of audience metrics. Linear data no longer provides the audience metrics that online publishers need to move forward and adapt to new and developing ways to reach audiences.

The analysis of data from the many platforms used in digital publishing affects decisions of where to invest, and which metrics contribute to the bottom line requires accurate data and effective analytics. Rather than using the information garnered from the many measurements to drive traffic to a website, quality content that engages the audience across the various platforms is proving to be more useful in gauging performance.

Predictive Modeling

Digital publishers, like newspapers, use predictive modeling as a critical component of a variety of decisions, but before they can analyze the data, they need to collect it. The data crunching tools that are now available provide more reliable information than has previously been available through surveys, focus groups, and customer comments. Predictive analytics uses preferences that are revealed through actual data rather than stated preferences. In this case, actions speak louder than words. With this dynamic data flow, publishers can react in real time to produce content that attracts loyal users.

Data-driven decisions for online digital publishers could include:

  • Advertising rate
  • Products for new and existing customers
  • Targeted marketing
  • Customized customer service (who gets discount/who pays full price)
  • Percentage of free content vs. paid

Advertisers were the be-all and end-all of revenue streams, but more and more, online publishers are using data to analyze and categorize their readers, resulting in options for readers who focus on politics or sports to pay for a subscription with the content they view most often. Access to the entire publication costs the most but pared down categories of content cost less.

Publishers are now able to estimate a customer lifetime value (CLV), and many have learned that new subscribers had a lower CLV than in previous years. This information had led to technical improvements like a redesigned website and mobile-friendly sites.

In the service of quality journalism, it should be noted that compelling journalism can attract a large enough paying audience to make up for any lost ad revenue. The key is to use the available predictive analytics to find a balance between free content supported by advertisers and paid subscriptions that are ad-free.

Predictive Analytics is a Living, Breathing Tool

In the fast-paced, ever changing world of digital publishing, measuring audience interest and loyalty and a publishers’ ability to attract new viewers is critical to success, whether that success is measured in quality journalism or profitability. Ideally, success should be measured in both areas.

The volume of visitors to your site is just the beginning of predicting how many will spend time reading and potentially paying for a subscription to ad-free content. The right data can help digital publishing companies decide at what point a paywall will help separate the wheat from the chaff. 

About The Analytical Consultant

Analytics and Business Intelligence professional with over 15 years of experience.

This entry was posted in Digital Publishing Metrics, Publishing Industry, Web Analytics Metrics and tagged , , , , , . Bookmark the permalink.

Leave a Reply