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Creating a Data-Driven Top 10 List That Impacts Content Strategy
Some of my favorite top 10 lists reflecting on the past year pull back the curtain on which metrics each publication used to develop their rankings.
Two Parse.ly clients shared which metrics they used to create lists of top posts in 2016. Scientific American found their Most Popular Science Stories of 2016 by looking at most visited articles. To identify The Sixteen Most-Read New Yorker Magazine Stories of 2016, The New Yorker measured time spent reading. Indicating how they created the lists provides critical context for their data. Without context, data is not as useful or actionable.
I set out to find Parse.ly’s top 10 blog posts of 2016 with two objectives in mind: 1) discern which metric would contextualize data in an interesting way, and 2) apply that information to content strategy.
Finding the Top Posts of 2016 by Engaged Time
The first step in my mission was to decide which definition of “top” best encompassed these goals.
I was curious about which articles most interested readers, which prompted me to look at engagement—specifically, engaged time. Organizing by engaged time rather than page views provides insight into which posts captured readers’ attention.
In the Parse.ly dashboard, I looked at top posts by total engaged minutes for the time frame of January 1 through December 31, 2016. I added a filter to exclude posts that were published before January 1, 2016 and filtered out our technical posts (see our post on digging deeper with data to see why we don’t include those in our content analysis).
Readers were most interested in presidential election data, namely the relationship between media coverage of candidates and article page views. Six out of ten of the top posts offer advice on how to use data analytics effectively or inform readers about digital media news.
Insights from Different Angles
I looked at the list through two other lenses: total engaged minutes for articles published at any time and average engaged minutes for articles published in 2016. Using additional metrics provided different insights about the same content.
After removing the date filter, only half of the top ten posts had a publish date of 2016. This is partly due to the type of audience we’re trying to reach and a focus on publishing a lower volume of content with higher distribution. Even so, seeing the data presented this way underscores that time spent creating and distributing evergreen content is valuable.
Here are the five top posts by total engaged time published before January 1, 2016.
Viewing posts by average engaged minutes (for all visitors) revealed that three of the top ten posts in the list have video.
When assessing how the top 10 posts of 2016 influenced content strategy, these two realizations provided good food for thought.
How the Top 10 List Impacts Content Strategy
The key is not just to measure data but to put it into action. What influence can this information exert on blog publishing strategy for 2017? A few ideas include:
- Continue to write data-driven stories about the topics that interest our readers. Seven of the top posts by total engaged minutes were from the Analytics That Matter section of the blog. More specifically, our readers are curious about data behind media industry behavior, such as political coverage and traffic referrer market share, and how to use data analytics to propel their own sites and stories forward in the current publishing climate.
- Tell more stories through video. Three out of ten of the top posts by average engaged minutes included video, indicating this medium drives reader engagement. Though it’s not surprising—we recently predicted video would be a primary concern for publishers in 2017—it’s important to note as a starting point for further developing video strategy.
- Identify and promote evergreen content. Removing the published date filter from the top posts by total engaged minutes list revealed half of the posts were not actually published in 2016, indicating the power of evergreen posts.
What are your thoughts on this approach to creating a Top 10 list? Here’s to more data-driven storytelling in 2017!