Getting historical context to plan an evergreen content strategy

Evergreen content is becoming an increasing focus across our global publishers. Creating a strategy around those pieces, or even the occasional zombie article, can support your goals around attracting new readers and creating loyal users over time.

Below, we wanted to show you how historical insights (while adding context through our Historical Dashboard) can complement your real-time data and in turn, inform the best mix of evergreen and other types of content within your publishing strategy.

A historical view of data to see ahead

Real-time insights give you those important, immediate signals of your content’s attention levels and the adjustments needed to keep audiences engaged. However, it’s important to establish historical context — not to dwell on your past successes or failures — but rather, to determine the key drivers of engagement as you build a path forward.

Let’s begin with setting goals. It’s important to build both short-and long-term strategies that:

  • Use relevant KPIs (organizational- or team-wide)
  • Focus on the drivers of long-term engagement and loyalty
  • Can be easily built into workflows
  • Evaluate content performance in the daily, weekly, or monthly context that makes sense for your organization

The last point is an important one, so let’s dig into the optimal timeframe to evaluate performance as you build out your strategy.

Analyzing historical data to help set your content calendar

The unique nature of content production (evergreen or otherwise) means a decision should be made about the frequency of historical analyses. If you publish several pieces a day, “performance history” may be better captured in daily or weekly reports. In contrast, publishing once a week or less means monthly reviews could be a better indicator of whether your audience engagement tactics are paying off.

Once your content analysis timeframe is set, here are some tips to look back and monitor periodic performance:

1. Daily reviews of data

On a day-to-day basis, timing and unique reader interactions may provide the richest insights to your team. For instance, our Historical Dashboard (as shown below) can help you answer the following daily questions:

  • Do certain hours of the day see more readers than others? 
  • When are the best times to serve your desktop or mobile audiences?
  • What does your referral data tell you about the best time to post on social media?

Answering these questions helps you make quicker adjustments to increase engagement, giving you more time to undertake deeper analyses.

2. Weekly analysis of data

Apply the same thinking as above on a weekly basis, with these additional considerations:

  • What audience behavior trends am I seeing on Monday vs. Friday vs. Sunday? 
  • What type(s) of content is contributing to higher or lower levels of engagement across the audience segments that matter most (e.g., mobile readers or loyal audiences)? 
  • Is there a commonality among lower or higher levels of readership at certain points on a certain day(s)?
  • How does my publishing schedule (time/frequency) impact Engaged Time or Recirculation across my site?

Weekly insights can help you proactively plan cross-channel content ahead of time for the times and platforms where audiences want it most.

3. A monthly look back at data

Bring all of the above together by understanding your KPIs on a monthly basis, even if you’re tracking progress towards half or quarterly goals. Then you can cover a spectrum of questions, such as:

  • Did I/my team hit monthly metrics goals
  • What channel(s) drove the most traffic to my site?
  • What sections/authors exceeded my KPIs?
  • Were there any outliers, such as resurfacing “zombie” content, that could help inform future content planning?

The ability to look back at even a month’s worth of data can give you enough perspective to understand what, if any, adjustments are needed to meet your goals. That, and it can also help support the decision to reallocate resources to the types of content or channels that are in need of more attention.

The takeaway: Putting Historical data into action

Chartbeat users, with the help of our Historical Dashboard, can use the tactics we’ve covered to provide daily, weekly, and monthly insights (which we provide through our Reports tool) across their teams. That said, anyone can take the following steps to ensure their data-informed planning and evergreen content strategy is calibrated for their needs.

Now it’s time to begin analyzing your data. Here are some actionable next steps based on the tactics we’ve discussed above.

1. Identify trends across your content

Look for data outliers across your channels over the daily, weekly, or monthly timeframes that matter to you (e.g., does social traffic peak in the morning or the evening? What about over the course of the week?). 

Then, compare your recent traffic and engagement for different segments to a previous time period, identifying any over- or -underperformance outliers or trends, all to ramp up your long-term learning.

2. Build a profile for success 

Use our auto-generated insight badges, which compiles all of your outperforming content across channels (e.g., the stories with a “High Social”, “High Search” or “High Mobile” badges). 

This can lead you down a path of asking whether the topic, timing, or type of narrative led to more engagement. Record the results so you can build a profile that sets up your content for future success.

3. Use those profiles, other signals to execute strategy

You’ll begin to see signals that you had a quality story with a high Engaged Time that didn’t reach its audience potential. In Chartbeat, this will come in the form of a story that has a “Needs Promotion” badge.

If this is content that normally garners attention, but underperformed relative to your expectations, you have options. In these cases, consider pushing it out again across channels, but with a new angle. And if you want instant insights, you can still see how the strategy is panning out in real-time.


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