Content decay is what happens when a post that used to bring in steady traffic slowly loses it — usually because search results have shifted, the information has aged, or newer content has overtaken it. The posts worth your time are the ones that were genuinely strong and are now fading, not the ones that were never big to begin with.
Here's how to find them on the Explore page.
First: how % Change works in Clariti
Clariti has three "% Change" filters and columns on the Explore page:
Page Views % Change
Sessions % Change
Visitors % Change
Each one compares the time window you pick against the equal window immediately before it. The window comes from a single control: as soon as you add a performance filter, a Performance Period selector ("Show metrics for…") appears with your filters, and it applies to every performance filter and column at once. Pick 90 days and you're comparing the last 90 days to the 90 days before that; pick 1 year and you're comparing the last year to the year before it.
That choice matters more than anything else on this page, which brings us to the most common mistake.
Set the Performance Period to 1 year, not 90 days
If you compare the last 90 days to the previous 90 days, you are comparing summer to spring — or fall to summer. For a seasonal site, that will flag nearly every soup, chili, and holiday recipe as "decaying" every single summer, and nearly every salad and popsicle recipe as decaying every winter. That's seasonality, not decay.
Setting the Performance Period to 1 year compares this year to last year, so both windows contain the same seasons. What's left after that is real decline. (1 year is also the longest comparison Clariti offers — % Change isn't available for the 2 years or All time periods.)
Use a shorter period only when you're investigating something specific and recent, like a suspected algorithm update.
The filter recipe
On the Explore page, click Add Filter and build these four, then set the Performance Period to 1 year.
1. Page Views — a floor, not a ceiling
Set the minimum to something meaningful for your site, like 2,000 over the last year. This is what separates real decay from noise: a post that fell from 12 views to 4 is down 67%, but it doesn't matter. Raise or lower the floor until the list is a size you'd actually work through.
2. Page Views % Change — the decline itself
Set the range from -100% to -25%. Anything shallower than about -25% year over year is usually normal fluctuation rather than decay. Tighten it to -40% if you want only the steepest drops.
3. Date Published — exclude anything published in the last year
This step is the one people skip, and it's the difference between a useful list and a useless one. Brand new posts almost always show a big negative % change, because they're coming down off the spike they get when they're first published and promoted. That's completely normal and it is not decay — there's nothing to fix.
Set Date Published to end about 12 months ago so those posts drop out of the list.
4. Date Last Modified — find the stale ones
Set this to end about 6 months ago. A post that's declining and hasn't been touched in six months is a much better use of your time than one you already refreshed last month, which may just need longer to recover.
Then sort by traffic, not by percentage
Once the filters are set, add the Page Views and Page Views % Change columns using the columns icon in the upper right of the list (its tooltip says "Select Columns to Show in List"), then sort by Page Views, highest first by clicking the column header. In the table itself the long names are abbreviated — Page Views % Change shows as "Views %" in the header.
Sorting by traffic puts your biggest posts at the top, so you're looking at the largest amount of traffic actually lost. A post down 30% from 100,000 views has lost far more than a post down 80% from 3,000 — but sorting by percentage would bury the first one and put the second on top. Percentage tells you how badly a post is sliding; volume tells you how much it's costing you. Fix the expensive ones first.
Turn the list into a plan
Once you're happy with the list, don't leave it as a filter you have to rebuild every time:
If you don't have a project for this yet, create one first (for example "Content Refresh") — the assign option stays disabled until at least one project exists.
Open the actions menu (⋯) at the top right of the list and choose Assign to Project.
In the dialog, assign Filtered Items — it's selected automatically whenever filters are active, so there's no need to check every box by hand. (You can still hand-pick posts with the checkboxes and assign just Selected Items instead.)
The same menu has Manage Labels — a label like "Decaying" lets you track this group over time when you re-run the filter each quarter.
After you refresh a post, add an annotation on its Performance chart to mark the date. That way, when you look back in three months, you can see whether the update actually moved the line. See Understanding Your Performance Chart.
Confirm it's really decay
Before you rewrite anything, open the post and check the Performance tab:
Add a year comparison to see this year against last year on the same calendar dates.
Chart the Google traffic source on its own. If Google is down but Pinterest is flat, it's a search problem and the fix is the content. If everything fell off a cliff on the same day, it's more likely a technical issue or an algorithm update.
Check the keyword table for positions that have slipped. A post that fell from position 3 to position 8 will lose most of its clicks even though nothing about the post changed.
Common mix-ups
The filter and column is named Page Views % Change — in the table header it's abbreviated to "Views %", which is why you'll sometimes see it written as "Views % Change." When you're adding the filter or column, look for the full name.
There is no filter called Date Republished. Use Date Last Modified — it reflects the last time the post was updated in WordPress.
A % Change filter always follows the Performance Period. It compares your chosen period to the equal period right before it, so the period you pick is the comparison — and comparisons max out at 1 year.
Frequently Asked Questions
Why is a post I just published showing a huge negative % change?
Because it's coming down off its launch spike. New posts get a burst of traffic from email, social, and initial indexing, and then settle into a normal baseline. That's expected. Filter out anything published in the last 12 months so these don't crowd your list.
Is there a faster way to get started?
Yes — Super Search understands sorting phrases like "by drop in views" (or "by drop in sessions"). Type one and Clariti sorts your whole library by steepest decline and adds the right columns for you; then add the filters above to narrow it down. See the Super Search article.
Should I use Page Views, Sessions, or Visitors?
Page Views is the usual choice for spotting decay, since it's closest to how much a post is being read. All three behave the same way in the filters, so use whichever you track elsewhere — just stay consistent so your numbers stay comparable between reviews.
How far back can I compare?
Comparisons max out at 1 year, and Clariti keeps roughly two years of Google data — which is exactly enough for a 1-year comparison. If your site has been in Clariti for less than two full years, use a shorter period and read the results with seasonality in mind.
Can I just ask for this instead of building filters?
Yes. If you've connected the Clariti MCP, you can ask your AI assistant something like "what should I refresh?" or "what's losing traffic?" and it will run this same analysis — comparing recent traffic against an earlier baseline, then checking which of those posts haven't been updated in six months.
Still Have Questions?
Reach out to our team anytime. We're happy to help you dig into your content. Email us at [email protected].