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Freshness Decay & Stale Content

Classifies your topic's freshness expectations and calculates staleness. Different topics (news vs evergreen) have different decay rates.

Why It Matters for AI Visibility

AI engines do not treat all content age equally. A six-month-old news article is stale, but a six-month-old tutorial is still perfectly fresh. ChatGPT, Perplexity, and Google AI Overviews factor content freshness into their citation decisions, and they penalize outdated information -- but "outdated" depends entirely on what the content is about. This factor goes beyond a simple age check. It classifies your content into one of five topic types -- breaking news, time-sensitive, reference, evergreen, or general -- and applies different freshness thresholds to each. A "how to" guide has a two-year freshness window, while a price forecast becomes stale after just one year. The real-world impact is significant. An evergreen guide published 18 months ago can score a perfect 10, while a trends article from the same date scores a 4. If your content references specific years, prices, or forecasts, AI engines hold it to a much stricter update cadence. Understanding your content's topic class is the key to managing freshness efficiently.

How We Score It

The analyzer first classifies your content into one of five topic types by scanning for keyword signals: breaking news (7-day freshness window), time-sensitive (90 days), reference (365 days), evergreen (730 days), or general (365 days). Each type has its own staleness threshold. Your score depends on how many days have passed since the most recent update date relative to your topic's thresholds. Content within its freshness window scores 10. Content in the aging zone (between fresh and stale) gradually decays from 10 down to 6. Stale content scores 4, and very stale content (past twice the stale threshold) scores 2. Pages with no dates at all -- no datePublished, no dateModified, no visible update text -- automatically score 2. The topic type makes a massive difference: the same one-year-old content scores 10 as evergreen but only 4 as time-sensitive.
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How to Improve

  • 1

    Know your content's topic class and update accordingly

    Breaking news needs review within days and should be archived after a month. Time-sensitive content (prices, forecasts, annual trends) needs quarterly updates. Reference material like API docs should be reviewed annually. Evergreen guides can go 1-2 years between refreshes. Match your revision cadence to what the analyzer expects for your topic type.

  • 2

    Add both datePublished and dateModified to every page

    Without any dates, the analyzer can only give you a score of 2 -- regardless of how fresh your content actually is. Both dates should appear in your JSON-LD schema. Even if the content was just published, set dateModified equal to datePublished so the signal exists from day one.

  • 3

    Refresh dateModified in schema and visible text simultaneously

    When you update content, change the dateModified in your structured data and update the visible "Last updated" text on the page. The analyzer checks both sources. A mismatch or a missing visible date weakens the freshness signal.

  • 4

    Schedule quarterly reviews for time-sensitive content

    Anything mentioning specific years, prices, forecasts, or trends gets classified as time-sensitive with a 90-day freshness window. Without quarterly updates, the score will decay below 6. Set calendar reminders to review and refresh these pages before they cross the aging threshold.

  • 5

    Remove time-sensitive language from evergreen content

    References to "2023 trends" or "this quarter" force a stricter freshness classification even if the core content is timeless. Rewrite these as "ongoing trends" or remove the temporal references entirely. This shifts your content into the evergreen class with a much more forgiving two-year freshness window.

Before & After

Before
Title: "2023 Marketing Trends You Need to Know"
Published: January 2023, never updated
Topic class detected: time-sensitive (signals: "2023", "trends")
Drift: ~800 days
Score: 2 (very stale)
After
Title: "Marketing Trends: A Timeless Framework"
Updated statistics, removed year-specific language
dateModified: March 2025
Topic class detected: evergreen (signals: "guide", "framework")
Drift: ~10 days
Score: 10 (fresh)

Code Examples

JSON-LD with both publish and update dates

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Complete Guide to Content Marketing",
  "datePublished": "2023-01-15",
  "dateModified": "2025-03-20"
}

Frequently Asked Questions

How does the analyzer decide if my content is time-sensitive vs evergreen?

It scans for keyword signals in your text and HTML. Year references ("2024"), words like "latest", "trending", "price", and "forecast" classify content as time-sensitive. Words like "how to", "tutorial", "guide", and "what is" classify it as evergreen. Two or more signals from one category determine the classification.

My evergreen guide is 3 years old but still accurate. Will it fail?

Evergreen content has a 730-day (2-year) freshness window and does not become stale until 1,460 days (4 years). At 3 years old, it falls in the aging zone, scoring around 6-8. Adding a recent dateModified after a content review resets the drift and can bring it back to 10.

What revision cadence should I follow for each content type?

Breaking news: review within days, archive after one month. Time-sensitive: review every 3-6 months. Reference: review annually. Evergreen: review every 1-2 years. General content: at least annually. These cadences align with each topic type's freshness thresholds.

Related Factors

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