Content Time Analysis - Interactive SOP

This Standard Operating Procedure (SOP) explains how to run a time-based performance analysis with Ahrefs data. It reveals how your content performs relative to its publication date and helps you identify improving or declining content over time.

Goal Measure content performance relative to publication dates instead of static site-wide averages.

Ideal Outcome You generate CSV outputs with performance indices, publication dates, and traffic metrics. Each page is benchmarked against content published before it, allowing you to clearly spot overperforming and underperforming content.

Why this is Important Static numbers (like “50 referring domains”) don’t mean the same thing at every stage of a site’s life. A brand-new site earning 50 links might be extraordinary, while a mature site earning the same could be average. Time-based analysis shows how impressive a result was for that moment in the site’s history.

💡 Competitive advantage: Few analysts account for temporal context. Looking at relative performance over time means you see opportunities others miss — for example, identifying older posts that were unusually strong for their period even if their absolute numbers look modest.

Where this is done Ahrefs Site Explorer for data exports, and the Content Time Analysis tool for processing and visualization.

When this is done Quarterly, or after major content launches, to monitor trends and build a temporal performance database.

Who does this Content Strategist or SEO Specialist.

Prerequisites or Requirements

• Access to Ahrefs Site Explorer
• Domain to analyze (yours or a competitor’s)
• CSV export with correct settings (see Step 1)
• Time to fetch published dates (5–15 minutes depending on dataset size)

Step 1: Upload Ahrefs Data

1. In Ahrefs Site Explorer, go to Top Pages.
2. Set date range to “Don’t compare”.
3. Enable “SERP titles”.
4. Export as CSV with UTF-8 encoding.
5. Include all available rows.

Step 2: Configure Published Date Fetching

1. Start the date-fetching process in the tool.
2. Set delay to 1000–2000ms for balanced speed and politeness.
3. Expect the process to take 5–15 minutes depending on dataset size.
4. Monitor progress in real-time.

💡 Publication dates let you calculate performance indices that normalize “how impressive” a result was at the moment it was published — not years later when expectations had changed.

Step 3: Run Time-Based Analysis

💡 Why median matters: Using the median prevents one viral hit from distorting the benchmark. For example, a post earning 200 links on a site that normally earns 1–2 would artificially inflate the average, making other posts look weaker than they really were. The median resists these distortions.

Step 4: Interpreting Results

• Performance Indices:
  • > 1.5 = exceptional performers
  • 1.0–1.5 = solid performers
  • 0.5–1.0 = underperformers
  • < 0.5 = poor performers
• Historical context: A post with 40 referring domains in 2015 might be more impressive than one with 57 in 2019, because expectations rise as a site grows.
• Relative performance: Indices show how surprising or impressive a result was for that period.
• Sorting advantage: Sorting by index, not absolute values, surfaces hidden gems — posts that would be overlooked if you only scanned the top-10 by links.

Optimization Strategies

• High performers (Index > 1.2): Replicate and refresh these as models for future content.
• Underperformers (Index < 0.8): Revisit for updates or consolidation.

Time-based analysis shows not just what content performed, but how impressive it was at the time. That perspective reveals hidden winners, uncovers overlooked opportunities, and gives you a research edge competitors rarely use.