Goal Provide a clear process for conducting a content inventory analysis using Ahrefs data to classify, rank, and evaluate website content.

Ideal Outcome You generate a complete content inventory with quartile rankings, composite scores, and efficiency metrics that highlight star performers, optimization candidates, and underperformers.

Why this is Important This analysis creates a roadmap for content optimization. It helps you decide which pages to amplify, which to improve, and which to remove, ensuring resources are used effectively.

Where this is done Inside Ahrefs Site Explorer and the Content Inventory Analysis tool.

When this is done Whenever you need to assess your or your competitor’s website’s content performance, typically once per quarter or after publishing a significant batch of new content.

Who does this Content strategists, SEO specialists, or marketing team members responsible for content performance.

Prerequisites or Requirements

• Website domain to analyze (own site or competitor)
• Access to Ahrefs Site Explorer
• CSV export from Ahrefs with correct settings

Step 1. Export Data from Ahrefs

1. Open Ahrefs Site Explorer.
2. Navigate to Top Pages.
3. Set date range to “Don’t compare” to prevent errors in the export.
4. Enable “SERP titles” for proper page identification.
5. Export as CSV with UTF-8 encoding.
6. Select all available rows for the most reliable dataset.

Step 2. Upload Data

Step 3. Run Content Inventory Analysis

💡 Tip: Quartiles divide your content into four buckets (Q1–Q4). Instead of obsessing over raw numbers, quartiles show relative performance. If a page lands in Q4, it outperforms 75% of your content. If it lands in Q1, it underperforms compared to 75% of your portfolio. This way you focus on patterns, not isolated datapoints.

Step 4. Interpret

1. Check 2–8 point composite scores:

   • 6–8: High-performing content; amplify and maintain.
   • 4–5: Mid-level performers; prioritize optimization.
   • 2–3: Underperformers; consider overhaul, consolidation, or removal.

2. Identify efficiency champions with high referring domains and low traffic.

   Example: A page with 300+ referring domains but very little traffic signals “earned links” (people linked because of quality or originality, not just rankings).

3. Spot low-efficiency pages where traffic doesn’t translate into authority. These may need targeted outreach or better internal linking.

💡 Context: Referring domains per visit helps separate passive link-building (pages rank high, so they naturally get links) from active link-worthiness (pages get links because they truly resonate). Especially useful when analyzing competitor sites — it highlights which posts genuinely gained traction, not just rankings.

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.

Contents

1. Definitions

1.1 Linear approximation

Replace the curve with the straight line that matches it at $x=0$ in height and slope.

1.1.1 Slope intuition

A line through $(0, f(0))$ needs one more number: its slope. Pick the slope wrong and the line peels away from the curve immediately. Pick $f'(0)$ and it stays glued for as long as possible.

1.1.2 Reconstructing a and b

The other way to read the formula: linear approximation is a search for the best $a + bx$.

Write $P(x) = a + bx$ and demand it agree with $f$ at $0$:

There is no choice left — matching height and slope forces both coefficients. So if you hand the formula something that already is a line, it must hand the same line back:

1.2 Quadratic approximation

Same idea, one more term: match the curve at $x=0$ in height, slope, and curvature.

1.2.1 Parabola intuition

A line can only match height and slope; it is always straight while the curve bends. Adding an $x^2$ term lets the approximation bend too, and $f''(0)$ says how much.

1.2.2 Reconstructing a, b and c

Now the search is for the best $a + bx + cx^2$. Write $P(x) = a + bx + cx^2$:

That $\tfrac{1}{2}$ is not a fudge factor — it is there because $P''(0)$ comes out as $2c$ rather than $c$.

Set $c=0$ and the quadratic formula collapses into the linear one. Linear approximation is the quadratic with curvature switched off, which is why a function with $f''(0)=0$ has the same linear and quadratic approximation.

2. Cheatsheet

2.1 Near 0

Each entry lists $f(0)$, $f'(0)$ and $f''(0)$, then both approximations, then the values against a direct calculation, then the graphs.

2.1.1 Trigonometric

2.1.2 Exponents and logarithms

3. Combining approximations

Rather than differentiating a complicated function twice, build its approximation out of the entries above. The one move that covers most cases: substitute, then truncate.

Truncate means: discard every power above the order you are working to — $x^2$ and up for linear, $x^3$ and up for quadratic.

For composition there is also the direct form, which is just the chain rule:

Two conditions worth keeping in mind:

• The inside function must land near 0. Every entry above is an expansion around $0$, so substitution is only legal when $g(0) = 0$.
• Truncating is not optional. The product of two linear approximations contains an $x^2$ term that is not trustworthy — the inputs were only accurate to first order, so the second-order information in the product is noise.

That second point is worth seeing. Multiplying two linear approximations gives

Drop the $x^2$ and the coefficient of $x$ is $f'(0)g(0) + f(0)g'(0)$ — exactly $(fg)'(0)$ from the product rule. The shortcut agrees with the definition, which is why it is safe.