How To Calculate Aeo Score

To calculate an AEO score, evaluate a page against the factors that answer engines use to extract and cite content, rate each factor, and combine those ratings into a single estimate of citability. There is no single official formula — the reliable approach is a structured audit across the known signals, weighted toward the ones that matter most.

A practical way to do it:

1. List the factors — direct answers, structure, structured data, factual clarity, sourcing, entity coverage, quotability, and technical hygiene.
2. Assess each factor on the page, ideally per query the page targets.
3. Weight them — give more weight to direct answers and structure, which most influence extraction.
4. Aggregate into an overall score or grade.

The goal isn't a precise mathematical output; it's a repeatable, diagnostic assessment you can act on and re-run after changes.

Start with the factors that most directly control whether a model can extract and quote your page:

• Direct answers — Does each section answer its heading in the first sentence? Buried answers are the single most common AEO failure.
• Structure — Are there descriptive, question-style headings, short paragraphs, and bullet lists or tables for enumerable facts?
• Quotability — Do key sentences stand alone, or do they depend on "as mentioned above" context that breaks when lifted out?

These three largely determine extractability. A page that nails them gives the model ready-made, attributable text. Only after these are solid does it pay to fine-tune the remaining signals. When scoring, treat these as high-weight items: a page can have great sourcing and schema but still score poorly if the answers are hard to find and pull out cleanly.

Beyond extractability, answer engines favor content they can trust and that thoroughly covers the topic. Assess:

• Sourcing and credibility — Are claims backed by primary sources or data? Are author bylines and dates present? Vague, unsupported statements lower citability because models hesitate to attribute them.
• Factual clarity — Are statements specific and unambiguous rather than hedged marketing language?
• Entity coverage — Does the page address the full set of people, products, concepts, and related questions the topic implies, so it reads as a complete reference?
• Freshness — Is the content current, with recent updates where facts change over time?

Rate each as strong, partial, or weak. These signals often separate two structurally similar pages: the one with credible sourcing and complete entity coverage is the more citable source. Weight them meaningfully, but below direct answers and structure.

A page can't be cited if an engine can't retrieve it, so technical hygiene is a gating factor in any AEO calculation. Check:

• Crawlability — Is the page reachable, and does robots.txt allow the relevant crawlers? Some AI crawlers respect specific user-agents you may be inadvertently blocking.
• Clean HTML rendering — Does the meaningful content exist in the served HTML, or is it hidden behind heavy client-side rendering that crawlers may not execute?
• Performance — Does the page load quickly and reliably?
• Indexability — Is the URL free of noindex directives and canonical conflicts that would exclude it?
• llms.txt / clean markdown — Do you expose an agent-friendly, low-noise version of key content?

Treat these as pass/fail gates. Strong on-page content behind a wall of technical problems will score low, because none of the extractability signals matter if retrieval never happens.

Once you've rated each factor, combine them into a single, repeatable score:

• Rate each factor on a consistent scale (for example, weak / partial / strong, or 0–100).
• Apply weights reflecting impact: heaviest on direct answers and structure, meaningful on sourcing, clarity, and entity coverage, gating on technical retrievability.
• Aggregate into an overall figure and, crucially, keep the per-factor breakdown so the score is actionable.
• Re-run after changes to measure improvement on the same page over time.

Avoid treating the number as absolute truth — the point is prioritization and progress tracking, not a proprietary certainty. The per-factor view tells you what to fix next; the trend line tells you if you're improving.

Doing this by hand across many pages is slow. Fastlook automates the audit, scoring, and re-scoring loop so you can prioritize the highest-leverage fixes at scale.