- ·Four axes: answer clarity, entity strength, topical placement, freshness — each 0–3.
- ·Score 9+ is citation-ready; 6–8 needs rework; <6 needs consolidation or retirement.
- ·Matrix score predicts 90-day citations 2.5× better than classical link metrics.
- ·Ship the scoresheet as a shared board so the client team owns the cadence.
Every audit engagement at Optimize Plus now includes a GEO readiness pass. The matrix is our internal shorthand, but it's useful enough — and asked about often enough — that we publish it here in full.
If you audit your own content and want a lens that predicts AI-search citation rather than classical ranking, this is the lens we use. It takes ten minutes per page once you're familiar with the axes and, in our internal data, predicts 90-day citation rate 2.5× better than link metrics do.
Why we needed a matrix
Classical SEO audits score pages on technical, on-page, and off-page signals. None of that reliably predicts whether a large language model will cite the page in an answer. A page can have perfect Core Web Vitals, a well-structured H1, and a strong backlink profile, and still never appear as a source in an AI Overview.
We needed a scoring lens purpose-built for generative surfaces — one that captured what those systems actually reward, and that gave us a defensible way to prioritise the content backlog when a client has 400 pages and budget for reworking 30.
The other reason we needed it: audit reports were getting longer while shipping less work. A one-page scorecard that ranks pages by expected citation lift is a better forcing function than a 60-page audit deck.
The four axes
Answer clarity: does the page state the answer to a real user question in the first two paragraphs? Not 'is the answer eventually on the page' — is it visible above the fold, in prose a machine can lift? Pages scoring high here read like the first paragraph is a standalone answer.
Entity strength: are authors, organisations, products, and locations named and linked with schema? Is there an About page for the author? A Person schema with sameAs to real profiles? Organization schema with a consistent NAP that matches Google Business Profile? Entity-strong pages are the ones AI systems trust to cite.
Topical placement: does the page sit inside a cluster of related pages that reinforce its authority, with descriptive internal links tying them together? A single article on a topic scores low even if excellent. A pillar plus twelve supporting pages scores high even if each individual piece is only good.
Freshness signal: is there a visible 'last updated' date, and does the underlying data reflect that date? A ghost-updated timestamp with identical content scores zero. A genuine refresh — updated statistics, current pricing, revised recommendations — scores full marks.
Scoring a page in ten minutes
Each axis scores 0–3. A page scoring 9+ (out of 12) is citation-ready and should be prioritised for internal linking and promotion. A page scoring 6–8 needs targeted rework, usually a rewrite of the intro plus schema additions plus internal links from adjacent pages. Below 6, the page is unlikely to earn a citation regardless of link equity, and we recommend consolidating or retiring it.
Two reviewers per page is enough. In our data, inter-rater drift stays under 15% when the axes are this explicit. We keep a one-page cheat sheet with example paragraphs at each score level, which cuts drift further and lets junior team members run the scoring pass reliably.
The scoring itself is not the deliverable. The deliverable is the prioritised backlog that falls out of scoring the top-100 organic-traffic pages, plus the rework brief for each 6–8 page that specifies exactly which axis to lift and how.
What the scores actually predict
Across ~1,800 scored pages we've tracked in production, citation rate at 90 days correlated 0.71 with matrix score and 0.28 with Ahrefs UR. That's the whole argument for using a purpose-built lens instead of borrowing classical metrics as a proxy.
Freshness was the single strongest individual axis (0.58 correlation), narrowly beating answer clarity (0.55). Entity strength (0.41) and topical placement (0.39) were weaker individually but their interaction with the other axes was where the compound gains showed up: a page scoring high on all four earned citations at 4.7× the rate of a page averaging equally across two axes and zero on the others.
The practical implication: don't optimise one axis in isolation. A page rewritten for answer clarity but left with no entity signals still won't get cited. The compound structure is the point.
Using the matrix in production
We run the matrix during audit, we re-run it monthly on target pages, and we use it to prioritise the content backlog. Pages moving from 5 → 8 in a quarter are the fastest path to citation share in our data — faster than publishing new pages and cheaper than building new links.
The other production use: the matrix becomes a shared vocabulary between us and the client. 'This page is a 7 on clarity, 2 on entity, 3 on topical, 1 on freshness' is a much more productive conversation than 'this page is underperforming.' It tells the writer, the developer, and the SEO exactly what to touch, in what order, and why.
We ship the scoresheet as a Notion or Airtable board in every engagement. Clients update it themselves between our monthly reviews, which turns the framework from an agency deliverable into an operating cadence the internal team owns.
Case: a 5 → 9 rework in one quarter
A B2B SaaS client's flagship comparison page ('Product X vs Product Y') scored 5 in January: strong topical placement (3), decent freshness (2), no entity signals (0), and buried answer (0 — the actual comparison didn't appear until scroll 3).
The rework took nine hours across two weeks. We rewrote the intro so paragraph one stated the winner and paragraph two stated the exception. We added Person schema to the author, Organization schema to the company, and Product schema to both products being compared. We refreshed all pricing and feature data as of the current quarter and put the 'Updated' date in body copy.
By April the page was cited in the AI Overview for the primary query and two adjacent ones. Sessions rose 41%, but the interesting number was that trial signups from that page rose 84% — because the citations were sending pre-qualified users, not researchers.
The mistakes we made getting here
The first version of the matrix had six axes and took forty minutes to score. Nobody ran it. Cutting to four axes and ten minutes was the single biggest usability win.
The second version weighted the axes equally. It shouldn't have — freshness and answer clarity carry more predictive weight and we now flag them for reviewers to focus on when scores are borderline.
The third version tried to auto-score with an LLM. It was close-but-not-quite on entity strength and consistently wrong on topical placement, because those require reading a cluster of pages, not a single page. Human scoring stayed. LLM assistance is now a helper that pre-fills obvious answers, not a substitute.
We ship this exact playbook in engagements.
If what you just read maps to a problem in front of you, a 30-minute conversation is the fastest way to know if we can help.