Methodology
How the SEO Health Score and GEO Readiness Score are calculated.
KinetixSEO doesn't stop at a score. Every analysis follows the same path — find each issue, hand you a fix tailored to that exact page, then verify the fix went live with an included re-scan. The scoring described below is the "find" step, and it's built to be trustworthy: every score on this page is produced by deterministic, rule-based measurement of your page's real HTML, HTTP headers, and (where available) performance data — never by asking an AI model "how good is this page?" AI is only used downstream, to write the replacement copy for an issue our rules already detected. That distinction matters: it means the same page scores the same way every time, and every point deducted traces back to a specific, checkable fact about the page.
SEO Health Score (0–100)
The SEO Health Score is a weighted average of nine categories. Each category starts at 100 points and loses points for every issue our checks actually find on the page — there is no separate "good behaviour" bonus, just deductions for real, detected problems.
| Category | Weight | What it measures |
|---|---|---|
| Technical SEO | 20% | HTTP status, HTTPS, indexability (noindex/robots.txt), TTFB, soft-404s, canonicalization consistency, security headers, mixed content, sitemap validity, crawlable links. |
| Content Quality | 18% | Word count vs. page type, readability, filler-phrase ratio, duplicate-content risk, H1-to-body relevance, text-to-HTML ratio, specific claims and first-hand experience language. |
| On-Page SEO | 14% | Title tag, meta description, canonical tag, H1/heading hierarchy, anchor text quality, Open Graph and Twitter Card tags. |
| Schema Markup | 11% | Presence and validity of JSON-LD, deprecated or SERP-retired schema types, required fields for rich results, client-side-rendering blind spots. |
| Performance | 9% | Core Web Vitals — LCP, CLS, FCP, TTFB, INP. Excluded from your score entirely until a real measurement exists (see "Unmeasured categories" below). |
| Trust & Compliance | 8% | E-E-A-T signal scoring (see below), orphan-page and thin-internal-linking risk, cloaking/hidden-text/keyword-stuffing/link-spam risk signals, malware signals. |
| Local SEO | 8% | NAP (name/address/phone) completeness, opening hours and geo-coordinates markup, review-count signals — scored only when local-business signals are present. |
| AI Search Readiness | 7% | Front-loaded answers, entity density, statistics, freshness and author signals, llms.txt validity, AI-crawler access in robots.txt. |
| Images | 5% | Alt text coverage, modern format usage (WebP/AVIF), lazy-loading on LCP images, responsive srcset usage. |
Unmeasured categories are excluded, not guessed
The Performance category requires a real Core Web Vitals measurement. Until one exists for your page, that category is left out of the score entirely — its weight is not filled in with an assumed value. Instead, the remaining categories' weights are rescaled proportionally so they still sum to 100%. In practice this means your score is always built only from what we've actually measured, never diluted or padded by a category we haven't checked yet.
Grade
Your numeric score maps to a letter grade: A (90+), B (80+), C (70+), D (60+), F (below 60).
What to fix first: impact × effort
Every issue in your report is also ranked by likely return on effort, not just severity. Each issue gets an impact score (1–5, based on its severity — critical, high, medium, or low — boosted when the issue's own data shows a real measured magnitude, like the percentage of images missing alt text or a Core Web Vitals millisecond value) and an effort score (1–5, a fixed estimate of how much work that specific type of fix is — e.g. removing a stray noindex tag is effort 1, rewriting duplicate content is effort 5). The two combine into a single "fix this first" ranking, so the top of your report is the highest-return fix, not just the most severe-sounding one.
GEO Readiness Score (0–100)
Generative Engine Optimisation (GEO) readiness measures how likely your page is to be structured in a way that helps AI systems like ChatGPT, Gemini, and Perplexity find and cite it — distinct from AI Citation Tracking, which checks whether you are actually being cited today (see the FAQ below). It's a weighted average of four pillars, each graded on whether its underlying sub-signals are fully present (full points), partly present (half points), or absent (zero).
| Pillar | Weight | What it measures |
|---|---|---|
| Evidence Density | 35% | Does the page contain statistics, named entities, and a front-loaded, self-contained answer — the raw material an AI system can lift into a sourced answer? |
| Structure & Position | 25% | Article/BlogPosting or FAQPage schema, plus a question-per-section content pattern — content already organised the way AI answers get extracted. |
| Authority | 25% | A named author signal and a freshness/last-updated signal. |
| AI Crawlability | 15% | llms.txt and RSL licensing files present, and whether cross-platform AI bots (GPTBot, ClaudeBot, PerplexityBot) and Google-Extended are actually allowed to fetch the page at all. |
When the score is capped or withheld
Two conditions make the pillar math moot rather than just lowering it: a page with a noindex directive, or a page where every tracked AI crawler is blocked in robots.txt. If either applies alone, the score is capped at 60 regardless of how well the pillars grade — a page that can't be indexed or fetched shouldn't score as if it's readable. If both apply at once, we don't report a numeric score at all: the page is neither indexable nor fetchable by AI systems, so a 0–100 number would imply a precision that doesn't exist.
Trust & E-E-A-T
Content-level trust signals feed into the Trust & Compliance category above via four weighted dimensions: Trustworthiness (30%), Expertise (25%), Authoritativeness (25%), and Experience (20%) — modelled on Google's published E-E-A-T quality-rater concepts. Each dimension is scored from concrete signals actually found in the page (author attribution, cited sources, organisation schema, first-hand-experience language, and so on), not a subjective AI opinion.
Pages detected as "Your Money or Your Life" topics (health, legal, financial, and similar) are held to a stricter bar automatically — for example, a health or financial claim made under an anonymous byline, or a professional-credential claim (e.g. "board-certified") with no verifiable profile link, is flagged as a real problem there in a way it would not be on general content.
From finding to verified fix
Every deduction in your report links back to the exact finding that caused it, in plain language, paired with a fix written for that specific page — copy-paste text or code grounded in the page's own evidence, not generic advice, and produced natively in the page's own language rather than translated from English. When a fix genuinely needs something only you have — a real business fact, or a developer to ship a code change — we say so and flag it for review instead of inventing an answer.
Once you've applied your fixes, one re-scan is included with every paid analysis. It re-measures the page with the same deterministic checks and marks each fix verified, not applied, or regressed, so you can prove the change actually went live rather than take it on faith. That's a single included verification pass per analysis, not continuous monitoring.
If you'd like to see this in practice on a real result, run a free check on your own page, or see our Terms for what we do and don't guarantee about the outcomes these scores predict.