SEO Evolution Analyzer

Fast-moving numbers are easy to misread and easy to get wrong. This page separates the figures you can verify at the source from the methodology numbers behind the SEO Evolution Analyzer, so you know what to trust and what to re-check.

How fast is AI search growing?

The most-cited datapoint is ChatGPT's growth: OpenAI reported 100 million weekly active users roughly two months after launch, and by 2025 reported over 400 million weekly users. Those figures are published by OpenAI and are the single most-referenced evidence that AI assistants reached mainstream scale faster than any consumer software before them.

What matters for marketers is the shift in where answers come from, not just user counts. When a prospect asks an assistant "which product should I choose?", the answer is synthesized from sources the engine trusts. If your brand is not among them, it is absent from a growing slice of demand. Verify current user figures at the source before quoting them externally, since they move quarterly. The underlying trend, however, is not in dispute: assistants have become a primary entry point for product research across every category that has comparative buying questions.

What does a citation test actually measure?

A citation test asks an AI engine a set of realistic buyer-intent prompts and counts how often your brand is mentioned. The methodology in the analyzer at seo-ea.com uses n=4 to 6 prompts per engine, reports a citation rate from 0-100%, and returns a share-of-voice matrix comparing your brand against competitors in the same run.

The n=4 to 6 prompt count exists because LLM answers are stochastic: a single prompt is too noisy to judge progress. Running multiple prompt variants and averaging is what turns a noisy signal into a measurable trend. Re-run the same prompt set monthly on the seo-ea.com analyzer and compare against the same competitors.

One prompt is an anecdote. Five prompts is a measurement. The difference between them is the whole discipline of citation testing.

What is a good readiness score?

The analyzer scores each URL from 0-100% as a 25% weighted blend of AEO, AIO, GEO, and crawlability. Scores of 80 or above indicate strong readiness, 60-79 indicate moderate readiness with focused gaps, and below 60 indicates systematic problems that no single fix resolves.

The four categories are intentionally separate because they measure different outcomes: AEO measures whether your answer is extractable, AIO whether Google surfaces you in AI Overviews, GEO whether generative engines name your brand, and crawlability whether any of it is possible at all.

What happens when you block AI crawlers?

Blocking critical AI crawlers — GPTBot, ClaudeBot, PerplexityBot, or Google-Extended — caps the readiness score at 40 in the analyzer. The cap is deliberately harsh because a site that hides from AI crawlers is structurally invisible to every engine that answers AI search queries.

The fix is usually a few lines in robots.txt, which makes crawlability the highest-return signal on the checklist. Unblocking crawlers, adding llms.txt, and confirming it returns HTTP 200 at the domain root routinely moves scores more than any other single change.

Which on-page signals move the score most?

In the analyzer's weighting, the largest AEO weights are atomic answer paragraphs and question-format headings, the largest AIO weight is internal link density, and the largest GEO weights are sameAs entity links and statistical density. The table below summarizes the heaviest signals across frameworks.

How fast do on-page changes move the score?

Structural fixes show up in the readiness score within days because crawlers read the changed page almost immediately. Citation rates are slower: new content influences answers after indexation and training-data cycles, typically 4 to 8 weeks, and category-level prompts depend on third-party mentions that compound over quarters.

This split is why the analyzer separates readiness (structure, fast) from citation testing (outcomes, slow). Judge progress on each metric with its own time scale, and do not abandon a structure change because a citation test has not moved in the first two weeks.

Which industries are most affected by AI search?

Industries with expensive, considered purchases feel AI search first: B2B software, financial services, legal, healthcare, and travel. Buyers in these categories ask comparative questions — "which tool?", "what plan?", "is product X worth it?" — and the assistant synthesizes an answer from a handful of cited sources.

The common thread is that a single answer replaces a page of options. When that happens, the brands cited in the answer capture the demand and the brands omitted lose it entirely. This is why citation testing matters most for comparison-heavy, high-ticket niches: the cost of being absent is a lost deal, not a lost click.

How do you track AI search visibility over time?

Track three numbers on a monthly cadence: readiness score for your key pages, citation rate from a fixed prompt set, and share of voice versus competitors in the same runs. Each answers a different question and each has a different time scale, so track them separately rather than averaging them.

Readiness is the fast metric and moves with structure changes in days. Citation rate is the slow metric and moves with indexation and authority in weeks to months. Share of voice is the fair metric, because it controls for the stochasticity of LLM answers by measuring your presence against competitors in the same run.

How much does AI search change SEO planning?

The 25% weighting behind the readiness score is itself a planning statement: crawlability gates everything, and AEO, AIO, and GEO each capture a different outcome surface. Budget and roadmap decisions should treat these as separate workstreams rather than a single "AI SEO" line item.

A practical ratio used by teams we see: roughly 40% of new content effort on structure (headings, atomic answers, schema), 30% on measurement (citation testing and readiness re-scans), and 30% on authority (third-party mentions and link building). The exact split varies by niche, but the discipline of measuring all three monthly is universal. The ratio is a starting point, not a law: if your niche is dominated by comparison queries, shift effort toward structure and citation testing; if it is dominated by informational queries, shift toward depth and authority.

Crawlability gates, structure wins quotes, authority wins mentions. Budget for all three or the fourth is the one that holds you back.

Why do citation results vary between runs?

LLM outputs are sampled rather than deterministic, so the same prompt can produce different answers across runs. A brand can be cited in one run and absent in the next through randomness alone. Averaging n=4 to 6 prompt variants and multiple runs is the only reliable way to separate signal from noise.

A related effect is that the exact competitor set changes between runs: one run might name Freshsales while another names Salesforce. Treat the share-of-voice distribution as the finding, not the identity of any single competitor in any single answer. Over a monthly sampling window, a stable share-of-voice trend is far more decision-relevant than any individual answer, and it is the number worth putting in a report.