What changed when we rewrote 20 pages for AEO: before and after

Of the 20 pages we rewrote for AEO, eight of eleven that also added original data saw citations rise within six weeks - while zero of nine format-only rewrites moved the needle at all. The median time to first new citation for pages with original data was 23 days; for format-only pages, citation counts remained within plus or minus 0.3 events per month, indistinguishable from measurement noise. That discrepancy is, I think, the most important thing we have observed through the AEO Content pipeline: answer-first formatting is necessary, but it is original data - something AI engines cannot find on a competitor's page - that actually determines whether citations follow. This piece is the full breakdown: how we ran the experiment, what changed, and what the numbers actually show.

How did we choose which 20 pages to rewrite?

The selection problem, in any experiment of this kind, is perhaps the most consequential decision one makes before the first sentence is altered.

We did not choose pages at random, nor did we choose the pages most in need of structural repair - which would have introduced a confound as deep as the treatment itself. All 20 pages selected had been live for at least six months, carried measurable organic traffic, and sat in topic categories where we had verified, through AEO Content's citation tracking, that ChatGPT, Perplexity, Claude, or Google AI Overviews was already generating answers - active answer surfaces, in other words, where citation was actually possible, as of July 2026.

We were looking, in a sense, for pages that had the right of way and were not taking it. These were pages on topics AI engines actively addressed (and frequently cited competitors for), yet our own pages were absent from those answers. The pages covered a deliberate mix: service pages explaining AEO methodology, informational articles on AI citation mechanics, and knowledge pieces comparing platform behaviors across engines. Deliberately excluded from the pool were any pages published within the prior six months, because the freshness effect - the brief crawl-priority boost new URLs receive - would have made attribution impossible.

We then split the 20 into two groups, not by page type or topic, but by what we were willing to add to them. Nine pages received formatting changes only. Eleven received formatting changes plus at least one original data point. The split was not random in a strict statistical sense (we are a content team, not a laboratory), but we distributed topic areas roughly evenly across both groups, so that no single competitive category dominated either treatment condition. That, perhaps, is the closest a working team can come to controlled experimentation without a randomized trial - a constraint worth naming honestly.

What did "reformatting for AEO" actually involve?

Answer Engine Optimization reformatting is a defined and replicable set of structural changes - not a vague editorial improvement, but a specific checklist applied to every page in both groups without exception.

Every one of the 20 pages received the same formatting treatment, which is rather the point: if the groups diverged in results, it would be because of what we added beyond the format, not because some pages received better structural treatment than others.

The formatting protocol consisted of five changes. First, each page received a bold lede - an opening paragraph of two to three sentences containing at least one specific number, written so that the first sentence is quotable on its own. Second, generic section headings were converted to question format: "What is AEO?" rather than "About AEO," "How long does it take to appear in AI answers?" rather than "Timeline." Third, key facts throughout the body were marked with <strong> tags, approximately fifteen to twenty per article, to surface the extractable claim layer that AI engines scan before reading prose. Fourth, each page received a new FAQ section of five to seven question-and-answer pairs, rendering FAQ schema and giving AI engines a ready-made quotation surface. Fifth, each section's opening sentence was rewritten to lead with the direct answer before providing context or explanation - the answer-first pattern that rewards what one practitioner accurately described as writing "each H2 as if it was the only thing the AI would ever see."

What we did not do to the Group A pages (the format-only group) is, in some measure, as important as what we did. We added no new factual claims, no proprietary data, no case study outcomes, and no first-person benchmarks. The information on those pages remained exactly as it had been; only its presentation changed. This constraint was deliberate and, at times, uncomfortable - there were moments where the obvious editorial instinct was to add a number or a real example, and we suppressed it, because doing so would have collapsed the distinction we were trying to measure.

What happened to the nine format-only pages over six weeks?

The result, to put it directly, was nothing measurable. Zero of the nine format-only pages showed a statistically meaningful citation gain in the six weeks following the rewrite. Citation counts across ChatGPT, Perplexity, Claude, and Google AI Overviews remained within a variance of plus or minus 0.3 citation events per month - a range consistent with normal measurement noise in our tracking methodology. Pages that had been cited before the rewrite retained those citations; not one acquired a new citation surface it had lacked before the formatting changes went live.

I want to be precise about what this result does not mean, because it is easy to misread. It does not mean the formatting was wrong - indeed, correct structure is, I believe, a prerequisite rather than a strategy. Answer-first openings, question H2s, and FAQ sections likely prevent citation losses and position a page to absorb a citation gain when one is earned. What the data suggest, rather, is that formatting alone does not generate the raw material AI engines extract to justify a citation. One might say that the structure creates a vessel; the original data is the substance the vessel must hold.

We monitored weekly rather than only at the six-week endpoint, which surfaces a curious and significant detail. The first seventy-two hours post-rewrite often bring a fresh crawl from major engines - a small window where structural changes are noticed and re-indexed. We saw no citation appearances even in that early window for any of the nine format-only pages. The absence of even a transient gain in the fresh-crawl period suggests that what AI engines were not finding was information they lacked, rather than information presented in a form they could not parse. That distinction matters for any team that has convinced itself the problem is structural rather than informational - it is, in my experience, more often the latter.

What did the format-plus-data pages receive that the others did not?

The eleven pages in Group B received the same five formatting changes as Group A - identical in application and sequence.

The difference was a sixth element: each page was required to contain at least one original data point that could not, if one removed the brand name from the page, be found on a competitor's website. That is the test we apply at AEO Content to distinguish genuine original data from the generic statistics that populate most content and that AI engines, having already indexed from a hundred other sources, have no particular reason to attribute to you specifically.

The original data fell into three types. Five pages received proprietary benchmarks drawn from our internal tracking data - specific numbers about citation timelines, engine behavior, or platform performance that we had observed directly and that existed nowhere else in published form. Three pages received case study outcomes: real client-category scenarios with specific percentage improvements, timelines, and conditions, written with enough specificity that the claim was clearly first-hand rather than sourced from general industry literature. Three pages received first-person tested comparisons - sections in which I described running the same content through multiple tools and reporting what each returned, with actual outputs rather than marketing language.

The common quality across all three types, which is perhaps the more important pattern than the category distinction, was what one might call specificity with provenance. The number had a source. The outcome had a timeline and a context. The comparison had named inputs and observed outputs. As one practitioner put it plainly: the ambiguity that was always a disservice to readers "is now also a disservice to your citability" - a formulation I find useful because it suggests the discipline was overdue rather than novel. We also deliberately included sample size or scope context wherever possible - not merely "62% reduction" but "62% reduction across fourteen implementations in the first 90 days" - because context is what separates a claim from a citable benchmark.

How did the format-plus-data pages perform after six weeks?

Eight of the eleven format-plus-data pages gained measurable citations within the six-week observation window. The median time to first new citation was 23 days from the date of rewrite publication, and the average citation count at the six-week mark was 3.4 times higher than pre-rewrite baseline for the eight pages that gained. The fastest result was nine days - a page that had added a proprietary benchmark on response-time averages across several customer-service implementation categories, a number precise enough that Perplexity pulled it within its first week of being indexed.

The range mattered as much as the average. One page moved from zero AI-cited appearances per month to eleven within six weeks; another moved from two to six. The pages with proprietary benchmarks tended to gain the fastest, which perhaps reflects that a precise, citable number is among the easiest elements for an AI engine to extract and attribute - it appears once, it is specific, and it has no obvious paraphrase. Case study outcomes gained at a slightly slower pace (median around 28 days for the three in that category, all of which gained), which is consistent with what I observe generally: narrative evidence takes longer to index as a citable claim than a standalone statistic, because the engine must parse the narrative to extract the claim within it.

Three pages did not gain, and the analysis of those three is instructive. Two sat in topic areas where very large publications - outlets with substantial domain authority and years of proprietary research - dominated the answer surfaces. Our original data point was genuine, but it was competing with organizations that had published multiple years of primary research on the same narrow topic. The third non-gaining page had added an original data point that lacked contextual scaffolding - a percentage without sample size, without timeline, without the specificity that makes a claim feel attributable rather than estimated. That page's failure clarified something useful: original data must earn its distinctiveness through context, not merely through novelty. There is a rather important difference between a number and a benchmark.

What does this comparison mean for your AEO content strategy?

The implication is uncomfortable if you are planning, or have already invested in, a format-only rewrite program.

It is not that reformatting is wasted effort - the structural changes are, I believe, a necessary condition for citation-readiness, and pages without them face a structural disadvantage regardless of their information quality. But formatting without original data produced zero measurable citation gains in our experiment, and that result held consistently across nine pages covering different topic areas, content types, and competitive environments. A rewrite program built on structural improvements alone should not, on the basis of this data, be expected to move your AI citations.

There is a useful distinction often made between what SEO and AEO each reward: "SEO gets you found. AEO gets you quoted." That formulation is accurate, and it has a practical implication most teams miss - to be quoted, you must say something worth quoting. Formatting tells the engine your content is structured; original data tells the engine your content is worth extracting. The two are not in competition, but neither are they substitutes. One might say the format earns attention and the data earns citation, and in our experiment at least, attention without citation produced no return.

The investment calculus shifts substantially once one takes this seriously. Format-only rewrites are faster and cheaper - an experienced editor can process several in a day, and the changes are systematic enough to template. But if the result is zero citation return, the cost is not low; it is infinite per citation gained. Original data production is more labor-intensive, requiring either research synthesis, client data, or genuine first-person testing. But eight of eleven pages gaining citations within six weeks, at an average of 3.4 times the pre-rewrite citation count, represents a return that is difficult to set aside. A team that invests in both formatting and original data will, based on what we observed, substantially outperform a team that invests in format alone - not marginally, but by the difference between measurable gain and no measurable gain at all.

Before: Format-only markup (0 citations gained)

<h2>AEO Optimization Benefits</h2>
<p>Answer engine optimization can help your business appear in AI-generated
answers. By structuring your content clearly and covering relevant topics,
you improve your chances of being cited by ChatGPT and similar platforms.
AI search is growing rapidly, and businesses that optimize early will have
a competitive advantage.</p>

After: Format-plus-data markup (citations appeared within 23 days)

<h2>What response times correlate with AI citation gains after an AEO rewrite?</h2>
<p><strong>Pages that added a proprietary benchmark in our 20-page experiment
gained citations in a median of 23 days, compared to zero citation gains for
pages that received formatting changes only.</strong> Across the eight pages
that gained, average citation count rose 3.4x above pre-rewrite baseline.
The fastest result - nine days post-publish - came from a page that introduced
a specific response-time benchmark: <strong>average first-response time of
4.2 minutes across 340 customer-service implementations</strong>, a number
AI engines had no competing source for and pulled within the first crawl.</p>

<!-- FAQ schema markup for the section -->
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What response times correlate with AI citation gains?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Pages with proprietary benchmarks gained citations in a median of 23 days and saw 3.4x citation increase at six weeks."
    }
  }]
}
</script>

The structural difference is visible at a glance: a generic heading becomes a specific question, a vague claim becomes a measurable benchmark with sample context, and the FAQ schema makes the question-answer pair machine-readable. The formatting changes alone (question heading, strong tag) appeared on both versions - what distinguished the one that earned citations was the number that existed nowhere else.

What will matter most in AEO content over the next 12 to 24 months?

The experiment described here was run on pages in a competitive but not saturated topic category. The conditions will not hold indefinitely. As more brands learn the answer-first formatting protocol, structure will become less differentiating - AI engines will encounter it everywhere and the signal will decay toward commodity. What will remain differentiating, I think, is precisely the thing that cannot be replicated without doing the underlying work: original data with provenance.

There is a particular quality of information that AI engines, in their curious way of reading and attributing, appear to find most citable - something that one might call verifiable novelty, the combination of precision (a specific number with sample size and timeline) and uniqueness (the same number not appearing on seventeen other domains). As more content teams learn to add proprietary benchmarks and case study outcomes to their pages, the bar for what counts as distinctive original data will rise. A single percentage with context will eventually become common enough to lose its advantage. What will matter is the depth and rigor of the data: longitudinal tracking, survey-derived benchmarks, platform comparisons conducted from direct access rather than inference.

I would also expect the "freshness" dimension of original data to gain weight. AI engines are, in varying ways, sensitive to recency - a benchmark published in the past six months may be preferred over one published two years ago even if both are accurate. Content teams that build ongoing data production into their workflow (quarterly benchmarks, annual client surveys, iterative platform comparisons) will accumulate an advantage that teams doing one-time rewrites cannot close by formatting alone. The structural changes we described in this experiment are, in a certain sense, a floor - not a ceiling. Original data is where the ceiling keeps rising.

The experiment will not settle every question about AEO - it is, one must acknowledge, a twenty-page study run by a content team rather than a randomized controlled trial, and the margin of uncertainty is real. What it does settle, I think, is the specific question of whether format-only rewrites justify their investment as a citation-growth strategy: they do not, at least not on the evidence we have. The structural changes are worth making - they are a prerequisite for citation-readiness, and pages without them face a disadvantage that no amount of original data will fully overcome. But the investment that moves citations is the investment in knowing something original - in conducting the comparison, synthesizing the client data, or publishing the benchmark that exists nowhere else. That is, in a certain sense, the oldest editorial principle there is: say something worth repeating, and you will be repeated. AEO has simply made that principle measurable.

If you want to know where your current pages stand on citation readiness - and which ones have the structural and informational gaps our experiment identified - the AEO Rank audit breaks it down by page, by criterion, and by what would need to change to move the needle. It is, in my experience, a more useful starting point than a general content audit, because it measures what AI engines actually extract rather than what traditional SEO tools can see.