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42% of Marketing Tool Pages Carry No Structured Data At All

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Drafted with AI assistance and edited by the Bach.ai team. How we write

How much schema markup do marketing tool websites actually have?

Across 132 pages measured in September 2026, 42.4% carried no structured data whatsoever. Among pages that had some, the frequent types were CMS defaults — ImageObject at 43.9% and Organization at 39.4% — while FAQPage reached only 6.1% and HowTo 0.8%.

For the surrounding account decisions, compare the full benchmark and the method.

In short

42.4% of pages carry no schema.org markup at all. The check is weighted 13 — the heaviest in the benchmark — because structured data is the one signal that tells a parser what a page is rather than making it infer.

What the category actually emits

Share of all 132 sampled pages carrying each type:

Type Share What it is usually
ImageObject 43.9% CMS default, attached to media
Organization 39.4% Site-wide, from a theme setting
WebSite 38.6% Site-wide, from a theme setting
Person 34.1% Author box
BlogPosting 29.5% Blog template
ContactPoint 27.3% Part of the Organization block
WebPage 23.5% Page template
BreadcrumbList 18.9% Navigation template
Product 9.1% Pricing pages
FAQPage 6.1% Hand-added
Article 2.3% Hand-added
HowTo 0.8% Hand-added

The pattern is unmistakable. Everything above 18% arrives automatically from a theme or CMS. Everything below 10% has to be deliberately added by somebody. The category has whatever schema its CMS gave it and almost nothing else.

Why FAQPage at 6.1% is the interesting number

FAQPage describes a set of questions with answers attached — which is structurally identical to what an assistant is assembling when it answers a query. It is the single most directly useful type for this purpose, and 93.9% of the category does not emit it.

Meanwhile faq_sections — the check that asks whether the page has question-shaped headings with answers beneath them, regardless of markup — passes on 37.9%. So roughly six times as many pages have FAQ content as have FAQ markup. Those pages have already done the expensive part, which is writing good question-answer pairs, and skipped the cheap part, which is emitting a JSON-LD block describing them.

That gap is the most straightforward win in the entire benchmark: no new content, no new pages, one template change that upgrades every post that already has an FAQ section.

The ordering that actually matters

If you are starting from nothing, the useful order is:

  1. BlogPosting or Article with a real datePublished and dateModified. This is what makes the freshness checks work — and only 40.9% of the category passes freshness signals at all.
  2. FAQPage on every page that already has question-shaped headings.
  3. BreadcrumbList, which tells a parser where the page sits in the site’s structure.
  4. Organization with a named author or Person — the expert-attribution check passes on 81.1% of the category, so this is one of the few places most sites are already fine.

Types five onward are refinement. The first two carry most of the value.

The failure mode to check for

Emitted schema that disagrees with the visible page is worse than no schema. The common version: a dateModified that updates on every deploy while the body has not changed since 2023. Our benchmark separates these deliberately — 40.9% pass freshness_signals and 3.0% pass content_freshness_depth, the check that asks whether the date is attached to anything that actually changed. A thirteenfold gap between having a date and the date meaning something.

Interpretation boundary

We read the schema present in the rendered DOM, so client-injected markup is counted. We did not validate every block against schema.org’s specification — a malformed FAQPage counts as present here and may still be ignored by a consumer. Read these figures as an upper bound on coverage, and validate your own markup with a dedicated tool before assuming it works.

Can software help?

Bach.ai audits your connected Meta account, estimates the revenue impact of what it finds, and proposes specific fixes. It applies a change only after you approve it. Think of it as an automated audit layer that surfaces issues and proposed fixes for your review — not a replacement for your team’s judgment, and creative production is not its core job, though the Pro and Agency plans can generate a limited number of variants.

FAQ

What percentage of marketing sites have no schema markup?

42.4% of the 132 pages we measured in September 2026 carried none at all. Schema is the heaviest-weighted check in our benchmark because it tells a parser what a page is rather than making it infer.

FAQPage, and only 6.1% of pages emit it. It describes questions with answers attached, which is structurally what an assistant assembles when answering a query.

Why do so many pages have FAQ content but no FAQ markup?

Because the content is editorial work and the markup is a template change, and the two sit with different people. 37.9% of pages have question-shaped headings; 6.1% emit FAQPage. That six-fold gap is the cheapest available win.

Is having a last-updated date enough?

No. 40.9% of pages carry a freshness signal but only 3.0% pass the deeper check asking whether the date is attached to content that actually changed. A dateModified that moves on every deploy is a signal in form only.

Method and sources

“Across 132 pages measured in September 2026, 42.4% carried no structured data whatsoever.”

Source: the Bach.ai AI Extractability Benchmark, run 2026-09-22. Sample: 132 pages returning HTTP 200, drawn from the published sitemaps of 12 advertising and analytics tools, plus our own 392 published posts. Every page was rendered in a headless browser and scored by the same 17-check engine the product runs against customer sites, so each figure is reproducible against the live web rather than asserted. Read the per-check rates as an industry signal, not a precise ranking of any one vendor: between 3 and 12 pages were sampled per tool, spread across the whole sitemap rather than drawn from the newest posts. The method is written up in full at how we measure AI extractability.

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