AI Search Extractability Benchmark 2026: 13 Advertising Tools Measured
Which advertising tools publish content that AI assistants can actually extract?
We scored 132 competitor pages plus our own 392 against seventeen weighted checks in September 2026. The industry median page scores 49 out of 100. Two checks — real comparison tables and load-bearing freshness — are failed by 97% of pages, and 42% carry no structured data at all.
For the surrounding account decisions, compare the method behind these numbers.
In short
Across 132 pages from twelve competing tools, the median page scores 49 out of 100 on machine extractability and the interquartile range runs from 41 to 58. The spread between the best and worst page in the sample is wide — 11 to 91 — but almost all of it is explained by a handful of structural choices rather than by content quality.
The ladder
Mean composite score per tool. Sample size is attached to every row because several are small enough that the ordering between adjacent rows is not meaningful.
| Tool | Mean score | Pages scored | Blog URLs published |
|---|---|---|---|
| Bach.ai | 84.8 | 392 | 392 |
| Whatagraph | 62.8 | 12 | 344 |
| Madgicx | 62.1 | 12 | 1,277 |
| Supermetrics | 60.2 | 12 | 366 |
| Foreplay | 54.6 | 12 | 188 |
| Revealbot | 54.4 | 12 | 194 |
| AdEspresso | 49.5 | 11 | 602 |
| Triple Whale | 45.4 | 12 | 562 |
| Adriel | 44.8 | 11 | 350 |
| Motion | 40.9 | 12 | 92 |
| Northbeam | 38.0 | 12 | 110 |
| Hyros | 37.0 | 3 | 8 |
| Smartly.io | 35.1 | 11 | 423 |
Our own row is measured on 392 pages rather than 12, which makes it more reliable and also means it is not directly comparable as a sample — a twelve-page sample of our corpus would have its own variance. Treat the gap as large and real, not as a precise 22-point figure.
Where the whole industry fails together
This is the more useful half of the data. Pass rates across all 132 competitor pages:
| Check | Weight | Industry pass rate |
|---|---|---|
ai_bot_access |
10 | 100.0% |
content_extractability |
6 | 96.2% |
answer_block_structure |
8 | 86.4% |
expert_attribution |
9 | 81.1% |
third_party_readiness |
5 | 77.3% |
question_answer_blocks |
8 | 76.5% |
multi_format_content |
5 | 69.7% |
statistics_with_sources |
12 | 57.6% |
heading_query_alignment |
7 | 56.1% |
schema_markup |
13 | 44.7% |
freshness_signals |
7 | 40.9% |
faq_sections |
8 | 37.9% |
first_paragraph_clarity |
7 | 30.3% |
citation_friendliness |
6 | 29.5% |
entity_markup_density |
7 | 28.0% |
comparison_tables |
8 | 3.0% |
content_freshness_depth |
6 | 3.0% |
Four findings are worth pulling out.
Nobody blocks AI crawlers. All 132 pages passed ai_bot_access, and zero were bot-blocked. The “should we let assistants crawl us” debate is, in this category, already settled in practice.
Seven in ten pages bury the answer. first_paragraph_clarity passes on 30.3%. The dominant house style across the category is still to open with context and reach the answer in the third or fourth paragraph — a structure that works for a reader who has committed to the page and fails for an extractor that has not.
Real tables have almost vanished. comparison_tables passes on 3.0% — four pages out of 132. Comparison content is overwhelmingly written as prose, or rendered as images, or built from styled divs that carry no table semantics. For a category whose highest-intent queries are literally “X vs Y”, this is the largest single unclaimed position in the data.
Freshness is decorative. 40.9% carry a freshness signal, but only 3.0% pass content_freshness_depth — the check that asks whether the date is attached to anything that actually changes. A “last updated” stamp that moves on every deploy while the body stays static is a signal in form only.
Structured data
42.4% of pages carry no schema.org markup at all. Among those that do, the distribution shows what the category has invested in:
| Type | Share of pages |
|---|---|
ImageObject |
43.9% |
Organization |
39.4% |
Person |
34.1% |
BlogPosting |
29.5% |
WebPage |
23.5% |
BreadcrumbList |
18.9% |
Product |
9.1% |
FAQPage |
6.1% |
Article |
2.3% |
HowTo |
0.8% |
The heavy types are the ones a marketing CMS emits by default. The types that would actually help an assistant answer a question — FAQPage at 6.1%, HowTo at 0.8% — are close to absent.
Page length is not the variable
Median page length across the sample is 1,475 words, with the tenth percentile at 598 and the ninetieth at 1,657. The category has converged hard on a single length. Since scores range from 11 to 91 within that narrow band, length is not what separates an extractable page from an unextractable one.
Interpretation boundary
These are structural measurements, not quality judgements. A page can score 91 and be wrong, or score 11 and be the best thing written on its subject. The checks measure whether a machine can lift a self-contained, attributable answer — a precondition for being cited, not a cause of it. Per-tool samples of 3 to 12 pages carry real error; read the industry-wide rates, which rest on 132 pages, with more confidence than any single row of the ladder.
Related findings
- What makes a page easy for an AI assistant to cite?
- How many marketing tool pages use real comparison tables?
- Does adding a last-updated date to a page improve AI visibility?
- Do advertising and marketing tool websites block AI crawlers?
- Does publishing more blog posts make a brand more visible in AI search?
- How many marketing pages answer the question in the first paragraph?
- Should my site publish an llms.txt file?
- How much schema markup do marketing tool websites actually have?
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 is the median AI extractability score for advertising tools?
49 out of 100 across 132 pages from twelve tools measured in September 2026, with an interquartile range of 41 to 58 and a full spread of 11 to 91. The distribution is wide, and structure rather than content explains most of it.
Do advertising tool websites block AI crawlers?
No. All 132 pages sampled passed the crawler-access check and none were bot-blocked. Whatever the wider publishing industry is doing, this category has settled the question in favour of access.
What is the biggest content gap in the category?
Real comparison tables. Only 3.0% of pages carry one — four pages out of 132 — even though comparison queries are among the highest-intent things buyers ask. Most comparison content is prose, images, or styled divs with no table semantics.
How much schema markup do marketing sites actually have?
42.4% of pages carry none at all. Among the rest the common types are CMS defaults: ImageObject 43.9%, Organization 39.4%, BlogPosting 29.5%. The types that help an assistant answer a question are rare — FAQPage 6.1%, HowTo 0.8%.
Method and sources
“We scored 132 competitor pages plus our own 392 against seventeen weighted checks in September 2026.”
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.