Siteimprove in AI search.

Executive summary

Siteimprove has rebranded around AEO and agentic content intelligence — and the third-party signals that matter are already in the index. The mechanics of AI citation have not caught up. AI engines select sources via two paths: grounded retrieval (the engine searches the web in the moment and cites what it finds) and ungrounded memory (the engine recommends from training data without searching). The grounded path is blocked at the firewall. The ungrounded path is anchored on a pre-2026 product description. Both paths fail at the same time, which is why an AEO vendor — Forrester Wave Leader Q4 2025, Gartner AEO Market Guide Representative Vendor 2026, G2 Spring 2026 Leader across all four product categories — shows up at 4% own-domain citation share when buyers ask AI engines to build accessibility shortlists.

94% of B2B buyers now use generative AI in their purchasing process, and 73% specifically use AI to build vendor shortlists. At 4% citation share, Siteimprove is structurally absent from the pre-contact phase of those journeys — the phase where 95% of vendor decisions are effectively made before the seller is ever contacted.

What's broken — three layers, all small fixes

The visibility gap is not one problem but three working together. Retrieval: Bingbot, MSNBot and Yandex all return HTTP 403 at the WAF — including on /robots.txt itself, which per RFC 9309 §2.3.1.3 means "do not crawl." ChatGPT and Microsoft Copilot are both Bing-fed; they cannot reach the site, so they fall back to training memory. Memory: a token-level perception scan finds zero mentions of AEO, agentic, or Siteimprove.ai across 37 alternative-sentence branches. The model still describes the company as a Digital Certainty Index for QA, SEO and Accessibility. That is the 2022 product. On-site: the homepage carries Organization + WebSite schema but no SoftwareApplication, Product, or FAQPage; the AEO product page at /platform/seo/aeo-visibility/ carries only template-level schema; a duplicate label appears under all four homepage product cards (the four pillars lose distinctness in the index); and the seven existing competitor comparison pages need calibration, schema, and three missing competitors (Deque, Conductor, TPGi) before AI engines will treat them as structured artifacts.

What's already working — don't rebuild it

The category authority is real and visibly weighted by AI engines. Forrester named Siteimprove a Wave Leader in Q4 2025 in the AEO category specifically. Gartner named it a Representative Vendor in the AEO Market Guide 2026. G2's Spring 2026 Leader designations cover Digital Governance, Digital Accessibility, Digital Analytics and SEO Tools — all four platform pillars. IAAP and W3C affiliations are present. AI bots are fully allowed at the server: GPTBot, ClaudeBot, Claude-SearchBot, Google-Extended, OAI-SearchBot and PerplexityBot all return 200 OK. The retrieval surface for AI is open. Enterprise customer references (Vodafone, BT, Cuisinart, and the case studies in the customer hub) are cited by AI engines today. The credibility substrate is in place; what's missing is the freshness layer catching up to it.

On the four-Ps model of AI presence — Presence, Positioning, Perception, Permanence — Siteimprove is strong on Presence (the brand is in training data and on review sites), weak on Positioning (the entity model anchors on the 2022 product), and at risk on Permanence (without fresh signals, current authority decays as models update on stale content). The work in §08 closes Positioning and stabilises Permanence; Presence is already there.

What to do — a 90-day plan

The full action list in §08 is light on dev resourcing and heavy on internal coordination. The 30-day foundation phase: unblock Bingbot at the WAF (1–2 days of web ops), ship JSON-LD schema across the homepage and platform pages (half a day of dev), fix the duplicate homepage label (five minutes), publish a curated llms.txt (under an hour), and refresh the thin Wikidata entity Q28127172 (two hours). The 60-day phase: extend the comparison hub by three competitor pages plus calibration of the existing seven (2–3 weeks of content), publish a definitive "How Siteimprove uses AI" page that the model can re-anchor on (two weeks), and publish a pricing range page that displaces TestParty's currently-indexed $15K–$50K+ claim (one week). The 90-day phase: sustained Reddit and G2 review-platform engagement to displace the stale third-party comparison content the model currently retrieves.

AI engines are presently re-deciding category authority, which makes the timing asymmetry structural. Domains that publish fresh authoritative signals during this 12-to-18-month window get cemented as the AI-cited reference; domains that don't have to displace hardened citations later. Cost of action now ≈ one cross-functional pod for a quarter. Cost of equivalent action in 18 months ≈ the same effort plus the displacement work to remove competitor citations that have settled into training data and review-platform indexes.

What this needs to move

The 30-day foundation needs 1–2 days of web-ops time to investigate and remove the WAF rule blocking Bing, half a day of front-end dev to ship JSON-LD schema, and editorial sign-off for the duplicate homepage label and the llms.txt. The 60-day phase needs content-team capacity for three new competitor comparison pages plus a recalibration of the existing seven, and editorial sign-off on the "How Siteimprove uses AI" page and the pricing range page. The 90-day phase needs sustained engagement on Reddit and G2. The single highest-friction approval is the pricing page; the rest are operational.

How we'll know it's working

Standard analytics will not capture this recovery. AI-driven referrals appear as direct or branded organic traffic in HubSpot and GSC; the engine that triggered the visit is not in the referrer chain. Three measurement layers run alongside the work: own-domain citation share on a fixed prompt set, tracked weekly via Siteimprove's own AI Visibility dashboard; self-reported attribution on demo-request forms with AI engines as named options (this captures up to 15× more AI-driven conversions than analytics alone); and branded search volume in Google Search Console as a proxy for AI-driven awareness lift. The M3 falsification target is own-domain citation share moving from 4% to 12%+ on the target prompt set.

Strategic finding worth surfacing

Beyond the mechanical gaps, every audited page exhibits a title/H1 split (§06.e): the <title> indexes against the category keyword while the <h1> reaches for an emotional value proposition, fragmenting the retrieval chunk that AI engines reward. This is not a strategic-retreat finding — the new AEO positioning is analyst-validated. It is page-level signal coherence, fixed in a week of editorial work on the top eight pages, ranked by inbound link concentration.

Audit scope and method

Three URLs reviewed in detail, plus three independent measurement passes across the public AI answer layer.

The lens is answer-engine optimization, not classic search rankings. Findings prioritise whether AI engines can find, parse and accurately represent Siteimprove — not whether the site ranks for a given keyword. Audit conducted April 28 – May 8, 2026.

Note on sample size: the 30-prompt sweep is directional, not statistical. It surfaces patterns that the bot test and the brand perception scan then explain mechanically. The three passes were designed to corroborate, not to substitute for one another.

Signals already working

Nine signals AI engines visibly weight, none of which need work. The audit assumes these stay in place.

Bing crawlers blocked at the server

For a vendor whose AEO product depends on AI visibility, this is the single most consequential finding in the audit. The block also extends to /robots.txt itself, which has standard-defined consequences.

A bot crawlability test on May 8 returned HTTP 403 for 22 of 147 bots — 19 of them are search-engine crawlers. The block is at the WAF or origin level, not in robots.txt. Direct curl with a Bingbot user-agent confirms 403 on both the homepage and on /robots.txt. With a GPTBot or ClaudeBot user-agent, the same URLs return 200.

// siteimprove.com — bot test results, May 8, 2026
GPTBot                  200    AI bots all allowed
ClaudeBot               200
Claude-SearchBot        200
Google-Extended         200
OAI-SearchBot           200
PerplexityBot           200

// 19 search-engine crawlers returning 403:
Bingbot Desktop         403    Bing variants — all blocked
Bingbot Mobile          403
BingPreview Desktop     403
BingPreview Mobile      403
MSNBot                  403
MSNBot-Media            403
YandexBot               403    plus Yandex variants
YandexImages            403
YandexVideo             403
… and 10 more search crawlers

// Direct curl confirms the block at the origin:
$ curl -I -A "Mozilla/5.0 (compatible; bingbot/2.0; +http://www.bing.com/bingbot.htm)" \
       https://www.siteimprove.com/
HTTP/2 403

$ curl -I -A "Mozilla/5.0 (compatible; bingbot/2.0; …)" \
       https://www.siteimprove.com/robots.txt
HTTP/2 403     ← critical: Bingbot can't even read robots.txt

Why this matters

ChatGPT search runs on Bing's index. Microsoft Copilot runs on Bing's index. If Bingbot can't crawl siteimprove.com, Bing's index goes stale — and the two AI engines that read Bing have nothing fresh to retrieve.

The robots.txt 403 makes the situation worse. RFC 9309 §2.3.1.3 specifies that when a crawler receives a 4xx response other than 404 on robots.txt, the standard interpretation is "do not crawl." Bingbot can't even check what's allowed because the rules file itself is forbidden. There's also a redirect-chain anomaly: under a browser user-agent, /robots.txt 301-redirects to /robots.txt/ (with trailing slash) and then to /Util/Errors/Error405/ — an internal error endpoint that 301s to itself. Whatever else this means operationally, it isn't a configuration any crawler should have to interpret.

This is the most likely mechanical explanation for why an AEO vendor — recognised by Forrester as a Leader in Q4 2025, recognised by Gartner in the AEO Market Guide 2026, with multiple G2 leader badges, with active product launches in March and April 2026 — shows up at 4% own-domain citation share. The site is producing fresh content. It's not reaching the index that ChatGPT and Copilot actually read.

Caveat: the test was run from a single IP. WAFs sometimes block by region or ASN. Recommend re-testing from a second region before stating the block as universal — but the pattern (every Bing/MSN variant blocked, no AI bots blocked, robots.txt clean) makes a per-IP false positive unlikely.

Stale citations and stale brand memory

Two layers of the same problem. The retrieval layer reaches third-party comparisons. The training layer remembers the 2022 product.

Citation share by source — "best accessibility platforms 2026"

Approximate share of source-domain citations across ~30 prompts on ChatGPT, Copilot, Gemini, Perplexity and Claude. Aggregated April–May 2026. Directional sample.

N = 30 prompts · 5 engines · own-domain share = 4% · 92% of citations come from competitor pages, third-party aggregators, or independent reviewers

Sources currently shaping the AI narrative

A representative sample of pages AI engines consistently cite — and the framing each one repeats.

Brand perception drift in training data

A token-level perception scan asked the model to describe Siteimprove, then expanded along 37 alternative branches. The result is consistent across every branch.

// brand perception scan — 37 generated branches, May 8, 2026
Recurring entities anchored:
  Digital Certainty Index (DCI)        — high confidence
  Quality Assurance / SEO / Accessibility — high confidence
  Compliance / WCAG                    — high confidence

Confidence on current positioning terms:
  "Analytics"                  19.8%   ← among the four named pillars
  "offers" (as primary verb)   11.63%
  "aimed" (purpose statement)  10.67%

Branches mentioning AEO, agentic, or Siteimprove.ai:  0 / 37

The model still describes Siteimprove as "a Digital Certainty Index for QA, SEO and Accessibility." Not one of the 37 branches mentions AEO, agentic content intelligence, or the Siteimprove.ai unified platform. That's the 2022 product, not the 2026 one. Under the chain in §01, this is exactly what would be expected: training-era memory plus blocked retrieval equals stale answers.

On-site technical issues

Four on-site gaps. None is the highest-leverage item in the report. All are easy to ship and visibly weighted by AI engines or by the credibility argument an AEO vendor has to make.

a. Schema on the homepage and AEO product page

Basic markup is in place; the platform-pillar gap is the real issue, especially on the AEO product page.

The homepage carries two JSON-LD blocks: WebSite with a SearchAction, and Organization with name, description, URL and a sameAs graph linking LinkedIn, Wikipedia, Facebook and X. That's a real foundation — it gives Google Knowledge Graph an entity anchor. But it stops there. The four platform pillars (Accessibility, Analytics, SEO/AEO, Content) carry no SoftwareApplication or Product markup. There is no FAQPage on the homepage or the AEO product page. The AEO product page itself, sitting at /platform/seo/aeo-visibility/, carries only the template-level WebSite/SearchAction — no Organization, no SoftwareApplication, no Product, no FAQPage.

// JSON-LD coverage across audited pages, May 9, 2026 (verified via curl)

                            Organization  WebSite  SoftwareApp  Product  FAQPage
homepage                      ✓            ✓           ✗          ✗       ✗ 
/platform/seo/aeo-visibility/    ✗            ✓           ✗          ✗       ✗ 
/why-siteimprove/competitor-comparison/  ✗        ✓           ✗          ✗       ✗ 
…/siteimprove-vs-silktide/    ✗            ✓           ✗          ✗       ✗ 

Two fixes. First: extend the homepage schema with SoftwareApplication markup for each of the four pillars (or one combined SoftwareApplication with sub-products), and add FAQPage wherever there's standing FAQ content. Second: ship full schema on the AEO product page — at minimum Organization, SoftwareApplication, and FAQPage. Direct lift on Gemini and Copilot via Knowledge Graph and Bing inheritance. Controlled tests show ChatGPT and Perplexity treating JSON-LD as flat text, so don't expect direct citation lift there — but for a vendor selling AEO, shipping an empty knowledge-graph signal on the product page is a story to avoid.

b. No llms.txt file

There is no current evidence that llms.txt is read by major AI vendors. Studies across 500+ sites show no measurable correlation with AI citations. John Mueller publicly stated Google does not use it. The argument for shipping it is not algorithmic — it's brand credibility.

$ curl -I https://siteimprove.com/llms.txt
HTTP/2 404
date: Fri, 08 May 2026 14:22:11 GMT
content-type: text/html; charset=UTF-8

Anthropic ships an llms.txt. Cloudflare, Vercel and Coinbase ship them. For an AEO vendor not to ship one is the kind of detail competitors will surface in a sales conversation. The fix is short: a curated map pointing to platform pages, the accessibility statement, the EAA Resource Center, case studies, and the glossary. Under one hour of work. Sell the optics, not the algorithm.

c. Duplicate label under all four product cards

"ACCESSIBILITY AGENTS HELP YOU REVIEW BEFORE YOU PUBLISH" appears beneath the accessibility, analytics, SEO, and content cards. AI crawlers ingest the same label four times and lose pillar distinctness. Verified via Google's own indexed snippet of the page.

// homepage — product cards section, rendered HTML
<div class="card">Accessibility</div>
  └─ "Accessibility agents help you review before you publish"
<div class="card">Analytics</div>
  └─ "Accessibility agents help you review before you publish"  ← duplicate
<div class="card">SEO</div>
  └─ "Accessibility agents help you review before you publish"  ← duplicate
<div class="card">Content</div>
  └─ "Accessibility agents help you review before you publish"  ← duplicate

Five-minute content fix. Outsized impact on how cleanly the four pillars get parsed — a cheap signal repair while the brand-perception drift in §05 is being addressed.

d. The comparison hub exists but is under-leveraged

Siteimprove already has a 7-page first-party comparison hub. It's substantive in places. It's also missing the schema, the calibration, and three of the competitors that AI engines actually cite.

The hub at /why-siteimprove/competitor-comparison/ covers Level Access, Silktide, Acquia, BrightEdge, SEMrush, Matomo, and GA4 — seven dedicated pages, all 200 OK. The Silktide page (sampled) carries a Forrester Wave Leader quote, a feature-by-feature Yes/No table across seven categories, two customer testimonials, and a concrete differentiation argument. That isn't thin content. The reason these pages don't show up in AI answers is not their absence — it's a stack of solvable issues:

1. The Bing block keeps them out of the index ChatGPT and Copilot read. Until §04 is resolved, no comparison content will reach those engines regardless of quality.
2. The pages carry only template-level WebSite/SearchAction schema. No Product, no Article, no ComparisonReview. AI engines parsing the page see "another page" rather than "a structured comparison artifact."
3. The Yes/No tables are uniformly tilted toward Siteimprove. Where both vendors offer the same capability, the cells read "Siteimprove Yes / [Competitor] Yes," but every category where there's a difference resolves in Siteimprove's favour. AI engines prefer comparison content that explicitly names where the competitor wins (engines learn to trust sources that calibrate honestly, which means honest framing is itself a citation-rate lever); the current pattern reads as marketing rather than honest comparison.
4. Three competitors that get cited heavily in AI answers are missing entirely. Deque (axe DevTools), Conductor, and TPGi do not have dedicated comparison pages. The existing Acquia page covers Acquia generally but doesn't surface for "Monsido alternatives" specifically — Acquia rebranded Monsido and the legacy positioning still drives queries.

The recommendation in §08 is therefore "extend and improve," not "build." Adding three competitor pages, shipping Product/ComparisonReview schema across all of them, and recalibrating the Yes/No tables to honestly name competitor strengths is two to three weeks of work, not the four to six weeks a greenfield hub would take.

e. Title and H1 disagree across the highest-traffic pages

A page-level signal-coherence problem, not a positioning problem. The category is analyst-validated (Forrester Wave Q4 2025; Gartner AEO Market Guide 2026). The pages themselves still hedge.

Every page sampled in the audit puts one phrase in the <title> and a different phrase in the H1. AI engines favour chunks where the query terms, the named source, and the supporting statistic concentrate together; when title and H1 push different value propositions on the same page, the retrieval surface fragments into two adjacent half-chunks rather than one strong one. Verified via direct HTML inspection on May 9, 2026.

// title vs. H1 across audited pages — verbatim

siteimprove.com/
  title  Agentic Content Intelligence — Siteimprove
  H1     Where accessibility meets performance

siteimprove.com/platform/
  title  Agentic Content Intelligence Platform — Siteimprove
  H1     Where accessibility meets performance

siteimprove.com/accessibility/
  title  Accessibility Digital Governance
  H1     From risk to reach with accessibility made simple

siteimprove.com/platform/seo/aeo-visibility/
  title  AEO Visibility — Siteimprove
  H1     [generic platform-pillar value prop, see §06a — no SoftwareApplication schema]

The pattern is consistent. The title indexes against the category keyword, the H1 reaches for an emotional value proposition, the lead paragraph splits the difference, and the schema (where present) anchors to the brand entity rather than the page topic. Each individual choice is defensible; together they produce a page that retrieves weakly on any one query.

Two clarifications matter, because the obvious read of this finding is "the new positioning is wrong," and that read is wrong. First, the AEO and agentic-content category is real and externally validated: Forrester's Wave designated Siteimprove a Leader in Q4 2025 specifically in this category, Gartner named the company a Representative Vendor in the AEO Market Guide 2026, and G2's Spring 2026 Leader badges cover the platform's four pillars. The category exists. Second, the finding here is page-level, not strategic — the editorial choice to translate "Agentic Content Intelligence" into "Where accessibility meets performance" at the H1 level is what splits the retrieval chunk. The fix is editorial coherence within the existing positioning, not a retreat from it.

The remediation is one week of editorial work. Pick the top eight pages by inbound link concentration. For each page, pick one query it should win. Rewrite the title, the H1, and the first 50 words of body copy so all three concentrate on that query, with one extractable statistic and one named source per page (the §08 ChatGPT quick-wins items 05 and 06 already cover the answer-first rewrite of the first 50 words — this is the same work, extended to title/H1 alignment). The Bing-block fix in §04 gets crawlers back into the index; this fix makes what they index actually retrievable on category-specific queries instead of brand-only ones.

AI engine landscape

Underneath the brand names, AI search runs on a handful of indexes. Investing in one signal often pays off across multiple engines. ChatGPT and Copilot both read Bing. Gemini and AI Overviews both read Google. Claude reads Brave Search. Perplexity runs its own crawler. As of February 2026, ChatGPT also runs an advertising layer that competitors can pay into.

What the ranking-factor research actually shows for B2B SaaS

The most extensive public study to date on LLM recommendation signals (OppAlerts, March 2026, covering 145 industries and 34,092 domains across more than 105,000 ChatGPT prompts) is worth reading carefully because its headline numbers are misleading when applied to any specific category. The all-industry table shows search engine signals and backlink authority as the strongest universal predictors, with Spearman correlations clustered between 0.20 and 0.24, while Reddit engagement, Wikipedia citations, Wikidata entries, Common Crawl coverage, and homepage keyword relevance all sit between 0.07 and 0.12. Those universal numbers average across very different industry patterns.

The per-industry breakdowns tell a different story for B2B SaaS categories that map closest to Siteimprove's positioning. Across SaaS verticals where the data is robust, Wikidata entity strength and SERP breadth consistently outperform the universal average by a factor of three to five. ERP software shows Wikidata at ρ=0.655. Healthcare practice-management software shows Wikidata at ρ=0.696. CRM software shows the closely related Wikipedia Citations signal at ρ=0.577. Customer support and contact-center software shows SE Outbound Links (a measure of SERP breadth that counts appearances across the linking surface of Google search results, not just rank) at ρ=0.547. Marketing automation shows Wikidata at ρ=0.383. These are dominant-tier correlations, not the modest universal ρ=0.12 figure that the all-industry headline would suggest.

Two caveats matter for interpretation. The first is that these are correlation measurements, not causal. R-squared values across all signals are modest, with the strongest universal predictor explaining only 5.8% of the variance in LLM recommendation scores, which means the measured factors leave most of what's happening unexplained. The recommendations the study supports are defensible bets, not deterministic wins. The second is that the study does not measure schema markup, llms.txt, content recency, citation patterns, or earned-media-vs-owned distribution as separate signals. The audit's recommendations in those areas rest on different evidence and are not refuted by what this study does or does not find. What changes in this audit as a result of the OppAlerts work: the Wikidata recommendation gains stronger justification through the B2B SaaS-specific correlations, and a new SERP-breadth recommendation joins the action list because Google search visibility across the full query surface looks more directly predictive of ChatGPT recommendations than the audit previously treated it.

Priorities, sequencing, and 90-day outlook

Unblock Bing, ship schema, build the comparison hub. Everything else is incremental.

Full action list, in priority order

The three above plus six more. All nine fit comfortably inside one quarter.

1. Unblock Bingbot at the WAF/origin level

   Pull WAF rule list, identify what's matching Bing's IPs or user-agents, allowlist Bing IP ranges, submit sitemap via IndexNow. Recovery in Bing typically 7–14 days. Until this is done, every other AEO investment for ChatGPT and Copilot is throttled.

   1–2 days

2. Ship JSON-LD on the homepage and key product pages

   Organization, SoftwareApplication, FAQ at minimum. Product schema on each platform sub-page. Closes the credibility gap given what Siteimprove sells. Direct lift on Gemini and Copilot via Knowledge Graph and Bing inheritance.

   ½ day

3. Extend the existing comparison hub

   Seven pages already live at /why-siteimprove/competitor-comparison/ covering Level Access, Silktide, Acquia, BrightEdge, SEMrush, Matomo, GA4. Three high-leverage adds: Deque (axe DevTools), Conductor, TPGi. Ship Product and ComparisonReview schema across all ten pages. Recalibrate every Yes/No table to honestly name where the competitor wins (the current pattern reads as marketing, not comparison). Worth a separate audit: why pages that exist aren't getting cited — the Bing block in §04 explains most of it, schema explains the rest.

   2–3 wk

4. Build Google SERP breadth across the full query surface, not just brand queries

   The play is to invest in topical content that captures Google SERP appearances across the full set of queries a buyer might run while evaluating accessibility, AEO, content governance, and analytics tooling. The OppAlerts ranking-factor evidence supports this directly. Across B2B SaaS verticals, a signal called SE Outbound Links — which counts how many distinct Google search result pages a domain appears in, weighted by the linking surface of those SERPs — correlates with ChatGPT recommendations at ρ=0.45 to ρ=0.67 in categories like customer-support software, MarTech, B2B marketing data providers, and consumer banking. The mechanism this implies is that Google SERP breadth across many adjacent query types (not just brand queries, but informational queries, comparison queries, and problem-statement queries) has direct predictive power for which domains ChatGPT recommends. Concretely, this means building out glossary pages for accessibility, AEO, and digital governance terms, problem-statement landing pages such as "how to audit a website for WCAG 2.2 compliance," "how to measure AI search visibility," and "how to detect content drift across a 10,000-page site," and comparison and alternatives queries beyond named competitors. This is a content investment that compounds across both Google and AI search rather than serving only one surface. Measurement is straightforward: track distinct query SERP appearances on a fixed prompt set monthly, alongside the citation-share tracking already in plan.

   8–12 wk

5. Publish "How Siteimprove uses AI" — as a knowledge-graph repair

   Treat this not as a sentiment fix but as the authoritative source the AI's brand model can re-anchor on. Concrete model and capability detail. Named architecture. Public docs links. The 2024 "no AI/ML" claim is still indexed and still cited; the brand perception scan shows the model still describes Siteimprove as the 2022 product. This page is what overwrites both.

   2 wk

6. Fix the duplicate label on the homepage

   Five-minute content fix with outsized impact on how clearly the four pillars get parsed by AI crawlers.

   5 min

7. Update the Wikidata entity (Q28127172) — the highest-impact ungrounded-memory lever

   Wikidata is one of the most efficient ungrounded-mention interventions available. It feeds Google Knowledge Graph directly, has fewer conflict-of-interest restrictions than Wikipedia, and shows up as a primary source across model updates. The OppAlerts March 2026 ranking-factor research makes the case substantially stronger than the original framing suggested. While the universal Wikidata correlation with ChatGPT recommendations is modest at ρ=0.120, the per-industry data for B2B SaaS verticals runs three to five times higher. ERP software shows Wikidata at ρ=0.655, healthcare practice-management software at ρ=0.696, healthcare IT at ρ=0.575, and marketing automation at ρ=0.383, with CRM software's closely related Wikipedia Citations signal at ρ=0.577. Siteimprove's category sits at the intersection of digital governance, accessibility, AEO, and analytics, and does not appear directly in the study, but maps closest to these high-Wikidata B2B SaaS patterns. The current Wikidata entry is thin (instance-of statements only, no current CEO, no Forrester Wave citation, no product references). A two-hour edit adds current CEO Nayaki Nayyar, Forrester Wave Q4 2025 Leader status, the four platform pillars, the Siteimprove.ai unified-platform positioning, and current customer references with sources. For Wikipedia itself, the safer path is to encourage independent journalists and analysts to source updates, since direct edits get reverted as promotional.

   2 hr

8. Ship llms.txt as credibility, not as ranking

   Curated map pointing to platform pages, accessibility statement, EAA Resource Center, case studies, glossary. Low effort, signals seriousness about AEO. Frame internally as brand theater for a category Siteimprove sells into — not as an algorithmic signal.

   < 1 hr

9. Seed Reddit and review platforms with current customer voices

   Industry citation-pattern data is unambiguous. Muck Rack's December 2025 analysis of more than one million AI citations found that 82% come from earned media (review platforms, forums, third-party mentions) and 94% from non-paid sources overall — a University of Toronto controlled study confirmed the bias is structural, not platform-specific. The current 4% own-domain share reflects exactly this distribution, and the highest-leverage way to shift it is to add weight to the earned-media half. Reddit citations appear consistently in AI answers about accessibility tooling. The relevant subreddits are r/accessibility, r/SEO, r/webdev, r/marketing, and r/bigseo. The format that performs is AMAs, technical Q&A, and real customer threads, not promotional posts. G2 review velocity matters in parallel: G2 is the most-cited software review platform across ChatGPT, Perplexity, and Google AI Overviews (Radix 2026). The two together also work as a defensive moat against the "$15K–$50K+" pricing claim and the "no AI/ML" claim that are currently being cited — fresh customer voices on neutral platforms displace older third-party framing.

   ongoing

10. Publish a credible pricing page (or pricing range)

    TestParty's "$15K–$50K+" claim is now indexed and being repeated by ChatGPT and Perplexity. A vague rebuttal won't displace a specific number. Even an honest "starts at $X for mid-market, custom enterprise" page gives AI engines a first-party number to anchor on.

    1 wk

ChatGPT-specific quick wins

ChatGPT search runs on Bing's index, so most of these double up as Copilot wins. Items 01 and 02 are blocked by §04 — they need the Bing unblock to compound.

90-day outlook

What to measure (and why standard analytics will miss most of it)

This work will look invisible in default analytics dashboards. AI-driven referrals show up as direct traffic or branded organic; the AI engine that triggered the visit is not in the referrer chain. Vault GTM Research has measured a 90% gap between what software attribution credits and what self-reported attribution credits, meaning standard analytics captures roughly one in ten AI-driven conversions. The quality offset is also significant: a March 2026 synthesis of six independent studies (Loganix, drawing on Averi's 680-million-citation analysis) found AI search traffic converts at 14.2% versus Google organic at 2.8%, a 5.1x advantage. The implication is that volume metrics will not show the win here; quality metrics will. The reporting layer to set up alongside the work above includes self-reported attribution on demo-request forms ("How did you first hear about Siteimprove" with AI engines as named options), branded search volume trends in Google Search Console (a clean proxy for AI-driven awareness lift), own-domain AI citation share on a fixed prompt set tracked weekly, conversion rate by acquisition channel (rather than session counts, which will mislead), and revenue per visitor by source. Siteimprove.ai's own AI Visibility dashboard handles the citation-share tracking natively. The rest sits in HubSpot, GSC and the analytics layer Siteimprove already operates.

State of AEO 2026 — research outline

Priority 09 in §08 names "publish original research" as a high-leverage move in one line. This appendix elaborates that line into a working brief — what the report would publish, why each chapter earns citations, what the production effort looks like, and how to distribute the asset for maximum AI-engine citation lift.

What the report would publish — seven chapters

Each chapter anchored on a single hero finding that's quotable and extractable. The hero finding is what journalists copy-paste; the supporting findings are what AI engines pull as evidence. Every chart is designed to be embeddable with attribution back to a deep-linked anchor on the report hub.

The linkable asset stack

The report is the bait; the citation machinery is the surface around it. Six concurrent assets, ordered by leverage.

1. The hub page at /state-of-aeo-2026/

   Deep-anchor link per chart, per finding, per industry. Built so a journalist citing a single stat can deep-link to its exact source. The hub is where every press mention and academic citation eventually points.

   infra

2. The ungated press PDF — no lead-gen gate

   Direct download for journalists and academic researchers. Lead-gen gating fragments academic citations and is the largest single mistake B2B SaaS makes with research reports. Marketing's lead-capture goal should be served by a separate "executive summary deck" gated asset, not the report itself.

   design

3. Seven per-industry mini-reports at /state-of-aeo-2026/[industry]/

   Templated landing pages that pull only that industry's slice. Double as SEO money pages for industry queries Siteimprove already targets in §03's solutions hub. Each one becomes a citation magnet for its trade press.

   2 wk

4. Embeddable charts with attribution

   Each major chart has an iframe embed code that includes attribution back to the hub. This is the mechanism by which WebAIM's Million accumulates thousands of citations year after year — embed velocity compounds linearly across publishers and academic-paper appendices.

   infra

5. Press kit and four-week analyst pre-briefing window

   Five pre-written quotable stats, a CMO-level briefing deck, two named expert sources from inside Siteimprove available for reporter calls. Gartner, Forrester and IDC analysts on the AEO and DXP coverage list get a four-week pre-publication window in exchange for inclusion in their next research note. The Forrester and Gartner recognitions named in §03 are the relationships that make this work.

   2 wk

6. Quarterly refresh on one dimension

   The report's data is updated once a quarter on a single chapter (e.g., Chapter 02 industry leaderboard refresh in Q3, Chapter 04 crawler-blocking refresh in Q4). Keeps the asset current in AI-engine retrieval indices and earns net-new citations across the year, not just at launch. Costs roughly one-fifth the effort of the annual flagship.

   quarterly

Production scope

Anti-patterns to avoid

1. The self-serving leaderboard. If the data shows Siteimprove customers outperform on a given metric, footnote it transparently. If it doesn't, do not engineer the chart to make them look better. One detected case of methodology cherry-picking destroys the asset for a decade.
2. The vendor-PDF aesthetic. The reference points are McKinsey Global Institute, Cloudflare Radar, Datadog State of DevOps — not gated SaaS ebooks. Design and writing budget reflect this.
3. Burying the methodology. See chapter ∎ above. Treat it as a chapter, not a footnote.
4. Skipping the embed code. If charts cannot be embedded with attribution, citation count drops by an order of magnitude. The compounding mechanism breaks.
5. One-shot release with no refresh cadence. A single annual report decays in AI retrieval indices. Quarterly micro-refreshes are what keep the asset current.