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State of the Industry · First-Party Data · Q2 2026

The State of AI Shopping Citations — 2026

First-party Q2 2026 baseline from the 1Digital® Workspace AI Visibility Monitor. Five engines, one prompt panel, 28 parsed responses — and an 86-percentage-point spread in brand-mention rate between the highest and lowest engine on the same questions.

No vendor whitepaper. No third-party scrape. Production telemetry from our own Workspace running the same monitor we ship into client engagements. The numbers below are what the engines actually do, captured at parse time, aggregated to the engine level. Brand-level findings stay private; engine-level posture is public.

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5
AI Engines Monitored
28
Parsed Responses (Pilot Window)
86 pts
Engine-to-Engine Spread
Weekly
Run Cadence

TL;DR

  • Five-engine mention-rate spread of 86 percentage points. Perplexity mentions monitored brands in 86% of successful answers; Google AI Overviews in 0%. The same prompt run through the same panel produces fundamentally different brand surface area depending on the engine — meaning "AI SEO" is not one optimization problem, it's five.
  • Google AI Overviews triggered on 100% of pilot prompts but named zero brands. AI Overviews behaved as a category-answer surface, not a recommendation surface, across the entire window. Brands chasing named AI-Overview citations should reset that expectation; the win condition is owning the category-defining answer block that AI Overviews paraphrases.
  • Perplexity is the lowest-cost, highest-yield engine in the panel. $0.022 per response and an 86% brand-mention rate make Perplexity the most attributable AI surface for brands optimizing for direct citation. The transparent citation behavior also makes it the easiest to instrument and prove ROI against.
  • Gemini's average first-mention position is 4,380 characters into the answer — roughly 3× deeper than the other chat engines. When Gemini does name a brand, it does so late, after extensive category generalities. Brands optimizing for Gemini need to win the late-paragraph entity slot, not the headline.
  • Cross-engine mention agreement is partial, not universal. On the prompts run through every engine, no brand from the monitored panel was mentioned by all five providers; on most prompts, no panel brand was mentioned by any of the five. This is the strongest possible evidence that single-engine optimization is leaving distribution on the table.
  • Claude's elevated error rate (web-search timeouts on 63% of pilot calls) is itself a finding. The engines do not have the same operational reliability — and reliability matters for measurement. Brands tracking citation share across all five engines need to account for engine-side failure modes when reading week-over-week deltas.

Reading guide: Aggregates are computed against the parsed-response table of the Workspace AI Visibility Monitor for the Q2 2026 pilot window. Mention rate is computed over successful (non-error, non-timeout) responses only. The pairwise overlap section uses prompts that both engines successfully ran in the same window. See the methodology page for the full schema, parsing rules, and entity-matching logic.

What this report measures

Four Definitions, Read Carefully

Citation rate

Percentage of successful provider responses where one or more URLs from the monitored brand's domain set appears in the engine's source list. This is the cleanest, most attributable signal — when an engine cites your domain, you can model the downstream click and the brand-recall lift directly.

Mention rate

Percentage of successful responses where the monitored brand name (or any of its registered aliases) appears in the answer text, regardless of whether the engine also linked to it. Mention is a weaker signal than citation but a far broader one — many engines name brands in narrative form without sourcing them.

Answer-presence (first-mention position)

Character offset of the first mention inside the answer text. Position correlates with reader attention: a brand named in the first sentence behaves differently in downstream conversion than a brand named in the closing paragraph. The Workspace logs first-position per mention; this report aggregates them as an average per engine.

Discovered brand surfacing

Brands and domains that appear in answers but are not on the monitored panel's subject or competitor lists. The Workspace flags these for human review; the categorical aggregates here are the only thing surfaced — no names, no domains. This is the engine's view of the competitive set, distinct from what the brand declared.

These definitions match the schema documented at /methodology/citation-share-monitoring. The Workspace's parsing layer applies them identically across all five engines so per-engine numbers are directly comparable.

The five engines, one at a time

Provider-by-Provider Breakdown

Ranked by Q2 2026 brand-mention rate against the pilot panel. Each engine's behavior, latency, cost, and posture is summarized below. The pattern — Perplexity at the top, Google AI Overviews at the bottom, three chat engines in between — is the most useful single-glance read of the AI search market a brand can have.

#1 by mention rate

Perplexity Sonar

Model: sonar-pro

86%Mention rate
10.6sLatency
$0.022 / responseCost

Posture in the panel: Highest mention-rate of any engine on the pilot panel — and the most transparent citation behavior. Perplexity is the surface where brand answers most reliably get named.

Observed behavior: Returns 5–8 named citations per answer on average, surfaces brand domains directly in-line, and reproduces brand language from cited pages with high fidelity. Best engine in the panel for brands that want measurable, attributable AI traffic.

Citations per cited answer: 5–8 · Pilot-window error rate: 0%

#2 by mention rate

OpenAI ChatGPT

Model: gpt-4o

40%Mention rate
12.8sLatency
$0.093 / responseCost

Posture in the panel: Middle of the pack on mention rate. The widest distribution — what wins on ChatGPT moves the most downstream traffic — but the engine returned 0 inline citations across the pilot window with browsing-mode parsing.

Observed behavior: Mentions brands in narrative form ("the X-style fit from brands like A and B") more often than as discrete sources. The implication: optimizing for ChatGPT means optimizing for paraphrase recall, not for footnote-style citation density.

Citations per cited answer: 2–4 (browsing on) · Pilot-window error rate: 29% (2 of 7 calls)

#3 by mention rate

Anthropic Claude

Model: claude-sonnet-4-6

33%Mention rate
36.9sLatency
$0.099 / responseCost

Posture in the panel: Mid-tier on mention rate but the slowest engine in the panel — and the highest error rate. Claude with web-search enabled remains a more deliberate, longer-form citation surface than the conversational competition.

Observed behavior: When web-search succeeds, Claude tends to cite primary-source content (manufacturer pages, original specifications) over aggregator content. The training-data-only mode shows meaningfully lower brand recall.

Citations per cited answer: 2–4 (when web-search invoked) · Pilot-window error rate: 63% (5 of 8 calls) in pilot window — under investigation; primarily web-search timeouts

#4 by mention rate

Google Gemini

Model: gemini-2.5-pro

17%Mention rate
32.0sLatency
$0.041 / responseCost

Posture in the panel: Lowest brand-mention rate of any chat-based engine in the pilot. Gemini's first-mention character position averaged 4,380 characters into the answer — roughly 3× deeper than the other chat engines — meaning when brands appear, they appear late.

Observed behavior: Heavy on category-level generalities ("look for premium materials, check warranty") before naming brands. Brands need late-paragraph entity surfacing — generic intro, named-brand body — to win mentions here.

Citations per cited answer: 1–3 · Pilot-window error rate: 25% (2 of 8 calls)

#5 by mention rate

Google AI Overviews

Model: serpapi-google

0%Mention rate
1.5sLatency
$0.015 / responseCost

Posture in the panel: 0% brand-mention rate across the pilot window. Google AI Overviews triggered for every prompt in this window (no_ai_overview = 0) but the returned Overviews were entirely category-level — no named brands surfaced in any answer.

Observed behavior: AI Overviews in the shopping-intent pilot acts more like a featured-snippet evolution than a recommendation engine: it answers the category question, not the brand question. The optimization play is owning the category-defining answer block, not chasing per-brand mentions inside an AI Overview that doesn't name brands.

Citations per cited answer: varies by query trigger · Pilot-window error rate: 0%

Calibration baseline · 1Digital® on the panel

How 1Digital® Stacks Up Against the Field

On May 17, 2026 we extended the Workspace prompt panel with 6 neutral agency-comparison queries — “best Shopify Plus SEO agencies,” “top BigCommerce SEO consultants for enterprise brands,” “best Magento Adobe Commerce SEO agency,” and three more in the same shape. We ran them through all five engines and measured 1Digital®'s subject mention rate against the average mention rate of 7 actively-monitored peer SEO agencies in the Workspace cohort. Same prompts, same engines, same window. Peer agencies anonymized.

Overall advantage on neutral agency-comparison prompts

1.87×

More brand surface area across AI engines than the average peer SEO agency, on neutral comparison prompts. 1Digital® averaged a 40% mention rate across the four brand-surfacing engines; the 7-agency peer panel averaged 21.4%.

Engine
Mention rate · subject vs peer average
Multiplier

Anthropic Claude

claude-sonnet-4-6

1Digital®
67%
Peer avg
17%
4.00×
Largest gap in the panel. Claude named 1Digital® on 4 of 6 agency-comparison prompts; the average peer agency surfaced on roughly 1 of 6. Claude's long-form synthesis posture rewards agencies with deeper case-study and methodology content over thinner directory listings.

Google Gemini

gemini-2.5-pro

1Digital®
50%
Peer avg
21%
2.33×
Strong 2.3× gap. Gemini surfaced 1Digital® on half the prompts (3 of 6) at an average first-mention position of ~1,960 characters — late-paragraph entity placement, consistent with the broader Q2 finding that Gemini names brands deep in its answers.

OpenAI ChatGPT

gpt-4o

1Digital®
50%
Peer avg
40%
1.24×
Marginal advantage. ChatGPT mentioned 1Digital® on 3 of 6 prompts but also mentioned the top peer agency on 5 of 6 — the engine surfaces multiple agency names per answer, so the win condition here is being included in the list, not being the sole answer.

Perplexity Sonar

sonar-pro

1Digital®
33%
Peer avg
29%
1.17×
Roughly at parity with peer average. Perplexity's citation-heavy posture spreads brand surface across many agency names per answer; 1Digital® appears on 2 of 6 prompts, only modestly ahead of the peer mean. The agency-comparison vertical is more competitive on Perplexity than on the chat surfaces.

Google AI Overviews

serpapi-google

1Digital®
0%
Peer avg
0%
—
AI Overviews returned zero brand mentions on every agency-comparison prompt — neither 1Digital® nor any peer agency surfaced. Consistent with the Q2 pilot finding that AI Overviews behaves as a category-answer surface, not a brand-recommendation surface, on shopping-adjacent verticals.

Reading the gap commercially: the engines do not surface agencies uniformly. Anthropic Claude and Google Gemini both produce the long-form synthesis answers most likely to appear on enterprise vendor-evaluation queries, and on those surfaces 1Digital®'s mention rate is materially higher than the peer mean — 4× on Claude, 2.3× on Gemini. ChatGPT and Perplexity surface multiple agency names per answer, so the win condition is being included in the list (which 1Digital®is, on half of ChatGPT's and a third of Perplexity's prompts), not being the sole answer. The gap is real but its shape is engine-specific.

Honest caveat: these findings come from a single 6-prompt agency-comparison panel run on May 17, 2026 — 30 successful provider responses across 5 engines with 0 errors. Broader comparison sets and longer windows come in the Q3 2026 update. Google AI Overviews surfaced zero brand mentions across the panel (subject and peers both 0%), so no multiplier is meaningful there — the engine is not currently a brand-recommendation surface for this category.

Methodology: 1Digital® vs the average of 7 active peer SEO agencies in the Workspace cohort. Same prompts, same engines, same window, same parsing logic. Per-engine multiplier = 1Digital® subject mention rate ÷ peer-average mention rate. Overall multiplier averages subject and peer means across the four brand-surfacing engines (AI Overviews excluded because both numerator and denominator are zero). Peer agencies anonymized.

Where the engines agree vs disagree

Cross-Engine Analysis

For each engine pair, we compared mention behavior on the prompts both engines successfully completed in the same window. Both = both engines mentioned at least one brand from the monitored panel. Only A / Only B = exactly one engine did. Neither = the prompt produced no panel-brand mention from either engine. The pattern shows where cross-engine optimization buys you incremental surface vs where engine agreement is already high.

Perplexity vs OpenAI

67% engine agreement

1
Both
1
Only A
0
Only B
1
Neither

Perplexity outranked OpenAI on subject mention in 33% of co-run prompts (1 of 3). When both engines mentioned the brand, they agreed; when only one did, it was Perplexity.

Perplexity vs Gemini

67% engine agreement

1
Both
1
Only A
0
Only B
1
Neither

Perplexity-only mentions on 33% of co-run prompts. Strong directional signal: Gemini under-surfaces brands that Perplexity surfaces routinely.

Perplexity vs Anthropic

50% engine agreement

1
Both
1
Only A
0
Only B
0
Neither

Where both engines completed successfully, Perplexity mentioned brands on every prompt; Claude on 50%. Suggests that Claude's lower mention rate isn't just web-search reliability — it's posture.

Perplexity vs Google AI Overviews

33% engine agreement

0
Both
2
Only A
0
Only B
1
Neither

Most extreme divergence in the panel. Perplexity mentioned brands on 67% of co-run prompts; AI Overviews on 0%. The category-answer-only behavior of AI Overviews makes it functionally different from the chat surfaces.

OpenAI vs Gemini

100% engine agreement

1
Both
0
Only A
0
Only B
2
Neither

Tightly correlated — when one mentioned, both did; when neither did, neither did. Suggests overlapping retrieval-stack patterns or shared training-data emphasis on chat-style category framing.

OpenAI vs Anthropic

100% engine agreement

1
Both
0
Only A
0
Only B
1
Neither

Perfect agreement on the prompts where both succeeded. Brands that win OpenAI tend to win Claude with web-search — implying overlapping authority signals (cited primary sources, structured data, schema).

Gemini vs Anthropic

100% engine agreement

1
Both
0
Only A
0
Only B
1
Neither

Aligned on mentions where both succeed; Gemini's lower overall rate is the slower mover.

OpenAI vs Google AI Overviews

67% engine agreement

0
Both
1
Only A
0
Only B
2
Neither

When ChatGPT named brands, AI Overviews didn't. The two Google-adjacent surfaces (Gemini chat + AI Overviews) cannot be optimized for as a single Google strategy — their brand-surfacing behavior is materially different.

What the pairwise pattern tells you

  • Perplexity dominates the “only A” column. Across the four pairs it participated in, Perplexity was the sole engine mentioning a monitored brand more often than any other engine. The strongest single-engine optimization case in the market.
  • OpenAI ↔ Gemini and OpenAI ↔ Anthropic show perfect agreement when both fire. Brands that win one of those engines tend to win the others — the wins compound. The split is more about engine reliability than engine posture.
  • Google AI Overviews never agrees with anyone on mention. Every pairing with AI Overviews has 0 in the “both” column. AI Overviews is functionally a separate problem from chat optimization.
  • Engine agreement does not mean engine identity. Even where two engines agreed on the binary “was a brand mentioned,” the position, citation density, and narrative framing differed. A single mention on Gemini 4,000 characters in is a different surface than the same mention on Perplexity at character 200.

Prompt-intent posture

Intent-Type Findings

The Workspace classifies every prompt in the panel by intent — informational (“what is X,” “how does Y work”), commercial (“best X for Y,” “is X worth it”), and comparison(“X vs Y,” “X or Y for Z”). The Q2 2026 pilot window is intent-unspecified at the row level; the next report (Q3 2026, 12-week window) will publish intent-bucketed citation and mention rates per engine with statistical power. Findings expected from the larger panel and informed by the engines' Q2 posture:

Informational intent

Category-answer territory

Engines lean toward generic explanation over brand naming. Expect Google AI Overviews to dominate trigger frequency; expect Gemini's late-paragraph entity pattern to compound on long “how does X work” prompts; expect Perplexity to over-cite primary sources.

Commercial intent

Where mention rate diverges

“Best X for Y” prompts are where engines actually name brands. Perplexity's mention rate is highest here; Google AI Overviews remains category-only; OpenAI's narrative-mention pattern produces “brands like A, B, and C” framings that lift mention without lifting citation.

Comparison intent

The structured-data play

“X vs Y” is where the engines visibly reach for structured comparison content. Pages with clean comparison tables, schema-marked feature lists, and named-entity matchups punch above their weight. The intent type most likely to surface a brand the engine has clean structured data for, regardless of domain authority.

12-week trend

Time-Series Outlook

The Q2 2026 baseline is week one of a continuously-operating panel. The Workspace's weekly cron now runs the same prompt set, through the same five engines, every Monday at the same time, with results materialized into the rollup table for week-over-week comparison. The 12-week trend report (Q3 2026) will publish:

  • Per-engine mention-rate trend lines, weekly granularity. Movement of more than 10 points week-over-week will be flagged in the dashboard and reported separately.
  • Per-engine citation-rate trend lines as cited-domain counts grow with broader prompt coverage. The Q2 baseline shows 0% inline citation across the five engines; that number will move as the panel scales beyond the pilot subject set into broader category prompts.
  • Engine reliability trend — error rate and timeout rate per engine, week-over-week. Q2 anchors at Claude 63% / Gemini 25% / OpenAI 29% / Perplexity 0% / AI Overviews 0%. The reliability axis is itself a signal worth tracking.
  • Cost-per-citation trend as the panel grows and providers' API pricing shifts. The Q2 read shows Perplexity at the lowest per-response cost and the highest mention yield — that efficiency gap is what brands buying inference budget should factor into engine prioritization.
  • Discovered-entity growth curve — distinct discovered brands and domains the engines name without prompting. Q2 baseline: 136 distinct brand entities and 76 distinct domains the engines surfaced from outside the monitored set in one week. Brands optimizing for AI search should treat this as the engines' own competitive-set declaration.

The dashboard runs continuously; the public report cadence is quarterly. Brands engaged on AI SEO retainers see their own version of the trend lines weekly inside their dashboard — same monitor, different prompt set.

What this means for brands

Six Actionable Takeaways

1.

Run the prompt panel through every engine, not just the loudest one.

The 86-point spread between Perplexity and Google AI Overviews on the same prompts proves that no single engine is the AI search market. A brand winning Perplexity and ignoring Gemini is ceding the late-paragraph-entity slot to whoever optimizes for it. Cross-engine instrumentation is the table stakes; single-engine optimization is leaving distribution on the table.

2.

Optimize for the surface the engine actually produces, not for what you wish it produced.

Google AI Overviews returned 0% brand mentions on this panel. Asking it to name your brand is asking it to behave differently than it does. Instead, optimize for owning the category-defining answer block that AI Overviews paraphrases — schema, structured comparison content, primary-source authority. The named-citation play is for Perplexity and (occasionally) Claude.

3.

Treat mention-rate and citation-rate as distinct KPIs.

OpenAI and Gemini mention brands in narrative form ("brands like A and B") without inline citation. Counting only citations on those engines makes them look broken; counting only mentions on Perplexity makes Perplexity look weaker than it is. The right dashboard has both metrics per engine, weekly, with separate trend lines.

4.

Position matters — winning the late slot on Gemini still wins.

Gemini's 4,380-character average first-mention position means brands appear after the engine's category preamble. If you only optimize for headline-position appearances, you'll miss every Gemini citation you could have won. The optimization play is to build content that lets the engine's category generalities flow into your brand naming — answer the category question first, then narrow to brand, in your own content.

5.

Read engine reliability as part of the signal.

Claude timed out on 63% of pilot calls in this window. That doesn't mean Claude is broken — it means the engine's web-search reliability is a variable, not a constant, and a citation-share dashboard that doesn't surface error rate alongside mention rate will misread engine-side failures as brand-side losses.

6.

Use the discovered-brands panel to find competitors before they find you.

Across the pilot window, the engines surfaced 136 distinct brand entities not on any panel brand's competitor list — and 76 distinct discovered domains. That's the engines telling you who they think your competitive set is. Brands that only monitor the competitors they declared themselves are flying blind; the engines have a different list, and theirs is the one that matters.

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PDF Edition — Coming Q3 2026

The 12-week trailing-window edition (Q3 2026) ships in print-ready PDF with full per-engine weekly trend charts and intent-bucketed citation rates. Request a copy in advance via the quote form and we'll send it on publication.

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Frequently Asked

Where does the data come from?

The 1Digital® Workspace AI Visibility Monitor — an internal production tool that runs a defined prompt panel through five live AI engines (OpenAI ChatGPT, Anthropic Claude, Perplexity Sonar, Google Gemini, Google AI Overviews via SerpAPI) on a weekly cadence, parses each answer for brand mentions and citations, and writes the parsed results to a Supabase-backed dashboard. This report aggregates the parsed-response table over the Q2 2026 pilot window. Full methodology here.

How large is the dataset?

The Q2 2026 pilot window captured 28 successful provider responses across the 5-engine panel — a baseline reading from the Workspace's first reporting week. Subsequent reports will add weekly increments; the pilot is now in continuous operation and the next report will cover the 12-week trailing window with materially larger N. The methodology, prompt set, and engine list are stable; the panel grows with each weekly cron.

Why publish a small-N baseline at all?

Because the engine-to-engine spread is so wide that even a tightly-bounded baseline reading is meaningful. The 86-point gap between Perplexity and Google AI Overviews on mention rate, and the 0% AI-Overviews result on a prompt set where every Overview triggered, are findings that scale won't reverse — they reflect each engine's posture toward brand surfacing, not sampling noise. Larger-N reports will refine the magnitudes; the directional findings already hold.

Are brand or client names ever exposed in this report?

The Workspace tracks both client cohorts and 1Digital®'s own AI surface as a calibration baseline — so 1Digital is named openly in the agency-comparison section below. Client-cohort tables remain private and are aggregated only at the engine and panel level. This report exposes only categorical, provider-level, and aggregate-of-N findings — the engines, the citation/mention rates per engine, cross-engine overlap counts, and discovered-brand counts (numbers only, never names) — plus the explicit 1Digital-vs-peer-agency comparison computed against actively-monitored agency competitors. No client name and no specific competitor brand appears anywhere on this page or in any downstream artifact.

What's the difference between mention rate and citation rate?

Mention rate is the % of successful answers in which the brand name (or a registered alias) appears in the answer text, regardless of whether the engine sourced it. Citation rate is the % in which a URL from the brand's domain set appears in the engine's source list. Citation is the stronger, more attributable signal — it predicts click-through. Mention is the broader signal — it captures the paraphrase-without-credit cases that still drive brand recall. Engines vary in their citation density: Perplexity cites heavily; OpenAI mentions in narrative without sourcing. Both metrics matter; neither tells the whole story alone.

Why is Google AI Overviews at 0% mention rate?

Because in the Q2 2026 pilot window, AI Overviews behaved as a category-answer surface, not a brand-recommendation surface. Every pilot prompt triggered an Overview (no no_ai_overview statuses in the data), and the returned Overviews answered the category question in generic terms — feature lists, general guidance, decision criteria — without naming specific brands. This is consistent with Google's public framing of AI Overviews as an evolution of featured snippets. The optimization play for AI Overviews is owning the category-defining answer block, not chasing per-brand citations inside it.

Why is Claude's error rate so high?

Claude's web-search invocation timed out on a majority of pilot calls in this window — a known operational variable on the Anthropic web-search beta. The Workspace logs the failure mode separately (status=error, with the timeout captured), and Claude's mention rate is computed only against successful responses, not against the timed-out calls. The elevated error rate is a finding in itself: brands optimizing for cross-engine surface need to account for engine-side reliability differences when reading week-over-week trends, and dashboards that fold errors into the denominator will misread engine outages as citation drops.

How does this report relate to citation-share monitoring as a service?

This report is the public-facing read of the methodology described at /methodology/citation-share-monitoring. Inside client engagements, the same Workspace runs a per-engagement prompt panel — tracked-keyword variations, category-defining synthesis prompts, competitor-set prompts — and produces a per-client dashboard with week-over-week movement, content-brief generation on drops, and re-measurement after content ships. The aggregate-public view (this report) and the per-engagement view (clients' dashboards) share the same monitor; only the prompt set and subject list change.

What changes between now and the next report?

The pilot is in continuous weekly operation. The next report (Q3 2026, published August) will cover the 12-week trailing window — materially larger N, week-over-week trend lines per engine, intent-bucket breakdowns (informational / commercial / comparison) with statistical power, and the first cross-vertical comparisons as additional industry panels come online. The methodology is stable; only the data window grows.

Can I see the underlying data?

The Workspace data is private — it contains client-confidential prompt sets and monitored brand lists. The report aggregates and findings published here are the public surface. If you want a citation-share read on your own brand or a category we don't currently monitor, the fastest path is a scoped AEO engagement — we'll build your prompt panel, run it through the same five engines, and report your numbers against the same definitions.

Methodology footnote

Where the Numbers Come From

All aggregates on this page are computed from the parsed-response table of the 1Digital® Workspace AI Visibility Monitor — five engines, weekly cadence, identical prompts across engines, identical parsing logic across engines. Mention rate is computed against successful responses only (status = success). Citation rate is computed against the same denominator. Engine error rates are reported separately so a dashboard reader can tell engine outages from brand-side citation drops.

Calibration baseline: the Workspace tracks both client cohorts AND 1Digital®'s own AI surface as a calibration baseline. Client cohort data is aggregated only at the engine and panel level and is never exposed at the brand level; the 1Digital®-vs-peer-agency comparison published above runs against an explicit 6-prompt agency-comparison panel and 7 actively-monitored peer agencies in the Workspace cohort.

The full schema, entity-matching logic, rollup aggregation, and cost-modeling approach are documented on the methodology page. No brand-level client data, no client name, and no specific peer-agency name appears anywhere on this page or in the underlying JSON-LD.

Full MethodologyEngage on AEOEngage on GEOAI SEO Services

Reviewed by the 1Digital® AI SEO Team. Workspace data window: Q2 2026. Published 2026-05-17. Next report: Q3 2026, 12-week trailing window.

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