AI Citation Rate Benchmarks by Industry

TL;DR

• AI citation rate benchmarks vary widely by vertical: Brandlight reports healthcare brand-mention rates of 60% on Perplexity vs 35% on Google AI Overviews — a ~25-point engine gap inside one industry.
• Conductor's 2026 AEO/GEO Benchmarks: AI referral averages ~1.08% of total website traffic across 10 industries; IT (2.8%) and Consumer Staples (1.9%) lead. ChatGPT drives 87.4% of all measured AI referral traffic.
• B2B SaaS shows the widest topic-vs-brand gap: aiseo.com.mx finds SaaS at 76% topic-citation rate on ChatGPT (50K responses), but DerivateX finds 44% of 50 B2B SaaS brands score below 50/100 on a composite visibility scale.
• Cross-engine overlap is small: per The Digital Bloom's 2025 LLM visibility report, only 11% of websites are cited by both ChatGPT and Perplexity — 89% of citations are platform-exclusive, so single-engine benchmarks understate the surface.
• Treat all numbers below as planning anchors, not industry ground truth: vendor methodologies differ in sample size (50-4,000 prompts), prompt mix, engine coverage (4-7 engines), and metric definitions ("citation" vs "mention" vs "share of voice" are not interchangeable).
• Re-baseline every quarter; AI engines update model versions on a multi-week cadence and YMYL category behavior diverges from non-YMYL.

Why benchmarks here are approximate

There is no single, vendor-neutral, audited source of AI citation rates by industry. Every benchmark in this article comes from a vendor study, with its own:

• Sample size — ranges from tens of brands (DerivateX: 50 B2B SaaS companies, 1,400 prompts) to thousands of queries (Brandlight: 4,000 healthcare queries; Tinuiti: ~9-vertical multi-platform panel).
• Prompt mix — some studies use commercial-intent prompts, others use full buyer-journey panels, others mix.
• Engine mix — some studies cover four engines, others cover seven.
• Metric definitions — "citation rate," "mention rate," and "share of voice" are not interchangeable.

Treat the numbers below as planning anchors. Do not directly compare a percentage from one study against a percentage from another.

AI referral traffic share by industry

Conductor's 2026 AEO/GEO Benchmarks Report measured AI referral traffic as a percentage of total website traffic across 10 industries (Conductor 2026 AEO/GEO Benchmarks):

Key finding from the same report: ChatGPT accounted for 87.4% of all AI referral traffic across the 10 industries measured. AI referral grew approximately 1 percentage point month over month at the time of the report. Brand share within these flows can be highly concentrated: NerdWallet captured 6.73% of all AI citations in Financials, while Zillow captured 7.36% of Real Estate brand mentions.

Brand mention rates: healthcare

Brandlight's healthcare study measured brand mention rates across 4,000 queries spanning 21 healthcare categories (Brandlight: Healthcare and insurance visibility):

This is the largest gap across engines documented for any single vertical: Perplexity names a healthcare brand in 60% of answers, while Google AI Overviews names one in only 35% of answers. The implication: an AEO program focused only on Google AI Overviews underestimates the citation surface in healthcare by roughly 25 percentage points.

Brand mention rates: SaaS and technology

Industries Dominating ChatGPT (aiseo.com.mx, Feb 2026) analyzed 50,000+ ChatGPT responses and found SaaS at the top with a 76% citation rate, followed by healthcare at 72% (aiseo.com.mx). Note: this measures citation presence per response, not per-brand share, and is ChatGPT-only.

DerivateX's State of AI Visibility in B2B SaaS: 2026 Benchmark Report ran 1,400 buyer-intent prompts across 50 B2B SaaS companies on ChatGPT, Perplexity, Claude, and Gemini and reported (DerivateX, via DemandGen Report):

• 44% of B2B SaaS companies score below 50 on a 0-100 composite AI visibility scale.
• Mention frequency is the largest gap; sentiment is generally positive once mentioned.

These two studies disagree on first read — but they are measuring different things. SaaS as a topic is easy for ChatGPT to answer; specific SaaS brands are not easy for ChatGPT to name.

Brand mention rates: financial services

The Digital Bloom's 2026 AI Citation Position & Revenue Report flags financial services as the highest-citation-volatility category among the verticals measured, while ecommerce is the most stable (The Digital Bloom). Within Financials, NerdWallet (6.73% of AI citations per Conductor) is winning more AI citations than traditional banks — a pattern consistent with editorial-comparison brands outranking primary brands.

For publishers and aggregators in finance, AI Overview citation probability is closely tied to organic SERP position: the report cites SERP #1 = 33.07% AIO citation probability, dropping to 13.04% at SERP #10 — a roughly 60% decline from top to bottom of page one.

Brand mention rates: retail and ecommerce

Tinuiti's Q1 2026 AI Citation Trends Report tracks 9 verticals — apparel, beauty, electronics, food and beverage, home and garden, manufacturing, OTC health, technology, and transportation and logistics — across 7 platforms (ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Google Gemini, Microsoft Copilot, Meta AI) (Tinuiti). Headline findings:

• 9% of total citations attributed to social media in January 2026.
• No universal top source across the nine retail verticals: top-cited domain shifts by category and platform.
• Amazon dominates Consumer Staples AI citations at 17.99% per Conductor.

Profound's enhanced citation categories layer adds the citation-type mix: Retail and eCommerce sees 29.7% media citations — meaning roughly three out of ten retail citations come from editorial coverage rather than direct brand domains (Profound: Enhanced citation categories).

Brand mention rates: telecom, media, and other verticals

From Profound's citation-category mix:

For real estate: Zillow earns 7.36% of AI brand-mention market share in the category despite not appearing as a top-5 cited domain in the underlying citation lists (Conductor).

Scrunch's industry breakdown covers 10 industries (finance, travel and transportation, hospitality and food, technology, retail, media and entertainment, healthcare providers, education, automotive, personal and home services) and finds that Reddit's role as a citation source varies sharply by vertical (Scrunch: Reddit paradox). Reddit moves the needle in technology, hospitality, and personal services, but contributes little in finance and healthcare.

Cross-engine overlap is small

A core finding worth keeping in mind when comparing benchmarks: only 11% of websites are cited by both ChatGPT and Perplexity — 89% of citations are platform-exclusive (The Digital Bloom 2025 LLM visibility report). A given industry's leading brands can therefore look very different on each engine.

How to use these benchmarks

1. Pick the closest-matching study. If you are in B2B SaaS, DerivateX is the closest fit. If you are in healthcare, Brandlight is closer than Tinuiti. Do not try to weight-average across studies.
2. Anchor only on per-engine numbers within a single study. Cross-study averaging hides methodology drift.
3. Use the AEOfix "pre-AEO" baseline as a starter target. AEOfix's benchmark is built on 110 brands across 7 verticals (December 2025 - February 2026) and provides a pre-AEO / post-AEO contrast useful for goal-setting (AEOfix benchmarks).
4. Re-baseline every quarter. All vendor benchmarks shift with model versions; figures referenced here are point-in-time as of April 2026.
5. Pair benchmark data with your own monitoring. Build a 50- to 100-prompt panel of buyer-intent queries for your category and run it weekly across the engines that matter to you (see Brand Mention Monitoring for AI Search).

Methodology caveats (must-read)

• Vendor-published figures may be biased toward outcomes that flatter the vendor's product. Always check sample size, query design, and engine mix before quoting.
• Definitions of "citation" vary: some studies count any URL appearance; others count only named brand mentions; others count only inline source attributions. These are not the same metric.
• AI engines update model versions on a multi-week cadence. A benchmark dated October 2025 may not reflect December 2025 behavior.
• For YMYL categories (healthcare, finance, legal), engine behavior is materially different from non-YMYL categories. Do not generalize across the YMYL boundary.

FAQ

Q: Are vendor benchmark numbers comparable across studies?

No. Sample sizes range from 50 brands (DerivateX) to thousands of queries (Brandlight 4,000; Tinuiti ~9 verticals × 7 platforms), prompt mixes differ (commercial-intent vs full buyer-journey), and engine mixes range from 4 to 7 platforms. Definitions of "citation" also differ — some count any URL appearance, others only named brand mentions, others only inline source attributions. Anchor only on per-engine numbers within a single study.

Q: Which industry has the highest AI citation rate?

It depends on the metric. By topic-citation rate on ChatGPT, aiseo.com.mx places SaaS first at 76% and healthcare second at 72% (50K-response analysis). By healthcare brand-mention rate, Brandlight finds Perplexity at 60% and ChatGPT at 54% across 4,000 queries. By AI referral traffic share, Conductor finds IT at 2.8% leading 10 industries.

Q: Why do healthcare brand-mention rates differ so much across engines?

Per Brandlight: Perplexity 60%, ChatGPT 54%, Copilot 46%, Google AI Overviews 35% — a 25-point spread. The likely drivers are differences in retrieval source mix (Perplexity weights its own crawler heavily), YMYL safety policies on Google's side, and engine-level brand-naming heuristics. Healthcare AEO programs scoped only to AI Overviews underestimate the surface by ~25 points.

Q: Should I weight-average across multiple vendor benchmarks?

No. Cross-study averaging hides methodology drift and produces misleading composites. Pick the closest-matching study for your industry (DerivateX for B2B SaaS, Brandlight for healthcare, Tinuiti for retail verticals, Conductor for AI referral traffic) and quote its numbers directly with full attribution.

Q: How often do these benchmarks need to be re-baselined?

At least quarterly. AI engines push model-version updates on a multi-week cadence (the GPT-5.2 → GPT-5.3 transition compressed the recency retrieval window from ~33% to ~6% for content under 30 days old). Vendor figures dated 6+ months old should be treated as historical context, not current-state benchmarks.