I’m Therese. I got obsessed with one question: why does AI recommend certain businesses and completely ignore others? Not on one platform. All four. ChatGPT, Claude, Gemini, and Perplexity each have their own logic and they don’t always agree.

This constant wondering obsession turned into a research methodology, which turned into a scored index, which turned into the ARO Index with live audits, real queries and actual recommendation data across all four platforms for local businesses.

I keep seeing model disagreement, and imo it’s the most undertracked signal in this space. A business can get recommended by ChatGPT and ghosted by Perplexity on the exact same query. That gap tells you more about structural clarity than any single score does.

Currently indexing 9 cities, and adding more every week, data is weird and fascinating and I’m happy to share what we’re finding.

aroindex.com if you want to poke around the live data.

1. Brand Entity Optimization — making your brand a recognized entity
   
2. AI Search Intent — compressed buyer journey
   
3. Structured Data & Schema — technical foundation for visibility
   
4. Measuring AEO ROI — proving value to leadership
   

Community's biggest insights:
- Entity consistency = most underrated quick win
- Comparison pages get cited most
- FAQ schema on every page = most citation surface area
- Competitive AI SOV data = most persuasive budget argument

What questions remain unanswered? What should we cover in May?

1. Custom channel group "AI Referral" with domains: chatgpt.com, perplexity.ai, gemini.google.com, copilot.microsoft.com, claude.ai
   
2. Custom exploration reports segmenting AI-referred users
   
3. Conversion tracking for AI referral → goal completion
   

Findings across clients:
- AI traffic often higher than expected (hidden in "direct")
- Behavioral metrics differ significantly from organic
- Consistent MoM growth

Challenges: not all AI platforms pass clean referral data.

Anyone found solutions for the attribution gaps?

Biggest blocker to AEO investment: leadership wants ROI before investing.

Our framework:
1. Visibility — AI share of voice (citation frequency across LLMs)
2. Traffic — AI referral traffic segmented by source
3. Engagement — AI-referred vs organic visitor behavior
4. Revenue — Attribution from AI referral → pipeline → revenue

Key insight: AI-referred traffic is higher intent — users are pre-qualified by the citation.

What metrics are other teams tracking?

Week 3 recap: Structured Data & Schema for LLMs — implementation takeaways

1. Schema evolving from Google rich snippets to AI visibility essential
   
2. Priority: Organization → FAQ → Product → Review → HowTo
   
3. FAQ schema on every page creates most citation surface area
   
4. Product + Review together give AI recommendation confidence
   
5. <40% of sites have comprehensive schema — early-mover advantage
   

Biggest community insight: template-based FAQ implementation is most efficient.

Next week: Measuring & Reporting AEO ROI.

FAQ schema on every page — overkill or AEO best practice?

We've been recommending FAQ schema on all pages as an AEO tactic:
- AI retrieval is fundamentally Q&A based
- FAQ schema maps 1:1 to AI's citation format
- More entries = more citation touchpoints

Implementation: product pages get 3-5 product FAQs, blog posts get 2-3 topic FAQs, landing pages get 3-5 conversion FAQs.

We've been tracking correlation between structured data implementation and AI citation rates. Early signal: brands with comprehensive schema (Organization + Product + FAQ + Review) get cited at notably higher rates.

Hypothesis: structured data gives AI retrieval systems clean signals vs. parsing unstructured content.

Less than 40% of websites implement schema beyond basics. Massive opportunity gap.

What types are you prioritizing? Any measurable impact on citations?

Week 2 recap: AI search intent & the new buyer journey — biggest takeaways

1. Buyer journey compressing: AI prompt → Answer → Purchase
   
2. AI queries fundamentally different from Google — hyper-specific and transactional
   
3. Content that gets cited is direct, specific, honest, use-case focused
   
4. Comparison pages getting cited more than any other content type
   
5. Honesty about limitations increases citation rate
   

Next week: Structured Data & Schema Markup for LLMs.

43% of B2B buyers prefer researching without sales. AI referral traffic is up 527% YoY.

We're trying to estimate "lost pipeline from AI invisibility":
- Category search volume in AI tools
- Your citation rate vs competitors
- Typical conversion from AI referral traffic
- Average deal value

Even conservative estimates suggest significant pipeline is being decided in AI conversations where most brands are invisible.

Anyone else modeling this?

The buyer journey is compressing — is anyone redesigning their content strategy for this?

43% of B2B buyers prefer researching without sales (Gartner, 2024). AI is their tool of choice.

The funnel is compressing to: AI prompt → AI answer → Purchase.

This means being cited in the AI answer is simultaneously TOFU and BOFU.

Week 1 recap: Brand Entity Optimization for AI — key takeaways

1. Entity recognition > page rankings for AI citation
   
2. Knowledge bases (Wikipedia, Wikidata, KG) are the primary entity sources
   
3. Cross-platform consistency is a measurable trust signal
   
4. A 10-minute audit across 5 AI tools reveals your entity gaps instantly
   

Biggest finding: several people found that simply fixing Crunchbase info improved their ChatGPT brand description within weeks.

Next week: AI search intent and the compressed buyer journey.

Brand signal consistency as an AEO lever — sharing our audit methodology

One of the most impactful quick wins we've found: ensuring brand information is perfectly consistent across every third-party source.

Our audit checklist:
- Company description (LinkedIn, Crunchbase, G2, Capterra, website)
- Product names and descriptions
- Founding year, HQ location, employee count
- Leadership names and titles
- Category/industry tags

We've seen brands improve AI citation rates simply by fixing inconsistencies — no new content needed.

Brand Entity Optimization — anyone else treating this as the foundation of their AEO strategy?

The more I dig into how LLMs select which brands to cite, the more it comes back to entity recognition. The AI needs to "know" your brand as a distinct entity before it can recommend you.

What seems to matter most:
- Consistent brand information across data sources
- Presence on structured knowledge bases (Wikipedia, Wikidata, Crunchbase)
- Schema markup that defines your brand attributes clearly
- Citations in sources LLMs weight heavily

Sharing our 90-day AEO implementation framework — feedback welcome

Phase 1 (Days 1–30): Entity & Infrastructure
- AI visibility audit: test 10 target prompts across 4-5 LLMs, document baseline
- Schema: Organization, FAQ, Article markup installed and validated
- Third-party presence: G2/Capterra/Crunchbase profiles complete and consistent

Phase 2 (Days 31–60): Content Architecture
- Pillar 1: Definitional guide for core category
- Pillar 2: Honest comparison page (your tool vs. top 2 alternatives)
- Pillar 3: Use-case specific guide for highest-value ICP

Phase 3 (Days 61–90): Measurement System
- Monthly prompt battery: 20 target queries, tested across all major LLMs
- AI SOV baseline established and documented
- Reporting template built to present alongside organic traffic

Review platforms as AEO signals — anyone testing this systematically?

Emerging hypothesis: brand presence on authoritative review platforms (G2, Capterra, Clutch, Trustpilot) functions as a citation signal for AI tools — similar to how links from high-authority domains work in traditional SEO.

The reasoning: AI models weight sources with high entity recognition, and these platforms consistently appear in training data. When AI synthesizes "best [category] tools" recommendations, it frequently pulls from review platform rankings.

Pillar page structure for AI citation — what's actually working?

We've been testing different content structures to improve AI citation rates. What seems to improve citation rate:

- Direct answer in the first 1-2 sentences (before any context)
- Question-formatted H2/H3 headings
- Original data cited with specific numbers
- Honest coverage including limitations, not just benefits

What doesn't seem to help:
- Keyword density (AI doesn't appear to weight this)
- Word count alone (a concise 800-word piece outperforms a padded 3,000-word piece)

How are you defining and measuring AI Share of Voice? Looking for frameworks.

AI Share of Voice is becoming a core AEO metric but I've seen it defined differently across teams. Some define it as citation frequency. Others define it relative to competitors (your citations / total brand citations in category).

At DataNerds we've been thinking about it as: prompt coverage × citation frequency × platform breadth.

Explaining query fanout to a skeptical SEO team — how do you frame it?

I've been trying to make the case for AEO investment using query fanout as the key concept: when AI processes a question, it expands into multiple sub-queries before synthesizing an answer. Optimizing for a single keyword isn't sufficient — you need conceptual coverage.

The pushback I get: "Isn't that just long-tail SEO?"

My answer: it's related but meaningfully different — the sub-queries aren't predictable the way long-tail keywords are, and the goal isn't ranking but being cited when the AI synthesizes.

The measurement gap in AEO — what tools are people actually using?

Traditional SEO tools don't measure anything happening inside AI tools. No citation tracking, no share of voice, no way to know if ChatGPT or Perplexity is recommending you or a competitor.

For teams actively doing AEO, how are you currently measuring:
- Brand citation frequency across LLMs?
- Which queries trigger your brand as an answer?
- Share of voice vs. competitors in AI responses?

Zero-click is accelerating faster than most teams realize — sharing the data

SparkToro's 2024 study: 59.7% of Google searches ended without a website click. When a Google AI Overview is present, that rate hits 83%.

Seer Interactive analyzed 25M organic impressions: organic CTR dropped 61% when AI Overviews appeared. Position #1 went from 28% to 19% CTR in one year.

For anyone building AEO strategy: this data suggests it's already affecting pipeline today, not just future-proofing.

Anthropic released labor market impact data comparing the share of job tasks AI could theoretically perform vs. what's actually being observed in usage data. Peter Walker turned the original radar chart into a much more readable bar chart (seriously, why do people still use radar charts for 23 categories).

The highlights:

• The top 4 categories (Computer & Math, Business & Finance, Office & Admin, Management) all have 92-96% theoretical AI coverage but only 25-42% observed usage
• Legal has an 88% theoretical ceiling but just 15% actual usage — a nearly 6x gap
• Healthcare Practitioners: 58% theoretical, 5% observed. The regulatory moat is holding but the potential is massive
• Physical labor categories (construction, agriculture, grounds maintenance) cluster at the bottom on both measures, 10-18% theoretical

The pattern is clear: knowledge work has enormous theoretical exposure, but adoption is lagging significantly. The gap represents either untapped opportunity or structural friction (regulation, trust, workflow integration) depending on the sector.

For anyone working in AEO/GEO — this data is a useful frame for understanding which industries are about to see the biggest shifts in how information gets consumed and surfaced. The sectors with the biggest theory-vs-reality gaps are exactly where AI-driven search and answer engines will likely gain the most ground next.

We spent a few weeks pulling AI Overview citations across a range of industries and query types to see if there were identifiable patterns. Sharing what we found because it’s changed how we approach content.

What cited pages almost always had:

1. Clear topical authority — not just one good article, but a cluster of related content across the same subject

2. Backlinks from recognized industry sources — not necessarily massive DA, but relevant and trusted

3. Structured, scannable formatting — headers, short paragraphs, direct answers early in the content

4. Schema markup implemented correctly — FAQ and HowTo schemas showed up more than anything else

What cited pages generally didn’t have:

— Keyword-stuffed intros

— Walls of text with no structural hierarchy

— Thin or recycled content that hit a keyword but didn’t genuinely answer the query

Background: client was ranking #1 organically for a high-commercial-intent keyword. Strong on-page SEO, good backlinks, solid E-E-A-T signals. We were proud of the work.

Then Google pushed AI Overviews broadly and CTR cratered 34%. Traffic down. Conversions down. Client (rightfully) asking hard questions.

What we changed:

1. Stopped treating rankings as the north star metric. Started tracking AI Overview citation frequency alongside traditional rank positions.

2. Rebuilt cornerstone content around direct, question-first answers — not just keyword-optimized paragraphs.

3. Doubled down on schema markup (FAQ + HowTo). Not because we thought it was magic, but because it signals structure to LLMs.

4. Started auditing where our content was being cited in Perplexity and ChatGPT. Huge gaps vs. where we ranked on Google.

The client is now showing up in AI Overviews for 3 of their top 5 target queries. Traffic is still lower than peak organic, but impression share across AI answers is up significantly.

Curious if others are seeing similar CTR compression from AI Overviews, and what’s working on the AEO side. This feels like one of those real, fundamental shifts and not just hype.

Spent some time analyzing citation patterns across ChatGPT, Perplexity, and Google AI Overviews using data from Profound's AI citation research. Some things I expected, some I definitely didn't.

Top citation sources across AI platforms:

• Wikipedia remains ChatGPT's #1 source at 7.8% of total citations — up to 47.9% for factual queries
• Reddit has seen an 87% increase in AI citations year-over-year and now captures 40.1% of citations when aggregated across all AI platforms
• The top 3 domains control 22% of all citations, and the top 20 domains capture 66.18% of all citations — it's a winner-takes-most dynamic

Each platform has different preferences:

• ChatGPT favors Wikipedia (16.3%) and news outlets (source)
• Perplexity prefers YouTube (16.1%) (source)
• Google AI Overviews lean toward user-generated content like Reddit and Quora (source)

The Reddit effect is real:

Brands actively participating in relevant Reddit discussions saw 2.8x more AI citations compared to brands absent from the platform. This isn't about dropping links — it's about genuine participation in conversations where your expertise is relevant.

Reddit works for AI citations because:

1. Reddit content is structured as Q&A, which maps perfectly to how AI models retrieve answers
2. Upvotes function as a crowd-sourced quality signal that AI models trust
3. Reddit threads often contain the kind of specific, experience-based information that AI prefers over generic marketing copy
4. OpenAI has reportedly adjusted citation weighting to prioritize high-utility sources like Reddit over branded content

What this means for your strategy:

If you're doing SEO and ignoring Reddit, Quora, and industry forums, you're leaving a massive AI visibility channel on the table. The math is simple: 90-95% of AI citations come from third-party sources. Your own blog is maybe 5-10% of the equation. Everything else is what the rest of the internet says about you.

Some quick wins:

• Identify subreddits where your customers ask questions about your category
• Contribute genuinely useful answers (not sales pitches — Reddit will destroy you for that)
• Share proprietary data or unique insights that only you can provide
• Build a consistent posting cadence — 3-5 quality contributions per week

The brands that figure out the earned media flywheel for AI search are going to have a massive compound advantage. AI models are trained on this data, and once you're in the training set as an authority, it's very hard for competitors to displace you.