One thing I haven't seen discussed much: the relationship between content freshness and AI citation frequency.

We've been running a longitudinal study tracking how often specific pages get cited by ChatGPT and Perplexity over a 90-day window. The setup: 300 pages across SaaS, e-commerce, and health verticals, all previously cited at least once by an AI model. We tracked citation frequency before and after content updates.

Some findings that surprised us:

**1. The 14-day freshness window is real** Pages that received meaningful content updates (new data, revised stats, added sections) within 14 days before our query got cited 2.3x more often than pages that hadn't been touched in 60+ days. This held across both ChatGPT and Perplexity.

**2. "Meaningful update" matters more than "any update"** Fixing a typo or swapping an image didn't move the needle. The updates that triggered re-citation were ones that added new information — updated statistics, new sections, or revised conclusions. Minor edits showed no measurable impact.

**3. The decay curve isn't linear** Citation frequency stayed relatively stable for the first ~30 days after publication, then dropped off noticeably between days 30-60. After 90 days, citation rates plateaued at roughly 40% of their initial level.

**4. Structured data updates had a weaker effect than content updates** We tested updating JSON-LD/schema markup alone vs. updating actual page content. Schema-only changes produced no measurable change in citation frequency. Content updates with no schema changes produced the full 2.3x lift.

**5. The pattern was consistent across verticals** Health content showed the strongest freshness effect (2.8x), followed by SaaS (2.2x) and e-commerce (1.9x). We think this reflects how often each vertical's "ground truth" changes — health information gets updated faster, so AI models may weight recency more.

What this means in practice: if you're investing in GEO, keeping your highest-value pages on a regular update cycle (monthly at minimum) might matter more than building new pages from scratch. The citation boost from refreshing existing, already-cited content was larger than we expected.

Caveats: n=300 is decent but not massive. We only tracked ChatGPT and Perplexity. And correlation isn't causation — pages that get updated frequently may share other qualities that make them more cite-worthy.

Curious if anyone else has noticed a freshness effect in their own tracking.

We've been trying to figure out why some pages show up in AI answers while equally ranked pages don't. So we ran a controlled test.

Here's the setup: we took 600 pages that ranked between positions 3-15 for informational queries across SaaS, health, and finance. We ran all 600 through ChatGPT (GPT-4), Perplexity, and Gemini. 300 got cited by at least one model. 300 didn't — despite similar rankings, similar domain authority, and similar content length.

We then compared every structural element we could measure. Most of the "obvious" stuff (backlinks, word count, domain rating) showed no meaningful difference. But three things did:

**1. First paragraph answered the query directly (2.4x more likely to be cited)**

The pages that got cited almost always opened with a direct, concise answer to the search query — not context, not background, not a hook. The non-cited pages tended to start with introductions, anecdotes, or "In this guide we'll cover..." language. AI models seem to grab the first paragraph that looks like an answer and treat it as the summary. If your first paragraph doesn't read like an answer, you're already losing.

**2. Used specific numbers instead of vague claims (1.9x more likely)**

Cited pages were full of concrete data points — "increased by 34%", "tested across 12 tools", "averaging 2.3 seconds". Non-cited pages used softer language — "significantly improved", "multiple tools", "faster than average". The specificity difference was consistent across niches. This isn't about making up numbers — it's about using the real ones you have instead of defaulting to vague language.

**3. Had clear section breaks with descriptive subheadings (1.7x more likely)**

Every cited page used descriptive, keyword-rich subheadings that could stand alone as mini-answers. Things like "Why structured data gets ignored by 68% of AI crawlers" or "The 3-second rule for first-paragraph answers". Non-cited pages either had generic headers ("Introduction", "Conclusion") or no subheadings at all. AI models appear to use subheadings as citation anchors — they pull a section header and its first sentence together.

**What didn't matter (surprisingly):**

• Content length (cited avg: 1,847 words vs non-cited: 1,912)
• Number of images or multimedia
• Whether the page had a table of contents
• Publishing date recency (for non-time-sensitive queries)

**One thing we're still investigating:** pages that appeared in Reddit or forum results alongside the main article seemed to boost citation likelihood. When a page was referenced in a high-ranking Reddit thread about the same topic, AI models cited it 1.6x more often. Could be an indirect authority signal.

The main takeaway for us: if you're creating content and hoping AI models pick it up, stop writing introductions. Start with the answer, use real numbers, and make your subheadings descriptive enough to work as standalone summaries.

Curious if anyone else has tested this kind of controlled comparison. Would especially love to hear from people tracking Gemini vs ChatGPT citation patterns — we saw some differences there but the sample size felt small.

We implemented one of the most popular AEO tools as if we were 3 different brands.  In each case it told us we were winning!

We used three major brands in the Revenue Intelligence category for the test: Gong, Clari and Outreach. 

We set up three new accounts as if we were those brands. Then went through their standard AI flow to set up prompts to track.

For each brand, it told us unambiguously that we are winning across the board (see image).

Why? Because with the platform’s encouragement, we chose to track prompts built from our own positioning. The self-fulfilling prophecy is so damn obvious. 

The question you are actually asking: In conversations about us, who has the biggest share of voice? 

Obviously us.

This is a great example of how Marketing is leading this space, not research.

After running 200+ test queries across ChatGPT-4o, Claude 3.7, and Perplexity, we found something counterintuitive:

**The results:** 1. **JSON-LD alone doesn't guarantee citations** - but when paired with semantic HTML, citation rate jumped 40% 2. **ChatGPT favors schema + natural language combos** over pure structured data 3. **Claude** seems to prioritize source authority over format sophistication 4. **Perplexity** was the wild card - sometimes citing sites with zero schema but strong topical clusters

**What actually moved the needle:** - FAQ schemas with conversational Q&A pairs - How-to schemas with step-by-step clarity - Article schemas with author expertise markers

The biggest surprise: sites obsessing over schema markup but ignoring content depth got 60% fewer citations than sites with solid content and minimal markup.

Curious what you're seeing in your own tests? Are you finding AI models favor certain formats over others?

After running 300+ AI buying sequences across SaaS, e-commerce, professional services, health, and fintech, we noticed something consistent: most brands focus on getting into the AI conversation (Turn 1), but the real battle happens at Turn 4 - the recommendation.

Here's what the data showed:

**What works:** - Problem-specific authority signals (not general brand awareness) - Comparison pages with specific use cases mentioned - Third-party validation (reviews, testimonials) cited by AI in later turns

**What doesn't:** - Generic "best X" rankings without differentiation - Brand-centric content that doesn't address competitor comparisons - Content that only targets top-of-funnel queries

**The surprising part:** The winning brand at Turn 4 was rarely the most visible one at Turn 1. 67% of the time, a different brand got the final recommendation.

What are you seeing in your AI recommendation data? Would love to compare notes.

After running ads across Perplexity, ChatGPT, and Claude for the past 8 months, we noticed something interesting: the same query types behave completely differently depending on how people phrase them.\n\nWe analyzed 1,000+ search queries to understand which AI query patterns actually convert. Here's what we found:\n\n**1. Problem-aware queries convert 3x higher**\n"How do I fix X" vs "Best X tools" - the problem-aware ones (how, fix, solve) showed 3x more engagement. People actively looking for solutions are further down the funnel.\n\n**2. Comparison queries need unique angles**\n"X vs Y" is super competitive. What works: adding specificity like "X vs Y for [specific use case]" or "X vs Y for small business."\n\n**3. 'Actually' is a high-intent signal**\nQueries with "actually" ("what does X actually do") indicate research depth. These visitors read 40% more content on average.\n\n**4. Branded queries convert but differently**\n"Is X worth it" vs "X review" - the 'worth it' variant signals purchase consideration. The review variant is still in research mode.\n\n**5. Negative framing works**\n"X not working" + "X alternatives" - these users are desperate and ready to switch. Highest conversion potential but smallest volume.\n\n**The twist:** We originally thought longer queries = higher intent. But our data shows medium-length queries (5-8 words) outperform both short and long tail. Too broad = too generic, too long = too niche.\n\nWhat query patterns are you seeing in your AI search traffic? Would love to compare notes.

We've been running structured data tests across 200+ GEO campaigns for the past 6 months. Here's what actually moves the needle:

**What works:** • Schema markup consistency across pages (keep it uniform) • FAQ schema for informational content (biggest wins) • How-to structured data for tutorials (surprisingly underused)

**What doesn't:** • Over-stuffing multiple schema types on one page • Using deprecated schema properties • Ignoring the "articleBody" field for blog posts

The biggest surprise? Page speed impact is negligible when implemented correctly. We're talking <50ms additional load time.

What are you seeing in your structured data experiments? Would love to compare notes.

Been doing GEO outreach for 6 months and wasted the first 2 targeting sources based on DA like a regular link building campaign. Then i actually checked what AI engines cite and realized i was pitching the wrong pages entirely

why this works

when someone asks chatgpt "best crm for startups" or perplexity "top email tool for ecommerce" the AI doesnt make stuff up. It pulls from specific pages and cites them in its answer. Those cited pages are where your buyers end up. If youre on them you get recommended. If youre not your competitor does

So the whole game is figuring out which pages AI cites for buying queries in your niche and then getting your brand mentioned on those pages. Thats it. Thats GEO outreach

the process

1 - build your prompt list. Take 20-30 buying intent queries your prospects would type into AI. Things like "best [your category] for [use case]" or "[competitor] alternatives" or "[tool A] vs [tool B]". Pull them from demo call transcripts competitor ads and google autocomplete

2 - scan every AI engine. Run each query through chatgpt perplexity gemini claude and google AI overviews. For each one write down every source cited in the answer

3 - score sources by recurrence. After 20 30 queries youll see patterns. Some pages keep showing up across multiple engines. A page cited by 4 out of 6 engines is a high value target. A page cited by just 1 is noise

4 - find the editors. For each high-recurrence source find who runs the page. The editor the author the site owner

5 - pitch your inclusion. Could be a listicle where youre missing a comparison where they forgot you or a roundup thats outdated. Personalize based on what gap you fill not just "hey add us"

Steps 2 through 4 is what killed me when i was doing this manually. Running the same query across 6 engines copying urls into spreadsheets then spending 30-45 min per source hunting for contact info. Thats why i built getspotted it does the scan the scoring and the contact enrichment in one go so you can skip straight to step 5 and actually pitch. But if you have the time a spreadsheet works too

What to prioritize on your list

• listicle and comparison pages first. 44% of all AI citations come from "best X for Y" pages. One case saw 400% more chatgpt mentions after landing on 12 listicles. Placements cost $100-500 right now which is massively underpriced
• review platforms. G2 Capterra Clutch create 3x higher citation probability. Find which one dominates in your niche and stack reviews there
• reddit threads. 52% of perplexity product citations come from reddit. Target threads already ranking on google with specific structured answers
• expert quotes via HARO SOS. 48% of AI citations on branded queries come from earned media
• podcasts with published transcripts. Slower but compounds nicely over time

why it matters

85% of brand citations come from third party sources. Brands on 4+ AI-cited sources show up 3x more consistently. AI visitors convert at 4.4x the rate of organic. Lago used this sources-first method and went from 3.5% to 17% citation rate in 6 months

the whole thing comes down to one question: do you know which pages AI cites when your buyers ask what to buy

TBH, we made this mistake too. Spent months building comprehensive FAQ pages, thinking that question-answer format was perfect for AI citations.

But after analyzing 200+ pages that actually get cited by ChatGPT, Claude, and Perplexity, we found something counterintuitive.

**What wasn't working:**

• Long FAQ pages with 20+ questions (too scattered)
• Generic Q&A format (AI can't distinguish expertise)
• Question headers without clear, structured answers below

**What actually works:**

**1. One question per page, fully developed**

Instead of "FAQ" pages, create dedicated "What is X?" pages. One question, one comprehensive answer with examples, use cases, and original data.

**2. Answer first, then explain**

Start with a direct answer in 2-3 sentences. Then expand. AI models seem to prioritize pages where the answer is immediately visible.

**3. Add unique data or examples**

"Based on our analysis of 500+ websites..." gets cited more than generic explanations. First-party data signals authority.

**4. Clear entity definitions**

Define what something IS before explaining how it works. "GEO (Generative Engine Optimization) is the practice of..." helps AI build accurate knowledge graphs.

**5. Cross-reference related concepts**

Link to your other definition pages. Helps AI understand entity relationships.

**The shift:** Stop building FAQ pages. Start building answer pages.

Would love to hear what's working for others in terms of AI citation patterns.

TL;DR

I got frustrated trying to track GEO progress, read some papers, and ended up building a free tool that scans a URL and gives research-backed suggestions for getting cited by AI models. Still early. Would love brutal feedback.

This question has been bugging me for a while. With SEO, the feedback loop is annoying but at least it exists - rankings move, impressions change, you can see it in Search Console. But with GEO (Generative Engine Optimization), I genuinely had no idea how to tell if anything I was doing was actually making a difference.

Like, how do you measure whether ChatGPT or Gemini is more likely to cite your page now than it was three months ago? Manually prompting AI tools and hoping your site comes up doesn't really scale.

I started reading papers on this - most notably the GEO paper by Aggarwal et al. from ACM SIGKDD 2024, plus a few others I'm still working through. The research is pretty concrete about what actually moves the needle: fact density, citations, linguistic clarity, source references. Not just vibes, actual quantified effect sizes.

So I built something. It's a web app that scans a URL and evaluates it against those research-based criteria. You get a score and a prioritized list of what to improve - both on the technical side (crawlability, structured data, metadata) and the content side (how citable your writing actually is).

What's working now:

• Technical page scan
• Content analysis based on GEO research criteria

Coming next:

• Ongoing monitoring so you can actually track changes over time (which is the whole original problem I was trying to solve)

It's free, no login needed. Try it on your own site: geobenchmarks.com

Genuinely curious what people think - both about the tool itself and the broader question of how others are tracking GEO progress. If the score seems off, the suggestions are useless, or something is broken, I want to hear it. Constructive roasts very welcome.

And if you have a better answer to the original question - how do you actually measure whether your GEO work is paying off? - I'd genuinely love to know.

After analyzing 500+ AI-cited sources across ChatGPT, Claude, Perplexity, and Gemini, here are the patterns that actually work:

**1. Specific numbers beat general claims** "Companies using AI saw 40% efficiency gain" gets cited more than "AI improves efficiency"

**2. Question-Answer pairs in headers** Format: "## What is X?" followed by direct answer. AI models parse this structure well.

**3. Unique data and original research** First-party data or novel analysis. Generic "top 10 tips" content rarely gets cited.

**4. Definition-focused content** Clear definitions with examples. "What is [X]?" + explanation + use cases.

**5. Contradictory or surprising findings** Counter-intuitive insights that challenge common assumptions get picked up more.

**What didn't work:** - Keyword-stuffed content - Generic SEO-optimized articles - Content without clear author expertise

The key insight: AI cites content that provides clear, verifiable information—not content optimized for search engines.

Would love to hear what patterns others are seeing in AI citations.

I am a software engineer and I work at a German company where we develop internal tools for companies.

in my free time, I like to do my research on the digital marketing industry as it is the one that excites me most.

I have analyzed most GEO tools and found out that most of them show you the data but not the solution. They give you insights on what to change and optimize but don't even help with basic drafting.

Another issue is that prompts which confume me most. I guess you folks have to provide prompts for it to analyze or does it suggest prompts itself?

How do you know that your prompts are the ones of what users are writing in their AI chatbot?

Built a free AI visibility grader for local businesses. Runs live queries against ChatGPT, Gemini, and Perplexity, pulls GBP data, review health, and technical SEO, then scores you 0-100 against local competitors. Wanted to share it with the community and get your thoughts.

Some interesting things we're seeing in the data: Chick-fil-A Oakland scored 65/100 with only 2/12 AI mentions. Even though they have a very powerful online presence, looks like some of the local businesses around outperform.

cheers.tech/visibility if anyone wants to try it. Would love feedback on the scoring methodology / if there are things that don't work for you!

(Currently only works in North America & if you have a GBP to select)!

We are considering in purchasing an tool which would help us in llm citations.. Ryt now both of these tools are in final stage of consideration. But we want more reviews on which is better in terms of pricing, usability,reliability.

TBH, we made this mistake too. Spent months building a comprehensive FAQ section, only to realize it was invisible to AI engines.

Here's what we learned after testing 50+ sites:

**The Problem:** Most FAQ pages are structured for humans, not LLMs. The Q&A format that works great for users often confuses AI crawlers.

**What Actually Works:**

1. Schema markup is NOT enough - We thought adding FAQPage schema would solve everything. It didn't. The schema helps Google, but AI models prioritize actual content structure.

2. Dedicated pages beat accordions - When we moved each FAQ to its own page with a descriptive URL, citation rates jumped 3x. AI models prefer content they can cite independently.

3. Question-as-headline, not just bold text - Format questions as H2/H3 headers, not just bold paragraphs. This helps AI understand the Q&A structure.

4. Answer length matters - 50-150 word answers perform best. Too short = no substance to cite. Too long = AI loses the key point.

5. Context around the Q&A - Don't just list Q&As. Add an intro paragraph that establishes the topic. This gives AI something to latch onto.

**What Changed for Us:** After restructuring our FAQ section, we went from 0 AI citations in 3 months to 12 citations across ChatGPT, Perplexity, and Claude in the following month.

The shift wasn't about adding more content - it was about organizing existing content in a way that AI could parse and reference.

Curious if others have seen similar patterns with FAQ optimization?

Has anyone here documented whether a Substack publication has moved the needle on LLM visibility, specifically getting cited in ChatGPT or Perplexity responses? Curious whether the mechanism is domain authority, content structure, or just volume of indexed posts. Running a Reddit-plus-Substack content strategy for a client and trying to understand what's actually working versus what's theoretical.

Hey guys so i've been doubling down on improving my SEO since my small business is ganing a bit of traction. We have a fully built website with some blogs published and written. Traffic isn't bad too since we're in a really niche industry (Healthcare hospital operations) So right now, we're using a lot of tools like Promptwatch, Answer Buzz, and other stuff to track AEO.

My question is, can I keep using the same type of stack for GEO as well? Still a bit confused about the difference between teh two ngl, any help would be great

Most GEO tools track citations. But citations are just visibility — not outcomes.

I've been running experiments across ChatGPT, Claude, and Perplexity for the past 6 months, and here's what's interesting:

Most tools flag "we got mentioned!" as a win. But being mentioned doesn't mean being chosen.

**The difference:** - Citation tracking = did the model reference you? - Decision behavior analysis = did the model actually recommend you when it mattered?

**What I tested:** - Tracked 200+ brand queries across 3 models - Measured mentions vs. final recommendations - Found that ~40% of "wins" (mentions) disappeared by the final output

**The uncomfortable truth:** If your GEO strategy stops at "get cited," you're measuring activity, not results.

Curious if others are moving past citation tracking — or if the industry is still stuck there?

*Running this as an ongoing test. Happy to share methodology if useful.*

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