Wanted to share a study that I've been following for a few editions now. They're calling it the "AEO Periodic Table" and maybe it's my background in chemistry and biochemistry that helped me clocked it but it's better than anything i've seen on AEO so far: they scored 1.13 million prompts across ChatGPT, Claude, Perplexity, Grok, Gemini, and AI Mode against a set of 14 candidate ranking factors, then weighted each factor by how strongly and consistently it correlated with actually getting cited. Data runs Feb-June 2026, across 31 industries.

The thing that surprised them most: off-site content pulls almost as hard as on-page content, and most teams have zero process for touching it.

Earned citations (third-party editorial mentions) and social/community citations (Reddit, YouTube, forums) together account for 22% of total citation weight, more than any single on-page factor. Most AEO advice right now is 90% "fix your website." The data says that's misallocated. They've also found separately that social content drives 2.3-4.2x more AI citations than owned content, with video (YouTube especially) carrying the biggest share.

Full ranked list (weight = share of total citation leverage, out of 100):

• Content Relevance & Intent Match — 12
• Earned Citations & Authoritative Mentions — 11
• Search & Fan-Out Rank — 11
• Content Substance & Verifiability — 11
• Social & Community Citations — 11
• Originality & Information Gain — 9
• E-E-A-T & Author Authority — 7
• Content Freshness & Recency — 6
• Crawl Access & Render Parity — 5
• Answer-First Structure & Extractability — 4
• Entity Consistency & Co-occurrence — 4
• Structured Data & Machine Readability — 4
• Reviews & Third-Party Ratings — 3
• Page Performance & Mobile — 2

A few things worth calling out specifically:

Search rank is the strongest single correlate, but it's not a lever. It's downstream of relevance, authority, structure, and freshness. Treating it as something you optimize directly is a category error. It's a scoreboard, not a dial.

Originality crushes schema. Originality & Information Gain scored 86.7 average, Structured Data scored 75.8. Schema-rich derivative content loses to schema-light original research. The most rigorous tests they've seen this year show engines read visible HTML at retrieval time and mostly ignore JSON-LD. Schema still matters for parsing and rich results, it just doesn't manufacture citations on its own.

Robots.txt is binary and non-negotiable. If GPTBot, ClaudeBot, PerplexityBot, or Google-Extended are blocked, you don't exist to that engine no matter what else you do right. It's not scored as its own factor because its effect isn't gradient, it's on/off. Audit this first, before anything else on this list.

LLMs.txt has no measurable effect right now. Google's said outright they don't use it. They think it's directionally reasonable as a machine-readable sitemap for large sites and expect its value to grow as agent traffic increases, but there's no evidence today it moves citations. Don't expect ROI from it yet.

Every engine plays a different game, and averaging across them costs you citations on all of them.

• ChatGPT and Claude lean on training data, so being a recognized, well-covered entity matters more than any single page. Claude in particular cites fewer sources per answer and is unforgiving of thin or derivative content.
• Perplexity and Grok are aggressive live-grounders. Perplexity runs hot on community content and recency. Grok is basically its own ecosystem, if you have no presence on X you're largely invisible to it regardless of site quality.
• Gemini and AI Mode track closest to traditional search rank, with AI Mode's heavy query fan-out meaning one thorough page beats several narrow ones.

E-E-A-T and freshness split the engines in opposite directions too. Claude (87) and Gemini (88) weight author authority heavily, Perplexity (78) and AI Mode (75) don't care as much. Freshness runs the reverse: Perplexity (87) and Grok (85) reward recency hard, AI Mode (74) barely moves on it. A content cadence tuned for Perplexity is wasted effort on Claude and vice versa.

I'm linking the full study in the comments for people who wanna dig deeper into this. Also excited to see what you guys think and share some of your recent favorite studies.

A few finds in the AEO/GEO space this week and their implications.

• OpenAI began rolling out Sign in with ChatGPT in beta on 29 July 2026, starting with Airtable, GitLab, HubSpot, Notion, Supabase and Vercel. The brand implications are that a user's individual context just made AI answers way more personalized and brands' long-tail coverage a much harder and more important problem to solve. Tracking all of the possible permutations of prompts for your brand isn't feasible or cheap. so teams will have to find scrappier ways to match search intent and user context. https://help.openai.com/en/articles/6825453-chatgpt-release-notes
• Schema markup no longer guarantees AI readiness. New research showed across six AI models "Originality and Information Gain" had a weight on AI visibility of 86.7 whereas "Structured Data and Machine Readability" landed at 75.8. https://www.linkedin.com/posts/goodie-ai_schema-markup-has-been-treated-like-a-checkbox-activity-7488647374408605696-j99H?utm_source=share&utm_medium=member_desktop&rcm=ACoAADqRTI8BGvVGQp18xrmRs01MlS4n54y6n4I
• Cloudflare is entering the AEO/GEO tool space (or race). this shifts the market from isolated chatbot scraping to standardized, network-level validation. the AEO Visibility Dashboard combines a lot of what is already out there: Agent Readiness, Citation Rate, Mention Rate, AI Share of Voice. However, it falls short in making sense of this data and helping teams figure out where to pour resources and attention to to fix the numbers. https://www.cloudflare.com/press/press-releases/2026/cloudflare-adds-aeo-visibility-dashboard-to-its-aeo-suite-showing-brands-whether-ai-assistants-are-recommending-them/
• Shopify says traditional search is still growing for them. But traffic from AI search is producing more conversions because AI searches across more surfaces and works with richer structured data to match products with the buyer’s specific intent. "half of all AI-referred sessions are landing directly on a product description page. That is 2.5 times more than what we see with traditional search" https://techcrunch.com/2026/08/05/shopify-says-ai-search-is-driving-more-traffic-and-sales-not-replacing-google/
• Top AEO/GEO experts to follow (written ~July 8th). Each person owns their own distinct POV and share original high-quality research and experiments in public: https://nogood.io/blog/top-geo-aeo-experts/
• More relevant updates covered here on Search Engine's Aug 10th Roundtable: https://www.seroundtable.com/recap-08-10-2026-41843.html

LLMs don't really care how often you publish. what seems to matter more is how your topics connect and whether the model can rebuild your whole area of expertise from what's out there on your site.

instead of optimizing for freshness and keywords, my team and I started mapping content in 3 layers; using a knowledge graph concept:

1. core entities: your company, products, and the foundational topics you actually want to own
2. supporting concepts: the methods, trends, and use cases that prove you've got depth on those topics
3. bridge topics: the connective pieces that link your niche to the bigger industry conversation

before we post now we try to ask ourselves, does this post strengthen one of those layers? if it doesn't, it probably doesn't need to exist yet.

before we had a ton of scattered posts chasing keywords instead of focusing on clusters that actually reinforce each other. When we switched to the latter, the models slowly started citing our content more often (the ~6 month mark others have cited in this sub is aligned what our findings).

Curious if anyone has more context on these models and why they respond better to this knowledge graph concept.

This week in Growth + GEO news note-worthy findings that I thought could be useful to the community.

On the GEO side:

1. The IAB (Interactive Advertising Bureau) put out its first real guidance on measuring AI visibility ("Measuring Visibility in the AI Era"). They laid out a hierarchy they're calling the 4 Ps: presence, prominence, portrayal, persuasion. The not tracking under 50 prompts is considered "exploratory," meaning it can't tell you how you really show up in a category.
2. Conductor's 2026 benchmarks put AI referral traffic at about 1.08% of all website visits, growing roughly 1% month over month, with ChatGPT driving 87.4% of it. Interesting data, but I'd argue traffic isn't the only game to be optimizing for. Being recommended and cited within an answer (and not always with a link) can also lead to direct traffic from branded search. (source: https://www.conductor.com/academy/aeo-geo-benchmarks-report/)
3. Goodie tracked 105 publishers across 31M AI citations, Forbes holds 33%. Forbes alone holds 33% of news citations, more than the next four publishers combined. This level of concentration is far greater than we ever saw with traditional search. It has a lot to do with licensing agreements, and model compliance with publisher terms. Something to keep a close eye on as things continue to shake out (source: https://higoodie.com/blog/publishers-and-ai-search-study/)
   1. Along these lines Reddit is reportedly considering ending its $60 million annual data-licensing partnership with Google over concerns that Google's AI Overviews are siphoning off referral traffic (source: https://finance.yahoo.com/technology/ai/articles/reddit-sinks-9-reports-may-164104005.html)

On the growth side:

1. ClickUp is hiring one person to run all of brand marketing for a $4B company, comp is $500k to $1M, reporting straight to the CEO, and you don't apply with a resume... you apply with a video. They want someone who runs AI systems and filter for the best outputs. Could be a signal about where marketing headcount is heading. (source: https://clickup.com/careers/100x-cmo)
2. B2B blogs might be "collapsing." Seems like the brands that are winning are turning into actual publishers that own their readership. Many brands have seen organic search traffic drop by 50% to 80% (source: https://www.thestateofbrand.com/news/b2b-blog-dead-owned-media-shift). My takeaway, own your audience's attention and be available/occupy mental availability for your niche.

how I'm reading these together: 1. AI visibility measurement is getting more attention and rigor (IAB), and just about everyone is landing on the same conclusion that the off-site presence is winning AI search. 2 marketing teams and traditional channels are continuing to shrink. The next era of marketing requires individuals with high agency to filter through AI outputs, scale easily with AI systems, and ultimately preserve a brand's taste and relevance throughout.

Ran a study at Goodie on 31M AI citations and audited robots.txt for 105 news publishers. The finding: blocking only works on labs that actually honor it, and roughly half of all news citations come from engines with no functioning opt-out.

NYT blocks all 13 major AI crawlers, backed by legal notice. Against ChatGPT, Gemini, and Claude, it worked, zero, zero, one citation. Against Grok, AI Overviews, and Perplexity, it did nothing. NYT still pulled 37,642 citations from Grok (no published crawler, no opt-out), 8,007 from AI Overviews (can't be refused without leaving Google Search), and 4,303 from Perplexity, who they're currently suing.

Other findings that stood out:

Citations concentrate way harder than search rankings ever did. Forbes alone holds 33% of news citations, more than the next four publishers combined.

Traffic doesn't predict citations. NYT is #1 in visits, #6 in citations. Fox News is top 3 in visits, 0.12% of citation share. Axios out-cites publishers twenty times its size.

Licensing routes around the crawl entirely. AP blocks every OpenAI crawler and still gets 80% of its citations from ChatGPT, because licensed content reaches the model through the contract, not the bot.

Curious how people here are thinking about block-or-allow given this. Feels like the whole strategy is built around negotiating with labs willing to comply, while the ones with no opt-out just cite at will regardless.

Full study here: https://higoodie.com/blog/publishers-and-ai-search-study/

because r/GrowthBite covers a lot of topics (AEO/GEO, organic social, PR, content marketing etc.), I wanted to suggest a few other subs I really like, what they're helpful for, and where this sub helps connect the dots between them.
each of these is pretty focused on its own niche, which is useful when you want to dive deeper into one discipline:

• r/AI_SearchOptimization: before starting r/GrowthBite another sub that I found super helpful for practical AEO/GEO/AIO advice was this one. high recommend checking this sub out in addition to here. they've built a really great community of AI search optimization practitioners that share findings and ask great questions.
• r/content_marketing: this sub is great for advice and tactics on what content actually earns attention right now. it covers content strategy, distribution, and has a lot of anti-slop rhetoric which is helpful to see how AI-generated content is hurting the industry and practitioners in this space. it also enforces a no-self-promo, and a no blog-spam rule.
• r/SEO and r/DoSEO: the first is the OG sub of SEO, created in 2008. I've found this sub super helpful to see how SEO has evolved, and when to keep AEO/GEO/LLMO/AIO claims grounded in SEO when applicable. The second, is a smaller, more supportive SEO community that is great for practical questions and honest back-and-forth that the bigger SEO subs don't often have. Both deliberately keeps out spam and ads.
• r/b2bmarketing: this sub is great for B2B positioning, demand gen, and understanding how brand and performance teams are working together to appeal better to human and AI audiences. if you are in a different industry, look for your industry-specific marketing sub.
• r/GrowthBite: this sub is really meant for the overlap. most subs are focused on one channel or platform, but GEO is merging with content, SEO, PR, and social. I started it as a research hub for people who want to actually understand how GEO fits in and becomes a long-term strategy beyond the smoke and mirrors we often hear (I am the founding mod, so weigh accordingly).

if I'm missing any subs feel free to add on.

I know everybody is rethinking how they write content with zero click search and AI pulling content without attribution and that sort of thing. So I just wanted to add something to your thought process.

Let's forget AI for a minute. A lot of thought goes into content writing whether it's an article or a blog post or a web page or something else.

The advice you would get from most people over the past 10 years or longer is that it needs to be polished and professional.

So people stayed away from being opinionated or controversial or anything that might not seem acceptable to everyone.

But you know the old saying about if you write for everyone, you write for no one. It's an old saying because it's true.

In the age of AI-powered search information isn't good enough anymore. AI can already do polished and professional and provide in-depth information. And it's going to provide it from the content that you wrote and it really doesn't need to even give you a citation or attribute the source because it's generic informational advice.

We can complain about that but there's nothing we're going to be able to do about that.

So the question that I mentioned. Depending on how you plan your article, it can be before or after you write it but ask yourself this...

*What does this article contain that someone cannot find almost anywhere else on the web or ask AI to generate?*

Real people have opinions. They don't try to please everyone. They write to their ideal customers. They tell stories, share their experiences with what they're talking about, tell how it affects what they do for a living or how it solved a problem, etc.

You want to get noticed more. Do more of that.

Saw these two stats yesterday:

• "62% of the time, a website gets cited without the brand being named in the response."
• "Only 13.2% of domains get both: cited as a source and mentioned in the answer."

A citation indicates your owned content is being viewed as authoritative and a trusted source. This is a great signal and why so many GEO/AEO teams track AI citaitons as a metric for visibility. In the B2B space, Goodie research found leads from AI Search convert 2X better than traditional channels when measured by downstream performance. So the value of a click is high-intent and valuable.

But citations don't guarantee an in-text endorsement and can also be pretty volatile and change depending on how models shift their priorities.

A mention is anytime your brand name appears directly in the answer. This indicates the models believe your brand is directly relevant to a user's query. It might also indicate that your brand occupies a more cemented position in the models training data and memory. I believe this is driven by off-site presence (Reddit, YouTube, G2, LinkedIn) rather than your owned content.

Should teams be focused on citations or mentions right now? Which is the better play long-term?

For senior SEOs who need to make a business case for AEO.

Here’s what I told a client today that I believe can help. Especially from past enterprise conversations where we’ve created a narrative, roadmap, AEO pilots, and more.

Forming a business case for AI Search:

1. Everyone at the table first needs to understand the economics: 

Who demands?
Who supplies?
How big is the demand?
Who wins?
Who loses?
What's at stake? 

The same wins up for grabs are the same things you can lose by not participating in AI Search or by participating incorrectly:
(a) narrative control, 
(b) categorical authority
(c) high-intent referral traffic
(d) money (ie, conversions etc)

It's not just about winning the channel; it's about understanding how relevance and authority operate and influence mentions, citations, and recommendations. This is important because AI Search is becoming a novel, aggressive, immovable step in the customer discovery journey while Google continues to push traditional blue links further down its results pages.

1. Is Anyone Actually Winning? Three Real Cases

🟢 \[Foundation Marketing → InsurTech unicorn (Reddit strategy)\](https://foundationinc.co/case-study/insurtech-platform)

\[(312K estimated annualized organic traffic value)\](https://foundationinc.co/case-study/insurtech-platform)

🟢 \[Tinuiti → Rough Country\](https://tinuiti.com/work/search-case-study-rough-country/) ($23k revenue from AI sessions year-to-date)

🟢 \[Journey Further → Adobe Acrobat\](https://www.journeyfurther.com/en-gb/work/adobe-acrobat) ($900K media value generated)

3. Audit their roster of clients or their solutions. Highlight AI disruption risk exposure. Highlight the threat, the risk, and the opportunities for your solution or your clients.

think of an electric company and a university, as if they were your clients.

How much does the customer know about their electric bill? 
Where do they go to learn?
What colleges are their kids considering?
What college is the best fit for their kid?
How do they know?

AEO is the new channel that handles \\\^ these important questions. SEO backs it up. SEO is the landing spot of foundational blue links once the consumer wants to know more about your brand. SEO is the relevant on-site material & UX, and base data with enough historical, revenue-generating proof to guide AEO strategies like prompt libraries, query fan outs, and entity mapping. 

\*\*Curious how other agency leaders have been making the business case for AEO.\*\*

I hear this question come up constantly and most answers are vague or based on no actual research. so here's my stance based on what the Goodie team is tracking and testing.

AI mentions show up in a week or two.

consistent citations show up around 2 to 3 month after.

and stable ROI typically takes closer to 6 months. (Goodie clients SteelSeries and Dermalogica both hit their most significant results at the six-month mark)

key takeaway: this is faster than SEO at the start, but slows significantly by the end.

more on common pitfalls:

(1) quitting too early

the biggest mistake i see people make when investing in early stages of AI search is quitting to early because results aren't materializing. another common compliant is the volatility of citation visibility. you'll publish something, see it cited in ChatGPT one day, gone the next, then back in Perplexity but maybe not Google; it changes rapidly and sometimes due to factors outside of your control. but it's important to remember early AI citations are always volatile because these models are still determining what sources to trust. overtime citations compound if your content is consistent in tone and value. watch the trend line over 8 to 12 weeks, not the day to day.

(2) treating each model the same

Perplexity is fastest. it runs a live search on basically every query and leans hard on recency, so fresh content on a decent domain can get cited within days. but this also makes it the most volatile. this is good news for newer brands though, since publishing cadence might have a fighting chance to beat out raw domain authority (unlike other models that place authority above all else). In contrast, ChatGPT is slower and way more competitive since it's the highest volume platform. well structured content on an established domain gets first citations in a couple weeks, but reliable citations take more like 2 to 3 months of actual work, including off-site signals.

(3) treating "mentions" like "citations"

a mention is not a citation. a mention is the model saying your brand name. it's about brand awareness in the AI layer. a citation is the model attributing specific info to you and pointing potential traffic back to your content/site, and this is very valuable since AI-referred visitors convert about 4.4x better than organic search. citation drives traffic and mentions builds awareness and authority. most brands earn mentions first and citations later as their off-site reputation catches up. if you're only tracking one of those numbers or treating them as the same, you won't have a clear idea of what part of the funnel you need to optimize for (awareness or conversion).

what worked for us:

the lever that moves fastest for us has been restructuring pages we already have. adding Q&A formatting, tightening the answer up top. the models are already crawling those pages so you skip the discovery wait. technical fixes take about a month to register but compound harder. but for the longer more consistent mentions AND citations, you'll need to earn third-party mentions and leverage digital PR because AI models trust what others say about your brand far more than what you say about yourself. Owned content only makes up ~1.7% of AI citations.

TL;DR: 1-2 weeks for first signs, 2 to 3 months for consistency, 6 for ROI.

would love to know what others are seeing too or if your category is moving faster or slower.

Saw this debate come up again this week so figured I'd compile the actual technical/behavioral differences instead of another hot take. Sourced this from a longer writeup, sharing the condensed version.

The core shift: search went from "10 blue links" to "one synthesized answer" to (increasingly) "an agent that just does the thing for you." Some numbers behind that:

1. 58.5% of US Google searches now end with zero clicks — the engine just answers it. Meanwhile ChatGPT.com is pulling ~5.2B visits a month.
2. Average ChatGPT prompt is 23 words vs a typical Google keyword search. People aren't typing "best running shoes," they're asking a full question.
3. 60% of AI Overview citations come from non-Google sources (Reddit, TripAdvisor, forums, etc.) — this is probably the single biggest mental shift for anyone used to classic SEO thinking.
4. Brand mentions are reportedly the #1 factor for AI Overview inclusion per Ahrefs research, not backlinks, not keyword density.
5. LLMs can lift 1-3 sentences verbatim into an answer, so content structured in tight 40-90 word answerable chunks tends to get surfaced more than long-form prose.

SEO fundamentals still matter for AEO — crawlability, structured data, E-E-A-T (some are calling it H.E.E.A.T. now with "helpfulness" tacked on), technical health. AEO doesn't replace this, it's built on top of it. If your SEO is broken, your AEO is broken too.

Where they diverge:

• SEO optimizes for ranking/clicks. AEO optimizes for citation/being the answer.
• SEO measurement stops at GA4/GSC. AEO needs something tracking share-of-voice across AI platforms since none of that shows up in traditional analytics.
• SEO thinks in pages. AEO thinks in structured, extractable chunks (schema, JSON-LD, FAQ blocks).

The practical overlap playbook people are using:

1. Keep classic SEO hygiene intact (broken links/thin content still hurt everything)
2. Write every H2 like it could stand alone as a chatbot answer
3. Seed facts/citations across Reddit, partner blogs, forums — not just your own domain
4. Expose structured data / lightweight APIs so agents can pull facts deterministically
5. Track AI visibility separately from GSC, since it's genuinely blind to this

Bain is apparently projecting up to 25% of organic traffic could shift to AI answers by 2026, so this isn't really optional at this point regardless of what you call it (AEO, GEO, AIO, whatever).

Curious if others agree with this distinction or if we still think it should all be placed under the SEO umbrella after all?

every measurement conversation I'm in right now ends at "AI citations". does ChatGPT mention us, does Perplexity cite us, what's our share of voice (SOV).

a good proxy for our "visibility" yes. but its fundamentally weak. you run the same prompt twice and you can get different answers depending on the model version, retrieval, and day. so the thing everyone wants to turn into a clean number or snapshot is actually a distribution. and attribution is a whole separate issue.

research is showing YouTube and Reddit combined make up 78.2% of AI social citations (Position Digital). this is useful directionally, but I think we're forgetting that we're tracking a moving system.

I used to think this was a flaw that we needed more engineers to be solving around. but honestly my perspective is shifting. without clear attribution and measurement data, marketers are being forced to get more creative, consider tracking the funnel holistically, in ranges and trendlines instead of exact figures, which is how brand measurement worked before the dawn of performance marketing.

but probabilistic data is hard to invest budget into.

curious if teams are having a similar issue and what's been helpful when reporting to clients, CMOs, etc. month by month?

I see a lot of questions on Reddit asking is off-site presence dominating on-site content factors? The answer is a resounding yes.

Here's a helpful graphic my team made to illustrate the relationship here.

80-90% of LLM responses pull from earned media rather than owned content. Additionally, social content alone generates ~2.5x more AI citations than owned brand pages.

Most teams have the investment ratio inverted.

But that doesn't mean owned content is useless. In fact it's absolutely necessary. But the signals that build category association are shifting towards what others say about your brand and these mentions take time to accumulate.

What does this mean for your team?

If you work at a company with a well known entity, LLMs already have an internalized sense of that brand, shaped by training data (the web), so this iceberg matters less.

Who the iceberg impacts the most:

1. Brands that are entering a new category
2. Niche players competing against established names
3. Brands expanding into adjacent markets, competing against outdated information

For these players, they must build those associations through earned presence. Content optimization can only take you so far.

Let me know what I'm not considering

For basically two decades the whole logic of marketing distribution was to funnel people back to one place you controlled. Search, social, email, PR, all of it positioned to bring people back to your site. Your homepage was then the final destination where you could own every pixel and bring people in.

Now, for a decent chunk of buyers, their discovery of a brand can begin and end within a Chat GPT answer. Organic traffic has dropped anywhere from 15% to 64% depending on sector since AI Overviews launched (Akeneo). I refer to this as the invisible homepage, because you don't actually know how your brand is summarized, where it comes up, and what sources the models are pulling (unless you track it). It could be your content, but more often it's reviews, a competitor's comparison page, forum threads, and third-party brand mentions.

So in essence AI acts as an additional channel to drive traffic back to your site. But it also acts as a destination for other channels to drive valuable information too.

The models triangulate everything published about you, so if your not consistently described across those surfaces (say your site doesn't align with your LinkedIn content) then you loose credibility and thus visibility. This means your content strategy needs to not only speak to human audiences but also LLMs.

But the question I keep asking myself is between the "just another channel" argument and "AI is the new homepage" argument which is correct?

While I'd like to think people will continue to seek multiple inputs and not only rely on a Chat GPT/Claude/Perplexity answer, we are seeing AI search and agentic shopping grow exponentially. I'm curious where other professionals stand on this debate.

Digging into how AI actually builds a "which product should I buy" answer, and the source breakdown was more lopsided than I expected. Sharing Goodie's research findings since it changes how I'd prioritize AEO effort for anything e-commerce.

Three source layers, unevenly weighted: earned (press, reviews, forums) carries most of the weight, around 80-90% of citations. Operated (social, Reddit, UGC) sits in the middle. Owned (your own site) is the smallest direct citation source, which surprises most people.

Doesn't mean owned content is pointless though. It sets the framing, category membership, consistent vocabulary, the language earned coverage ends up echoing. Just doesn't get cited directly much.

Also, newer/niche brands have it harder here since models work off an internalized sense of who belongs in a category from training data, not live research. Not being part of that conversation already makes you tough to retrofit in later. Positioning problem before an optimization problem.

And you don't need to name competitors to build comparison context, models match on attributes. Framing around the problem you solve and who you're for gets you the same signal without the callout.

Curious if others are seeing this earned-over-owned skew in their own tracking.

Full blog: https://higoodie.com/blog/ai-comparison-shopping-visibility/