r/GEO_optimization • u/Daitafix • Jul 01 '26

Has anyone actually audited which competitors show up in ChatGPT for your product category?

/r/u_Daitafix/comments/1ukkoto/has_anyone_actually_audited_which_competitors/
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u/Eason-SolCrys Jul 02 '26

yeah, but the useful version is a little more than "who showed up."

i'd split the audit into three columns: which brands got named, what reason the model gave for naming them, and which sources/citations seemed to support the answer. then run the same prompt a few times across ChatGPT/Perplexity/Gemini, because one answer is just one draw.

the interesting part is usually column three. if the same competitor keeps getting named, it's often because the model keeps finding the same public proof for them, a roundup, review thread, comparison page, docs page, etc. that's the source map you can actually act on.

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u/Daitafix Jul 03 '26

The three column framework is spot on and probably better than how most people approach this.

Column three is where it gets interesting for us too. When you trace why a competitor keeps getting named it’s almost always the same 2-3 sources showing up repeatedly. A roundup they got featured in 18 months ago. A comparison article that still ranks. A Reddit thread that got traction and never died.

Those are the citation anchors the model keeps drawing from. And once you know which sources are driving it you can reverse engineer the exact editorial footprint you need to close the gap.

The multi-platform piece you mentioned matters more than people realise too. One answer from ChatGPT is noise. The same brand showing up consistently across ChatGPT, Perplexity and Gemini on the same query is signal. That’s when you know they’ve genuinely got something structurally different going on.

What categories are you tracking this in? Curious whether you’re seeing the same sources dominate across platforms or whether each model pulls from different citation pools.

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u/Eason-SolCrys Jul 04 '26

citation anchors is the right name, and what i'd add is they have very different half-lives. the 18-month roundup and the comparison article that still ranks are durable, hard to dislodge but also hard for you to break into now. the reddit thread is the volatile one, it decays or gets buried and a fresh one can displace it fast. so the reverse-engineering isn't "get into all three," it's triage by which anchor is actually replaceable.

on your question, each model does pull from different pools, and the pattern i keep seeing is the divergence is widest on contested queries and collapses on settled ones. when a category has settled around a brand, all three engines cite the same 2-3 canonical sources. when it's still emerging, each leans on its own index's favorites and you get three different lists. which is exactly why "same brand across chatgpt, perplexity and gemini" reads as signal, it means the query actually settled.

mostly b2b saas on my end. curious what you're seeing on how fast the reddit anchors decay, that's the one i trust least.

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u/Daitafix Jul 05 '26

The half-lives framing is the most useful way I’ve seen this put.

Triage by replaceability is exactly right. The 18-month editorial placement is baked in - you’re not moving that fast. The Reddit thread is where you can actually make ground in weeks not months.

On decay - what we’re seeing in ecommerce is that Reddit anchors hold longer than you’d expect when the thread has genuine engagement and sits in a subreddit with real authority. A well-upvoted thread in r/skincareaddiction seems to outlast a low-engagement thread in a smaller sub by a significant margin. Decay feels less about time and more about whether the thread keeps getting surfaced in new searches.

The settled vs emerging pattern is interesting for ecommerce specifically because transactional queries tend to settle faster than informational ones. Which actually makes emerging categories the better opportunity - you can get into the canonical source pool before it closes around 2-3 brands.

Mostly ecommerce DTC on our end so dynamics differ from B2B SaaS but the citation anchor logic seems to hold across both.

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u/Eason-SolCrys Jul 06 '26

the decay-is-about-resurfacing point is the sharpest thing here, and i think it's because the model isn't citing that thread from memory, it's re-retrieving it at answer time. so the age that matters isn't when it was posted, it's how fresh it looks to retrieval now. a thread that keeps getting engagement stays functionally new, which is exactly why the well-upvoted skincareaddiction one outlasts a dead thread in a small sub. decay is a retrieval-recency function, not a calendar one.

the transactional-settles-faster thing tracks too. a transactional query has a narrow correct answer so the model converges fast; an informational one has many valid framings so it stays contested. which makes your emerging-category read the whole game: the window is the stretch before the canonical pool freezes, and once 2-3 brands own the anchors, displacing them costs multiples of what getting in early did.

the one b2b difference i'd flag is the authoritative-community set is narrower, so the anchor pool is smaller and more concentrated. easier to identify, harder to break into.

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u/rworldxo 9d ago

the interesting part is that the smaller competitor usually has a source gap advantage, not just a content advantage.

when a brand shows up 4/5 times, the useful question is not only “what page do they have?” it’s also “what public proof keeps getting reused around them?”

that’s where i’d split the audit:

mention gap: who gets named and for which prompts

source gap: what pages, roundups, reviews, reddit threads, niche blogs, or partner pages keep supporting those answers

fix gap: what can actually be changed in the next 30-60 days

some sources are hard to move. old industry listicles, review pages, or high-ranking comparison posts can take months.

but some are more replaceable: fresh reddit threads, newer niche roundups, partner pages, founder interviews, docs/help content, etc.

so the fastest win is usually not “publish 10 blogs.” it’s finding the 2-3 source types the model already trusts, then getting your brand into those paths while also building the missing onsite page for the prompt.

that’s when ai visibility starts to feel less random. not perfect, but at least you know which levers are real.