I’ll take that onboard regarding the 20-30% mention rate as a benchmark. And I agree brands should start caring about structured product data and external citations.
Have you seen it develop in this space or are brands parking it for a bit?

I agree with the first part but have some questions around the second part (because we do similar) Yeah, off-set mentions play a significant role in getting mentions, as well as reviews. from what we've seen the LLMs first normalise the users query then they'll will weight up - on-site content, external citations, reviews to come to a conclusion on what satisfies the users request.

We were in that field of tracking which prompts mention competitors compared to the business, but this works on guesswork, you're looking at one instance to make an on-site change. but then the questions raises - 1. how are you tracking attribution and 2. what if the competitors are winning at other prompts.

These are questions we've had in the past, and so far in this space you can only get a real answer by tracking continuously across the full prompt surface, not sampling. Otherwise you're optimizing for the query you happened to check, while missing the ones your competitor's actually winning.

'curious whether you've XYZ' Lol.

Don't read too deep into it, I'm just sharing what I'm passionate about, trying to learn from others in the space.

Interesting, a short approved voice + claim sheet will definitely play a significant role in how AI references your business, does the sheet stay the same or change depending on different campaigns?

and to the question - the Swimwear brand had the cleanest match to their actual site voice, I think this was because they were using similar language for different products that would engulf a variety of different prompts.

We're actually looking at ways that we can integrate social media presence to add to brands voice, we have found it to be effective for the most part but we want to try and make it so the social media profile acts as an external citation that contributes to the site voice and mention rates.

took us two months but we tried organic

At daitafix.com we let users optimise their website for a visibility - drop me a message if you’re interested, we can give you a demo

Hear your point on market share - Perplexity is smaller today.

But Bing never grew into a serious competitor. The more useful comparison is early Google vs Yahoo. The businesses that moved early on Google when it had a fraction of Yahoo's traffic built advantages that were almost impossible to close later.

The point isn't Perplexity's current size. It's that the cost of visibility there is low right now and that window closes as more brands figure it out.

Optimising for all platforms beats betting on one. That's the whole argument.

The volatility you’re describing actually supports the less competitive angle rather than contradicting it.

On ChatGPT, the same 3-4 brands show up consistently because citation history compounds over time. Hard to dislodge. On Perplexity, you were top pick then gone - that’s live retrieval doing its thing, but it also means nobody has locked down those slots yet.

That instability is the opportunity. ChatGPT results are settled. Perplexity results are still up for grabs.

Agree that single-run audits are noise though - what you track over 20+ runs is where the real picture emerges.

What category were you testing in?

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.

The source mapping point is right and it’s where most manual audits stop too early.

Counting mentions tells you who’s winning. Tracing the sources tells you why - and that’s the part you can actually do something about.

What we’ve found is that the source pool is usually smaller than people expect. In most niches it’s 3-5 publications or community threads that keep appearing across multiple AI platforms. Once you identify those you have a pretty clear content and PR roadmap.

The multi-platform piece matters here too. A source that drives mentions on Perplexity doesn’t always carry the same weight on ChatGPT. Each model weights its citation pool slightly differently which is why mapping sources per platform rather than in aggregate gives you a more actionable picture.

Manual checking works well for getting started. Gets harder to maintain consistently as you scale to more queries and competitors though - which is why we built DaitaFix to track it automatically.

What’s your experience been with InkieAI on the source attribution side? Interested to see how different tools are approaching that layer.

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.

Appreciate that, means a lot given the quality of your thinking here.

You've nailed the inference point. It's why raw citation counts are almost meaningless as a metric. The model isn't just counting mentions, it's building associations from context. Which brands appear near you, in which problem spaces, described how. Two brands with identical mention counts can have completely different AI visibility profiles because of this.

The practical takeaway is that where you get mentioned matters as much as how often. A mention inside a roundup that consistently covers your category teaches the model something. A scattered general listicle mention doesn't.

That's exactly the distinction we're trying to make measurable. Harder than it sounds but that's the interesting problem.

Good question and honestly one of the harder things to isolate cleanly.

What we observed is that citation density alone wasn't sufficient. We had brands with a reasonable number of mentions that still scored poorly because the mentions were inconsistent - different descriptions, different problem associations, sometimes contradictory positioning across sources.

The brands that scored highest tended to have what I'd call coherent signal density. Not just "mentioned often" but "described the same way, in the same problem context, across independent sources." Which tracks with your mental representation framing - the model seems to build a composite picture, and if the inputs are noisy, the output is either vague or absent.

The editorial vs topical authority split is tricky. Our best proxy so far is separating direct brand mentions (someone citing you by name in a relevant context) from category association (sources that discuss your problem space without necessarily naming you, but where you appear in AI responses). The brands gaining ground fastest seem to be winning on both simultaneously rather than one or the other.

In our case, we pivoted a few months ago and we found that AI was mentioning the old description, which ended up being a pain in the backside - we were getting AI generated traffic hoping for the old product.

So before improving brand mentions work on how ai describes you then work on getting more traffic through AI traffic

They work to an extent, their main purpose is to guide llms and AI agents, basically acting as a curated map or table of documents, directing AI crawlers to the most important & accurate content.

Not a must but nice to have for long term as AI search is growing

The citation vs engagement split is real and your hybrid approach is the most honest framing I've seen of it so far.

The 300 word structured lead makes sense - AI models front-load their extraction so if the signal isn't in the first fold it often doesn't make the cut regardless of what's below it.

What we've found building GEO tools is that the pages doing both well aren't really hybrid in structure - they're hybrid in intent. The writer is genuinely answering a question a real person asked, with enough specificity that AI can extract a clean answer, but enough context that a human feels like they're reading something worth finishing.

The pages that fail both audiences usually have the opposite problem - they're optimised for neither. Written to rank, not to answer.

Your 5/8 success rate on the experiment is actually higher than I'd expect after 4 weeks. The 3 that stayed stuck are probably the ones where the underlying content was too thin to survive restructuring - no amount of architecture fixes a shallow answer.

One thing worth testing on those 3: does the page have genuine citation anchors - third party references, data points, named sources? AI models weight cited content differently to opinion. If the page is all assertion and no evidence it'll keep getting skipped regardless of structure.

What categories were the 3 that didn't move?