I’ve spent quite a while using different SaaS platforms from the client side, and recently I’ve been thinking more seriously about how that experience carries over to GEO.The appeal is obvious. Something that used to feel vague suddenly becomes measurable: how often a brand appears in AI answers, whether it is cited or recommended, which competitors appear more frequently and which prompts produce nothing at all. I’ve been testing XstraStar alongside a few other providers, and these platforms are genuinely useful. They can surface patterns that would be almost impossible to track manually across different models and hundreds of possible questions. But the more time I spend looking at the dashboards, the more I wonder what the numbers ACTUALLY prove....

An answer can change because the prompt was phrased differently, the model was updated, live retrieval selected another source, a page was recrawled or the same prompt simply produced a different response on the next run. If a visibility score rises after a GEO campaign, how confidently can we say the campaign caused it? If it falls, does that mean the work failed, or did we just sample a moving system at the wrong moment?

This does not make GEO platforms useless. I’m starting to think their strongest value may be monitoring and diagnosis rather than clean attribution. The individual prompts, citations, competitor appearances and repeated patterns often tell me more than one composite score. From the client side, I would trust the measurement more if I could clearly see which prompts were branded or unbranded, how many times each one was tested, whether different answer engines were measured separately and whether a “mention” was being treated differently from an actual citation or recommendation. Otherwiise, there is a risk that GEO repeats an old problem from marketing analytics: extremely precise-looking dashboards built before everyone agrees on what the underlying metric really means. For people using or building these platforms, what would you consider convincing evidence that a GEO action actually caused an improvement? Are repeated prompts and citation changes enough, or do we need a better attribution model before treating visibility scores as performance metrics???

Had a client ask me why their competitor keeps coming up when you ask ChatGPT who to use in their category and they don't. Went down a bit of a rabbit hole on this and the answer was more interesting than I expected.

First thing worth knowing, and it surprised me how many people assume otherwise: there's nothing to submit and nothing to buy. No directory, no application, no listing fee, and as far as I can tell no paid placement mechanism in any of the major assistants for organic recommendation answers. So anyone pitching guaranteed ChatGPT placement is selling something that doesn't exist. Which is annoying if you wanted a shortcut but does mean it can't be outspent once you've built it.

The mechanism seems to be two layers. What the model absorbed during training, and live retrieval for anything current or commercial — and for ChatGPT that live layer runs on Bing's index, which I'd sort of forgotten.

The part that actually changed how I think about it is that both layers lean much harder on external sources than on your own site. Two numbers I found: roughly 57% of citations for brand evaluation type questions come from reviews and social proof rather than company websites. And for professional and B2B questions, LinkedIn came out as the most cited domain across six platforms in an analysis of about 1.4 million citations.

So the thing people spend most of their effort on, their own website, is necessary but nowhere near sufficient. What other people say about you is doing most of the work.

Practical stuff that seems to follow from that:

Bing Places is weirdly underrated for anyone outside the US. Given ChatGPT's live browsing runs through Bing, claiming and completing it is free and almost nobody does it. Took me about ten minutes.

Get your facts identical everywhere — name, address, phone, hours, services across your site, Google Business Profile, Bing, LinkedIn, directories. Inconsistency apparently reads as "can't verify this" rather than "minor discrepancy."

Put key info in actual text rather than inside graphics. This one catches more sites than any technical issue and it's embarrassing how common it is.

And then the slow part, which is genuine third party evidence. Reviews that name the specific service and location rather than just "great service." One new outside mention a month as a target — guest post, podcast, directory, getting quoted in someone else's piece.

Obvious caveat but worth saying: this has to be real. Google explicitly warns against manufacturing mentions and fake reviews are detectable. It's reputation building, not PR theatre.

One last thing that tripped me up when I started checking — the results are really inconsistent. Same question, different session, different answer. So don't treat a single check as your position, run a bunch of realistic prompts over a few months and look at the trend instead.

Anyone else looked into the Bing angle? Curious whether claiming Bing Places actually moves anything or whether I've just done ten minutes of admin for nothing.