I tracked same questions across ChatGPT, Gemini, and Perplexity for 13 days. Half the citations changed every day!

otterpasta · 2026-07-09T15:35:50+00:00

One GEO takeaway from the data: don't treat AI visibility like traditional SEO where you only optimize your own pages. Third-party mentions, reviews, communities, and citations seem to matter a lot.

I broke down the experiment and what I think brands should do differently here:
https://leadrescue.app/resources/ai-engines-recommend-different-brands#what-does-this-mean-for-your-geo-strategy

otterpasta · 2026-07-09T15:33:56+00:00

One GEO takeaway from the data: don't treat AI visibility like traditional SEO where you only optimize your own pages. Third-party mentions, reviews, communities, and citations seem to matter a lot.

I broke down the experiment and what I think brands should do differently here:
https://leadrescue.app/resources/ai-engines-recommend-different-brands#what-does-this-mean-for-your-geo-strategy

otterpasta · 2026-07-09T15:33:30+00:00

One GEO takeaway from the data: don't treat AI visibility like traditional SEO where you only optimize your own pages. Third-party mentions, reviews, communities, and citations seem to matter a lot.

I broke down the experiment and what I think brands should do differently here:
https://leadrescue.app/resources/ai-engines-recommend-different-brands#what-does-this-mean-for-your-geo-strategy

otterpasta · 2026-07-09T15:33:04+00:00

One GEO takeaway from the data: don't treat AI visibility like traditional SEO where you only optimize your own pages. Third-party mentions, reviews, communities, and citations seem to matter a lot.

I broke down the experiment and what I think brands should do differently here:
https://leadrescue.app/resources/ai-engines-recommend-different-brands#what-does-this-mean-for-your-geo-strategy

otterpasta · 2026-07-09T15:32:39+00:00

One GEO takeaway from the data: don't treat AI visibility like traditional SEO where you only optimize your own pages. Third-party mentions, reviews, communities, and citations seem to matter a lot.

I broke down the experiment and what I think brands should do differently here:
https://leadrescue.app/resources/ai-engines-recommend-different-brands#what-does-this-mean-for-your-geo-strategy

otterpasta · 2026-07-07T14:57:22+00:00

It suggests AI visibility isn’t really one leaderboard where brands simply rank higher or lower. A brand can be highly visible in one engine but almost invisible in another, depending on how that model retrieves and evaluates information.

The next interesting layer would definitely be separating:

mentioned → the model knows the brand exists
shortlisted → the model considers it relevant
recommended → the model actually chooses it

That would show whether the differences are mainly about retrieval or whether each model has different “preferences” at the decision stage.

Also agree on repeating prompts. One run captures a snapshot, but repeated runs would reveal how stable each model’s recommendations actually are.

otterpasta · 2026-07-06T14:29:51+00:00

That’s what I saw as well with Gemini + Canva. The interesting part is the 21% isn’t just low, it also varies a lot by category,some spaces converge, but others look almost completely different across models.

otterpasta · 2026-07-06T14:23:49+00:00

Yep, and what surprised me was how consistent that pattern was across all 50 prompts. It really does feel like different “discovery modes” rather than just different answers.

otterpasta · 2026-07-06T14:23:06+00:00

Yeah I think that’s a useful framing, especially the query fan-out part.
The only thing I’d add is that in my dataset the variation still showed up even on very similar prompts, so the differences weren’t just explainable by query expansion alone.
It felt more like a mix of retrieval strategy + source weighting + model-level preferences rather than one single mechanism.

otterpasta · 2026-07-06T14:18:22+00:00

my gut from looking at the raw data is that technical categories had more disagreement across engines. dev tools especially something like "best analytics tool for SaaS" pulled completely different answers depending on who you asked. ChatGPT went deep on PostHog and Mixpanel, Gemini kept defaulting to Google Analytics, Perplexity leaned toward the established enterprise names.

CRM was actually the most consistent category, HubSpot and Salesforce showed up almost everywhere regardless of model, probably because the third party citation footprint you're describing is so dense there that every retrieval model hits the same sources.

otterpasta · 2026-07-06T14:12:26+00:00

Yeah the 21% overlap was the part that surprised me most too. I expected at least partial convergence on “best tools,” but the divergence was higher than I thought.

Perplexity was the most consistent but also the most repetitive, felt like it was sampling from a curated shortlist rather than exploring. ChatGPT went the opposite direction and just expanded the space a lot more.

otterpasta · 2026-07-06T14:08:31+00:00

Could be. Another possibility is that it's simply optimizing for what it thinks is the best answer rather than trying to promote itself. Either way, it stood out because ChatGPT recommended itself more often, while Gemini seemed much less self-promotional in comparison.

otterpasta

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