If you work on growth or SEO for an insurance brand that is not State Farm, you have probably typed your own category into ChatGPT and watched it recommend seven companies, none of them yours. The instinct after that is to go rewrite a landing page. That is almost always the wrong first move, because you have not measured anything yet. You do not know which questions you lose, which engines you lose them on, or which sources those engines are actually reading.
This is a measurement problem before it is a content problem. Here is how to instrument it.
Geology ran the version of this test that most teams never get around to. In its June 2026 insurance study, 100 insurance buyer prompts went through ChatGPT, Perplexity, Gemini, and Google AI Overviews, tracking 15 insurers. Incumbents took 78 to 82 percent of insurer mentions on ChatGPT, Perplexity, and Gemini, and 66 percent on the more balanced Google AI Overviews.
Two details from that study are the ones you will build your measurement around:
You cannot act on either of those until you have your own version of the numbers for your own brand. So build it.
Do not test one query. Insurance buyers ask in predictable shapes, and your visibility differs wildly across them. Build 40 to 100 prompts across four buckets:
Store them as data, not as a doc, so the run is repeatable:
[
{ "id": "cat-01", "bucket": "category", "prompt": "best renters insurance 2026" },
{ "id": "niche-04", "bucket": "niche", "prompt": "renters insurance for a freelance photographer with gear" }
]
For every prompt on every engine, record two fields: were you named, and which sources were cited. That second field is the one most audits skip, and it is where the strategy actually comes from.
{
"prompt_id": "cat-01",
"engine": "perplexity",
"brands_named": ["State Farm", "Lemonade", "Progressive"],
"you_named": false,
"sources_cited": ["reddit.com/r/insurance/...", "nerdwallet.com/...", "moneygeek.com/..."]
}
Run it manually for a first pass if you have to. If you want it repeatable, the Perplexity and Gemini APIs return citations directly, and for ChatGPT and AI Overviews you can script the browser. The point is a table you can rerun next month and diff.
From that log, derive:
Now the fixes are obvious because the data pointed at them.
Geology's breakdown of how insurance brands win in AI answers covers the content side of this in more depth, and its insurance AI-visibility service is the same measure-then-act loop run end to end. But the sequence starts here, with measurement. You cannot fix a visibility gap you have not located, and for insurance the gap is almost never on the page you were about to go rewrite.
Mehul Jain writes about generative engine optimization at Geology, where the team studies how AI engines decide which brands to recommend. This piece draws on Geology's June 2026 insurance visibility study.