u/ErikCodes

I am seeing more local SEO conversations split into two separate questions:

1. Can the business rank in maps for the obvious local query?
2. Can AI/search answers understand when that business should be recommended?

Those are related, but I would not measure them the same way.

For local service businesses, a lightweight AI visibility check could be:

1. Pick 10 to 20 buyer-intent prompts Examples: "best emergency plumber near [city]", "who should I call for water damage in [city]", "alternatives to [competitor]", "how do I choose a [service] company".

2. Separate mentioned from recommended Being named in an answer is weaker than being recommended with a reason.

3. Record the evidence used Look for review language, service pages, category wording, location pages, third-party mentions, directories, FAQs, case studies, and local proof.

4. Compare against map pack strength A business might be strong in the map pack but unclear in AI answers, or clear in AI answers but weaker on proximity/category/review signals.

5. Track the same prompts monthly One-off screenshots are interesting, but the useful signal is whether changes to reviews, pages, citations, and positioning move the same prompt set over time.

My bias is that AI visibility for local SEO should not become a separate magic checklist. It is mostly a measurement layer on top of the basics: clear entity information, service/location relevance, reviews, third-party proof, and pages that answer buyer questions directly.

How are people here tracking this today? Are you treating AI visibility as its own deliverable, or folding it into local SEO reporting?

AEO/GEO reporting is getting messy because a lot of tools seem to collapse the whole problem into "was the brand cited or not." That feels too shallow for actual SEO strategy.

The metrics I think are more useful:

1. Prompt intent coverage: informational, category, alternatives, comparison, local/service, and vendor-selection prompts should be separated.

2. Recommendation share: who is actually recommended when the prompt has commercial intent, not just who gets mentioned somewhere in the answer.

3. Framing: what attributes the answer associates with each brand. Example: enterprise, cheap, agency-friendly, local, technical, risky, trusted.

4. Source dependency: which third-party pages, review sites, directories, PR mentions, docs, schema, or comparison pages appear to influence the answer.

5. Stability: whether the answer is consistent enough over repeated runs to be worth optimizing for.

6. Gap mapping: whether missing recommendations point to entity clarity, proof, page structure, authority, or positioning issues.

The big question for me is how to make this actionable without turning it into another vanity dashboard. If you are reporting on AEO/GEO already, what are you measuring that actually changes your SEO roadmap?