How to think about local AI recommendations
Start with the customer’s question, not a promise to decode a secret ranking formula.
A specific question needs specific information
“A gym near me” and “a beginner-friendly strength gym with evening classes” describe different needs. For your own planning, list the situations where your business genuinely fits. Those are better questions to test than prompts that quietly steer the assistant towards your name.
Search access is not a guarantee
OpenAI distinguishes its search crawler from its training crawler. Allowing search access is a technical consideration, not a guarantee that a particular answer will recommend your business. We check relevant access settings without presenting crawler traffic as customer demand.
Keep facts and assumptions apart
If an answer cites your menu, record that citation. If it does not explain why it chose a business, do not invent the reason. A competitor appearing in one answer does not prove that its reviews, schema or a particular article caused the result.
Use a repeatable observation plan
Our suggested starting point is a small set of unbranded customer questions, an agreed location and more than one observation over time. Save the date, the assistant, the exact prompt, the answer and any sources. Keep the test setup as consistent as practical and note changes.
Put the findings to work
Look first for verifiable gaps: a missing service page, an obsolete price, unclear access information or a broken booking link. These are useful to fix even before a later answer changes. Treat more speculative explanations as hypotheses to test, not facts.
Source for the platform-specific guidance. Our suggested workflow is NearMention’s own approach, not an official ranking formula.
See how this applies
to your business.