A useful monthly answer engine optimization (AEO) and generative engine optimization (GEO) report explains whether comparable answers changed, whether their claims are accurate, and which evidence justifies the next action. Its headline should survive a check of the underlying answers and denominators. A larger count of recommendations can coexist with an unchanged baseline and a more serious product error.
Connect an observed AI referral to a qualified enquiry by preserving the captured session reference when the enquiry is accepted, carrying the enquiry ID into the customer relationship management (CRM) system, and joining it to a dated qualification decision. Keep those records separate. A referral identifies an observed source; a submitted request establishes an enquiry; a later assessment establishes whether the resulting opportunity qualifies.
Measure recommendation frequency as the share of eligible answers that positively propose a named option. Record explicit comparative preference separately, together with the context in which it is expressed. List position supplies neither label on its own. For answer engine optimization (AEO) and generative engine optimization (GEO), reporting those distinctions prevents a change in presentation from becoming a claim about preference.
Record the test conditions before interpreting a change in an AI answer. The exact prompt, answer surface, search configuration, location provenance, language and conversation context belong beside the saved response. A screenshot of a missing brand, without those conditions, cannot establish that its visibility declined.
A branded question gives an assistant a business to evaluate. A discovery question asks the assistant to find suitable businesses. Combining their answers into one recommendation rate conceals that difference. Keep separate cohorts, meaning groups of prompts with the same eligibility rules, before claiming that a brand is being discovered.
An agency can appear in every answer to a prompt that already contains its name and still be absent when a buyer asks whom to hire. A useful prompt set exposes that difference. For answer engine optimisation (AEO) and generative engine optimisation (GEO), work concerned with a business’s presence in AI-generated answers, agency discovery should be tested against the buying decision and the conditions that could change the shortlist.
Ask an AI where its recommendation came from, then investigate the sources it supplies. A follow-up explanation can help you find evidence for a claim. It does not, by itself, establish which pages were retrieved before the original answer or why one option was selected. Save the original answer before continuing, and judge the later explanation against records you can actually examine.
An AI citation supports a claim only when the cited material establishes that claim at the same scope and strength. Open the source, split the answer into separately checkable statements, compare each statement with a specific passage, and decide whether to keep, narrow, replace or withhold it. A working link and a familiar publisher name do not complete that check.
A citation can support a sentence about an AI search feature while establishing nothing about the agency named beside it. In answer engine optimization (AEO) and generative engine optimization (GEO), factual support, a brand mention and an agency recommendation are separate findings. A useful citation report preserves those differences.
An answer can praise a company and still tell the buyer to choose something else. Consider this fictional illustration: Meridian Tools sells catalogue processing software, and a retailer needs capacity for 30,000 text-only catalogue updates per hour. Under the same specified workload and configuration, Meridian Standard supports 40,000 updates per hour, Meridian Entry supports 10,000, and an unnamed alternative supports 20,000. These are invented product facts for the illustration.