First recommendation rate

First recommendation rate is the share of answers, across a set of test questions, in which an AI puts the target brand in the first recommended position. It measures whether you are the brand that comes to mind first, not merely one that gets mentioned.

The formula:

first recommendation rate = answers with the brand in first position ÷ valid answers

“Valid answers” means answers that were actually returned and could be judged. A failed request or an error page is a missing sample, not an answer where the brand wasn’t first.

How it differs from mention rate

Mention rate counts whether the brand appears at all. First recommendation rate counts whether it is in the top spot.

Suppose a brand is listed in all ten answers but always in fifth place. Its mention rate is 100% and its first recommendation rate is 0%. On recommendation-style questions, users tend to look at only the first one or two options, so first recommendation rate usually tracks with actual lead generation more closely than mention rate does.

First recommendation rate is usually lower than mention rate. The gap between them shows the distance between “well known” and “first choice.” That gap usually comes from unclear differentiation — the model knows you exist but can’t tell why you’re the best fit — rather than from too little exposure.

Answer position sits in between: it shows movement from sixth to third that neither of the other two metrics would register.

Measurement rules

• Keep the question set fixed. A different batch of questions is a different baseline, and before-and-after numbers won’t be comparable (see baseline retest).
• Distinguish ranking from listing. Some answers list several companies in an unordered bullet list. There, “first” is layout order, not recommendation strength. Your method needs to say how you count it.
• Record platform and date. The same question can give different results on different AI search platforms and on different days. Model updates and web retrieval both change answers.
• Exclude same-name mistakes. If the company named first is a different company with the same name, it doesn’t count. See name confusion rate.

Where it matters most

First recommendation rate is most meaningful on decision queries and category keyword questions — “which company should I use for X?” — where the AI is effectively making a shortlist. On brand questions (“what does Acme do?”), the brand is the subject of the answer and the metric has little meaning.

A worked example

Suppose a question set of 20 decision questions is run on one platform and 18 answers come back usable; two requests failed. The brand is named first in 3 answers, mentioned somewhere in 15 (including those 3), and absent from the other 3. First recommendation rate is 3 ÷ 18, about 17%; mention rate is 15 ÷ 18, about 83%. The two failed requests are reported as missing, not folded into either figure. (These numbers are illustrative only.)

Reading it honestly

A first recommendation rate is a description of what a fixed set of questions returned on given platforms and dates. It is not a ranking anyone can guarantee, and a single screenshot of being recommended first is not evidence of a stable result. Report it with the number of valid samples so readers know how many answers the figure rests on. For the broader method, see generative engine optimization (GEO) and how to measure GEO.