Recommendation share is the proportion of all brand recommendations made across AI-generated answers to a defined prompt set that name a specified brand. It is a metric defined by blimpp, published here with its full measurement method. It answers the commercial question behind AI visibility: when an engine advises buyers in your category, how much of that advice points at you.
In one sentence
Recommendation share is your slice of the recommendations an engine actually hands out, which is closer to revenue than being cited or mentioned.
How recommendation share is calculated
Recommendation share = (recommendations naming the brand ÷ total brand recommendations in the answer set) × 100
The denominator counts every brand recommendation across all responses to the prompt set, so if one answer recommends three brands it contributes three to the denominator. The numerator counts the recommendations naming the target brand. What qualifies as a recommendation, as distinct from a mention or citation, follows the rules in measuring citations, mentions and recommendations; sampling follows the repeated-run protocol.
Worked example
Twenty commercial prompts are each run five times on one engine, producing 100 responses. Across those responses the engine makes 240 brand recommendations in total. The target brand accounts for 36 of them, a recommendation share of 15%. Illustrative example: blimpp benchmark data is added to this page as studies publish.
Why recommendation share matters
Citations measure whether your pages feed the answer; recommendation share measures whether the answer sends buyers to you. The two regularly diverge: a brand can be recommended on the strength of third-party sources without its own site being cited once, and a frequently cited publisher earns no recommendations at all. For a commercial brand, recommendation share is the number the board understands, and the one competitors are silently taking from each other inside answers nobody screenshots.
What affects recommendation share
Evidenced factors: the brand’s presence and framing across the third-party sources the engine retrieves for the prompt class, since engines compose recommendations from retrieved evidence; and whether the brand is retrievable at all for the category’s sub-queries. Factors with practitioner evidence: consensus across independent sources recommending the brand for the same use case, recency of those sources, and how precisely the brand’s positioning matches the prompt’s stated need. These are treated as working hypotheses until tested.
Related concepts
References
- Zhang, K. et al. (2026), From Citation Selection to Citation Absorption: arxiv.org
Author: Harpal Singh · Last reviewed: 7 August 2026