Core idea
What this page covers
A team usually searches for "AI recommendation share" after hearing it in a meeting — for AI recommendation share, nobody in the room actually agrees on what it means.
SOV: any mention counts The goal for AI recommendation share is to stay concrete enough for a marketing team to act on, not just define it at a high level.
Reader intent
Questions this page answers
Teams usually land on AI recommendation share when trying to make a practical decision about AI recommendation share, not when they want a definition in isolation — the questions below on AI recommendation share are the real evaluation paths this page answers.
Along the way, this guide also covers adjacent themes such as ai recommendation share, what is ai recommendation share and why it matters, what is ai recommendation share for brands, ai recommendation share vs ai share of voice difference, how to measure ai recommendation share marketing, ai recommendation frequency tracking b2b, so the page helps both category discovery and deeper implementation work.
Strategic reframe
What this reframes
The metric that hides inside AI share of voice
SOV: any mention counts
The buyer intent filter
What makes a prompt a "recommendation prompt"?
How recommendation share is calculated
Same methodology as SOV but filtered to recommendation-intent prompts only
Key topic
The buyer intent filter
AI recommendation share usually starts as a definitions problem in most teams. The real payoff of understanding AI recommendation share comes later, once it changes planning and budget decisions. What makes a prompt a "recommendation prompt"?
Examples: "What's the best tool for...", "Which platform should I use...", "What do most companies use for... How to categorize your prompt library by intent
Evidence to gather
Proof points that make this strategy credible
These are the data points and category signals for AI recommendation share that should strengthen AI recommendation share before it's treated as a serious competitive asset in a high-intent SERP.
FAQ
Frequently asked questions
What is AI recommendation share?
AI recommendation share is intent-weighted presence: the share of buying-intent prompts, such as 'what should I use for X?', where an AI model actually recommends your brand. Share of voice counts any mention, while recommendation share counts only mentions made at the decision moment.
How is recommendation share different from share of voice?
Share of voice counts any mention, including informational answers. Recommendation share counts only responses where the model is recommending. A brand can have high SOV and low recommendation share if it is never suggested to buyers.
What counts as a recommendation prompt?
Buyer-intent questions such as 'What's the best tool for X?', 'Which platform should I use?' or 'What do most companies use for X?'. Categorize your prompt library by intent so you can filter to these.
How can you improve recommendation share?
Publish product comparison content that gets cited, aggregate review signals from sites such as G2 and Capterra, and build category leadership signals like awards, analyst coverage and case studies.
