What is AI Recommendation Score?
AI Recommendation Score is a composite metric that estimates how strongly AI systems recommend a brand, product, or service across relevant customer questions.
It rolls multiple signals, frequency, position, citation coverage, and competitive context, into a single, comparable number teams can track over time and benchmark against a defined competitive cohort. Instead of reading dozens of prompt-level outputs, marketing and ecommerce teams get a clear answer to one question: when AI is asked, how strongly does it recommend us?
What the score considers
- Recommendation frequency , how often the brand is named in response to relevant buyer-intent prompts.
- Recommendation position , where in the answer the brand appears, and whether it leads or trails the recommendation list.
- Recommendation quality , the framing, qualifiers, and strength of the recommendation language.
- Competitor presence , which competitors appear alongside or instead of the brand.
- AI Share of Voice , share of recommendations within a defined competitive set.
- Citation strength , the credibility and breadth of sources AI engines lean on when recommending.
- Category visibility , coverage across the full set of category, problem-led, and comparison prompts that define the market.
Example score breakdown
Illustrative sample only. Real scores are produced from a benchmark on a defined prompt set and competitive cohort.
Why it matters
AI engines are quickly becoming the recommendation layer for consumer and B2B buyers. Brands need a simple, defensible benchmark to understand whether those systems are recommending them, or recommending competitors instead.
AI Recommendation Score gives teams a single number to align executive conversations, track progress quarter over quarter, and prioritize the work that moves recommendation outcomes most.
Who uses it
How to improve your score
- Improve product and category pages with clear, complete information AI engines can rely on.
- Increase credible citations across reviews, publications, and category-defining sources.
- Build comparison content for the head-to-head prompts buyers actually ask.
- Strengthen reviews and ratings across the platforms relevant to your category.
- Publish thought leadership that signals authority in your space.
- Standardize brand messaging so AI systems describe you consistently across sources.