What is AI answer share?
Also called recommendation share — the proportion of buyer-intent prompts where AI engines actually recommend your brand. Here's exactly how it's calculated, with a worked example.
AI answer share (recommendation share) is the proportion of tested buyer-intent prompts, across a fixed set of engines, where an AI answer actively recommends or shortlists your brand. It is always reported as a numerator over a denominator — e.g. 12 / 40 prompts — before any percentage.
Answer share is the headline number of an AI visibility run. It answers a single buyer question at scale: when someone asks an engine for the best option in your category, how often does the engine say you? It is one of the four metrics in our framework, alongside citation share, omission rate, and misrepresentation rate — see what is AI visibility.
How AI answer share is calculated
Every answer in a run is classified into one of three states for your brand:
- Recommendation — the engine endorses or shortlists you as an answer to the prompt. Counts toward the numerator.
- Mention only — your name appears but without endorsement (e.g. listed as one of many, or referenced in passing). Tracked, but does not count as a recommendation.
- Omission — you are absent from an answer where you could plausibly fit.
The denominator is the total number of scored prompt-by-engine cells. With N prompts × M engines, you have N × M cells. Answer share is:
Answer share = recommendations ÷ (N prompts × M engines)
Because the denominator is the full grid of prompts and engines, a low share can mean two different things — you're being omitted, or you're being mentioned without endorsement. That's why we always report omission rate next to answer share rather than in isolation.
Worked example: 12 / 40
Take a fictional category — "AI sales assistants for seed-stage SaaS." Suppose a run uses 8 buyer-intent prompts × 5 engines = 40 cells. After scoring the captured answers for your brand:
- 12 recommendations — your numerator.
- 5 mention-only — named, not endorsed.
- 23 omissions — absent where you could fit.
Your answer share is 12 / 40 prompts (30%). Reported in full, the same run reads:
| Metric | Numerator / denominator | Rate |
|---|---|---|
| Recommendation (answer) share | 12 / 40 cells | 30% |
| Mention-only | 5 / 40 cells | 12.5% |
| Omission rate | 23 / 40 cells | 57.5% |
The story is sharper than "30%": you're recommended in under a third of answers and outright omitted in over half — so the priority is closing omissions on prompts you clearly fit, not just converting mention-only answers. Two competitors in the same run might sit at 24 / 40 and 19 / 40, which tells you how far the gap runs.
Pitfalls to avoid
- Reporting a percentage without counts. 3 / 10 and 30 / 100 are both "30%" but carry very different confidence. Always keep the numerator and denominator visible.
- Counting mentions as recommendations. Being name-dropped in a list of twelve is not an endorsement. Conflating the two inflates the number and hides the real gap.
- Too few prompts. Under ~20 prompts, one missed answer swings the percentage by 5 points. Use a stable set of 25–50.
- Mixing engines into one average without breakdown. A 30% blended share can hide 55% on Perplexity and 10% on ChatGPT — different fixes for each. Report per engine as well as blended.
- Non-buyer-intent prompts. Padding the set with prompts no buyer would ask makes the denominator soft. Every prompt should map to a real persona and funnel stage.
Answer share describes what engines said in captured answers — nothing more. We don't promise a target share, and we improve it only by improving the public evidence engines read: accurate comparison pages, earned third-party citations, clear product facts. No paid placements, no hidden content.
A note on sources
The calculation above is AI Ranking Pro's measurement framework. The choice to score against a fixed, reproducible prompt set reflects standard survey-style sampling practice — show the raw counts and the sample, not just a rate.
- [1]AI Ranking Pro measurement framework — recommendation vs mention vs omission scoring over an N × M prompt-by-engine grid, defined in our methodology.
- [2]Sample run with full numerators and denominators — see the sample report.
AI answer share FAQ
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It's reported as a fraction first, then a percentage — e.g. 12 / 40 prompts (30%). The numerator and denominator both matter: 3 / 10 and 30 / 100 are the same percentage but very different confidence. We always show the raw counts so you can judge the sample.
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A mention is any time your brand name appears in an answer. A recommendation is when the engine actively endorses or shortlists you for the buyer's question. We track mentions separately, but answer share counts only recommendations, because that's what moves a buyer's shortlist.
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Enough that the result is stable, not anecdotal. A typical audit uses 25–50 buyer-intent prompts across 3–5 engines. Fewer than ~20 prompts and a single missed answer swings the percentage too much to trust.
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Not honestly. The number reflects what engines actually say in captured answers. You improve it by improving the public evidence engines read — accurate comparison pages, earned third-party citations, clear product facts — not by paid links or hidden text, which we never use.
Know your real answer share.
Monitoring reports your recommendation share as raw counts and per engine each month, with captured answers behind every number.