AI Search Metrics: Mentions, Citations, Share of Voice & Sentiment
AI visibility programs tend to fail in one of two ways: they track only a single vendor score, or they drown stakeholders in dozens of unstable metrics. A better approach is a small hierarchy that separates presence, prominence, sourcing, perception and business impact.
Presence: mention rate
Mention rate asks a binary question at prompt level: did the answer name the brand? It is easy to understand and useful for trend reporting, but it does not tell you whether the mention was prominent, favorable or linked.
Sourcing: citation rate and cited domains
Citation tracking identifies when your domain—or another source—is referenced in the answer. Separate “brand was mentioned” from “our site was cited.” A brand may be recommended based on third-party sources without receiving a direct citation.
Competitive position: AI share of voice
Share of voice compares your presence with competitors across a defined prompt set. The denominator matters. A score based on branded prompts, category prompts and a giant vendor database can each mean something different, so document the prompt universe behind the metric.
Perception: sentiment or framing
Automated sentiment can be directionally useful, but teams should inspect important responses manually. “Best for enterprises but expensive” contains both endorsement and a drawback; one sentiment label can flatten that nuance.
Coverage: topics and prompt clusters
Topic-level coverage prevents aggregate metrics from hiding weak areas. A brand might have strong visibility in educational prompts and almost none in purchase-stage comparisons. Break results by funnel stage and topic.
Outcome: referral traffic and conversions
When AI platforms send trackable referral traffic, connect those visits to landing pages and downstream outcomes. This does not capture zero-click influence, but it is the closest bridge from visibility measurement to business performance.
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Sources & verification
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