New Metrics for the AI Era: Tracking Mentions, Citations, and Share of Voice

The digital landscape is changing, shifting away from fixed, ranked search results toward AI systems that generate answers probabilistically based on context and patterns.

AI Visibility Metrics: The Essential Framework for AI Search

For decades, marketers optimised for keyword rankings within a list of blue links. Today, users are getting more and more accustomed to receiving direct, synthesised answers from Large Language Models (LLMs) such as ChatGPT, Perplexity, and Google Gemini. This shift has elevated a new central pillar of digital presence: AI visibility metrics, a multidimensional set of measurements for how a brand is recognised, interpreted, and surfaced by AI systems.

The Core Metrics of the AI Era

In this paradigm, visibility is no longer defined by rank alone. Instead, AI visibility metrics are built on two foundational signals, each serving a different strategic purpose:

• Mentions
  A mention occurs when an AI model names a brand as a relevant entity, solution, or example within its generated response, without providing a direct link. Mentions reflect whether AI models associate brands with specific topics and narrative authority, making them a core component of AI visibility metrics.

• Citations
  A citation appears when an AI response includes a clickable link to a brand’s website as a source for a specific claim or dataset. Citations tend to drive higher-intent traffic from users seeking verification or deeper exploration and represent one of the most measurable outputs among AI visibility metrics.

These two primitives form the basis for more derived AI visibility metrics.

Share of Voice, Sentiment, and Consistency

Building on mentions and citations, AI Share of Voice (SoV) measures how frequently a brand appears relative to competitors across a defined set of prompts or model outputs. Unlike traditional SERP-based SoV, AI SoV reflects presence within generated answers rather than ranked lists, which extends the scope of AI visibility metrics beyond search rankings.

As AI behaviour is probabilistic rather than deterministic, two additional AI visibility metrics are important to track:

1. Sentiment
   Visibility alone is insufficient if brand mentions are framed negatively or inaccurately. Sentiment analysis within AI visibility metrics captures whether AI-generated references are positive, neutral, or adverse, which is essential for reputation management.

2. In-top Percentage
   Beyond whether a brand appears at all, AI visibility is also shaped by where it appears within an answer and how often it is included among the top recommendations. Brands that are surfaced early in an AI-generated response, or that consistently appear within the top set of options, tend to receive greater attention and recall.

Why These Metrics Matter

The transition to AI-driven discovery is changing how users engage with information. In some categories, impressions continue to rise while traditional click-through rates decline, reflecting the growing prevalence of answer-first interfaces. Website impressions are rising while traditional clicks are declining, a pattern we outlined in our earlier analysis of SEO vs. AEO, where visibility increasingly occurs within AI-generated answers themselves rather than through traditional click-driven discovery.

At the same time, traffic that does originate from AI-generated responses often arrives with clearer intent, as users have already received contextual grounding before clicking. According to this industry report, users searching with LLMs are 4.4 times more likely to convert than those using traditional search engines. Because generative systems compress the discovery process, being cited as a source increasingly resembles a new form of prominence within the search experience rather than a replacement for ranking itself.

These patterns are still emerging and vary by industry, but they help explain why tracking AI visibility metrics has become strategically relevant even as traditional performance indicators fluctuate.

The Shift to Prompt-Based Measurement

Capturing these dynamics requires moving beyond static keyword dashboards toward prompt-based measurement of AI visibility metrics. Instead of monitoring short query strings, measurement must reflect the natural-language questions users actually ask AI systems.

Modern visibility platforms translate mentions, citations, sentiment, and in-answer position into structured AI visibility metrics that can be tracked over time. This enables teams to identify where competitors are repeatedly surfaced while their own brand is absent, revealing actionable visibility gaps.

Organisations applying this approach are aligning marketing, content, and technical teams around AI-first discovery. Those that continue to measure success solely through traditional ranking and traffic metrics risk losing relevance in environments where AI visibility metrics, not clicks alone, determine discoverability.