6 KPIs to measure visibility in AI search

AI score

A score from 0 to 100 based on how often your brand is mentioned in tracked prompts and how prominently it appears in AI answers.

In Good Tracking, we display this in a timeline view where you can easily see how it develops over time compared to competitors across tracked prompts.

Mentions

The total number of times your brand appears in AI answers for your tracked prompts. Each occurrence counts as a mention.

Mentions give you concrete numbers on your actual presence in AI answers. While AI score considers both frequency and placement, mentions show the raw volume, how many times your brand actually appears when users ask relevant questions.

Positivity index

A score from 0 to 100 showing how positive or negative AI answers are when they mention your brand. A higher score means more favorable sentiment overall.

Being visible is not always enough. How you are presented plays a crucial role. The positivity index shows whether AI models associate your brand with positive or negative attributes, which directly affects how potential customers perceive you.

Competitor ranking

Shows how visible you are compared to your competitors.

In Good Tracking, we present this in an overall leaderboard that compares your brand’s AI visibility with competitors across tracked prompts. The table shows each brand’s position, AI score, share of voice, and mentions per AI model.

The competitive perspective puts your results into context. You may have strong numbers, but if your competitors consistently rank higher or are mentioned more often, you lose potential customers at the critical discovery stage.

LLM visibility

Which LLMs you are visible in.

Different users rely on different AI models. Some use ChatGPT, while others use Google’s services such as AI Mode, AI Overview, and Gemini. By knowing which models mention you, you can identify where you have strong positions and where gaps need to be filled.

Share of voice

The share of AI answers that mention your brand compared to competitors.

Share of voice quantifies your portion of the total “AI conversation” in your field. A low share means competitors dominate the narrative, even if you are technically being mentioned.

Prompt analysis lets you go beyond the numbers and read the actual AI responses. Here you can identify which aspects of your offering are highlighted, which competitive advantages are communicated, and where there are gaps between how AI describes you and how you want to be described.

The KPIs above give you numbers, but to truly understand your AI visibility it helps to read what the AI models actually say about your brand.

A prompt analysis lets you move beyond aggregated data and examine the actual AI responses word for word. Here you see not only that you are mentioned, but how you are mentioned, which product features are highlighted, which use cases are communicated, and in what context your brand is presented.

This is where you find the most useful insights: when AI consistently misses one of your most important competitive advantages, when a competitor gets praise for something you also offer, or when the description of your brand does not match your actual positioning. These gaps do not appear in an AI score or mention number, they require reading and analyzing the content.

A deep analysis is not about reading every response every day, it is about regularly reviewing a sample to understand patterns, identify opportunities, and spot problems before they affect your results.

Measuring your visibility in AI search requires multiple perspectives. AI score and mentions give you an overview of your presence, while positivity index and competitor ranking show how you are presented and where you stand in relation to competitors. LLM visibility helps you understand where your strengths and gaps are.

Together, these KPIs provide a solid understanding of your AI visibility.