AI Visibility Tracker: Methodology & Metric Reference

What the AI Visibility Tracker Measures

As more people turn to AI assistants for research and recommendations, visibility within these experiences is a meaningful driver of brand recognition, reputation and trust. The AI Visibility Tracker from 3BL monitors how often and how prominently your brand appears in responses generated by top AI platforms.

Reputation Drives Measurement

The AI Visibility Tracker measures how companies show up in AI-generated answers through the lens of reputation.

Reputation is a collective perception built from public signals: reporting, news coverage, stories and what others say about a company. AI models learn from and retrieve those same signals when they answer questions.

The AI Visibility Tracker measures how companies show up in AI-generated answers through the lens of reputation. AI models learn from and retrieve those same signals when they answer questions. And unlike search demans, public signals are something companies can shape through consistent storytelling and distribution. Measuring AI visibility through reputation connects the metric to the work comms teams already do.

How the framework is structured

Every prompt the tracker runs belongs to a topic, and every topic sits under one of seven reputation pillars. The structure follows the pillar-to-topic model used by established reputation and ESG frameworks such as RepTrak, SASB and MSCI. See AI Visibility Tracker Definitions for the full list of pillars and topics.

Each topic is tested from the same set of angles, so results are comparable across topics:

• Which companies lead on the topic
• How companies in an industry compare
• Which companies AI recommends
• Examples of companies doing it well
• Critical or negative questions
• Questions that name the company directly

Every prompt is also tagged by the audience most likely to ask it, such as investors, talent, consumers or media.

How prompts are scored

Standard prompts: The same core prompts run for every company in an industry, so peer benchmarks are apples-to-apples.

Custom prompts: Run alongside the standard set to cover a company's specific programs, products or priorities.

Branded prompts: Prompts that name the company are excluded from core scoring, so scores reflect whether AI brings a company up without being asked about it.

The AI Visibility Score 

The AI Visibility Score is the primary metric in the tracker. It is a single composite index, scored on a 0-100 scale, that summarizes your company's overall prominence in AI-generated responses.

Score Components

The AI Visibility Score is calculated across three dimensions, all scored out of 100:

Note: These weightings may change over time as the product develops.

Query Coverage
Query Coverage measures the percentage of tracked AI responses in which your brand receives any mention. It is formatted as a percentage and reflects how broadly the brand appears across the full set of queries, regardless of how many times it's mentioned within any single response.

Position Score 
Position Score captures how prominently your brand is mentioned within AI responses across tracked queries on a 100-point scale. Mentions that appear earlier in a response, or that anchor a recommendation, carry more weight than passing references. 

Share of AI Voice
Share of AI Voice measures the rate at which your brand is mentioned across all tracked responses, relative to the total volume of brands mentioned. In the context of the Visibility Score, this metric serves as the Share of Voice component, representing a given brand’s share of the overall conversation related to their industry and topics of interest.

How the AI Visibility Score Is Calculated

The AI Visibility Score is calculated by multiplying each component by its weight and summing the results:

Visibility Leadership Indicator

In addition to the numeric score, the AI Visibility Tracker includes a Leadership Indicator classification that represents where your score falls relative to others. 

Supporting Metrics

Supporting metrics within the AI Visibility Tracker provide more information about what’s driving your score, the sources cited in conversations about your brand, and how you compare with industry peers across platforms and topics of interest. 

Rate Metrics

Rate metrics express mentions and citations as a proportion of total responses. This makes it possible to compare performance fairly across time periods or competitors, even if query volume changes.

Mention Rate
Shown as a percentage, the mention rate tallies how often your brand name appears in tracked AI responses, regardless of whether it is cited as a source. 

• Company Mention Rate: Rate at which your brand is mentioned across all tracked responses.
• Competitor Mention Rate: Rate at which other brands in the defined competitor set are mentioned across tracked responses.

Unlike Query Coverage, this metric counts volume. If your brand is mentioned three times in a single response, indicating the response was more significantly focused on you, that contributes three times more than a single mention would.

The AI Visibility Tracker also uses raw mention counts to support metrics such as Share of AI Voice.

Citation Rate
Shown as a percentage, the citation rate tracks how frequently a brand is cited as a source within a response. Beyond a simple mention, citations indicate the AI response positioned your brand as a source of information, either with attribution or a link back. 

• Company Citation Rate: Rate at which your brand is cited as a source across all tracked responses.
• Competitor Citation Rate: Rate at which other brands in the defined competitor set are cited as a source across tracked responses. 

Note: Broken or inaccurate links may appear due to simulated citations from older LLM models or stale search results.

Sentiment Score

The Sentiment Score reflects the tone of AI-generated language when your brand is mentioned. 

How the Sentiment Score is calculated

The Sentiment Score is measured at the response level and centers around language used in association with the target company, not with competitors, the industry, or problems in general. Sentiment is not measured at the mention level. Every mention of the target company in a single response will be tagged with the same sentiment score. 

The response-level scores are averaged in the tracker to produce a single Sentiment Score that reflects how your brand is discussed most often on a given AI platform or in association with a given topic. 

Importantly, the Sentiment Score does not simply reflect positive or negative keywords. A response can mention problems without reflecting negative sentiment toward the brands addressing those problems. For example, a response that includes the phrase “climate change is devastating, and Apple is leading with net-zero pledges" results in positive sentiment for Apple, not negative sentiment simply because a problem (“climate change”) and a negative keyword (“devastating”) are mentioned. 

3BL’s AI sentiment model analyzes whether the company's actions are favorable, not whether the topic itself is positive.

Positive (Score: 1, or 100%): Your company is portrayed favorably
Example: "Levi Strauss has reduced emissions by 40%."
Example: "Nike has committed to ending child labor in supply chains."

Negative (Score: -1, or -100%): Your company is portrayed unfavorablly
Example: "Target has faced multiple data breach lawsuits."
Example: "BP's oil spill in 2010 caused massive environmental damage."

Mixed (Score: .5, or 50%): Your company receives both praise and criticism, a likely scenario that follows the real-world nuance brands navigate each day .
Example: "Apple leads in accessibility features, but has been criticized for supply chain practices."

Neutral (Score: 0): Your company isn't mentioned at all, or is mentioned in purely factual terms without positive or negative association
Example: "Walmart has 4,700 stores nationwide."
Example: Article discusses ESG trends without specifically praising or criticizing your company

Unknown (Score: 0): The AI couldn't determine sentiment. This is rare and usually indicates a data quality issue.

Sentiment label	Score value	Description
Positive	1	Favorable language is used in the response when your brand is mentioned
Mixed	0.5	Both positive and neutral/negative language is present
Neutral / Unavailable	0	No clear sentiment in the response or the sentiment is not evaluable
Negative	-1	Unfavorable language is used in the response when your company is mentioned

How Prompts Work

Prompts are the questions the tracker asks AI models to see whether, and how, your company shows up in the answers.

Prompt logic

Every prompt the tracker runs belongs to a topic, and every topic sits under one of seven reputation pillars. The structure follows the pillar-to-topic model used by established reputation and ESG frameworks such as SASB and MSCI. See AI Visibility Tracker Definitions for the full list of pillars and topics.

Each topic is tested from the same set of angles, so results are comparable across topics:

Angle	Example	User Intent
Who leads	Which [industry] companies are leaders in business ethics?	Topic authority
How companies compare	How do [industry] companies compare on employee wellbeing?	Competitive comparison
Who AI recommends	Which [industry] companies would you recommend for responsible sourcing?	Recommendations
Real-world examples	What are examples of [industry] companies investing in their communities?	Examples
Critical questions	Which [industry] companies have faced criticism over data privacy?	Topic authority
Questions about you	What is [your company] known for on climate action?	Topic authority

Prompts are written with placeholders for your company, industry and competitors, which the tracker fills in for your account. That's how the same standard prompt produces a relevant question for every company in an industry.

Every prompt is also tagged two more ways:

Audience: The stakeholder group most likely to ask it, such as investors, talent, consumers or media. This effects language used in prompts based on the audiences you choose in your settings.

Framing: Whether the question is positive, neutral or critical. Critical questions show whether AI associates your company with a topic's risks, not only its leaders.

Types of prompts

Prompt Type	Definition	Counts Toward Visibility Score
Standard	Runs for every company in an industry, so results can be benchmarked against peers	Yes
Custom	Added for a specific company's programs, products or priorities; runs alongside the standard set	No; counts toward a separate score for those specific prompts
Branded	Names the company directly	No
Unbranded	Asks about an industry or topic without naming the company	Yes

Branded prompts are excluded from core scoring so your score reflects whether AI brings you up without being asked about you. Branded results still appear in the tracker, so you can see how AI describes you when it's asked directly.

Adding custom prompts

The standard library covers most of what companies need. Add a custom prompt when there's a question it doesn't ask or is specific to your company.

To write a strong custom prompt:

• Write it like a real person would. Plain language, one question, no jargon.
• Tie it to a topic. Every custom prompt should fit one of the 46 topics so it rolls up correctly in your results.
• Leave your name out where you can. "Which beverage companies are leading on water stewardship?" tells you more about visibility than "Is [your company] good on water?"
• Keep the framing neutral unless you're deliberately testing a critical angle.
• Check it's not already covered. If a standard prompt asks the same thing, you don't need a custom one.

Learn more about adding custom prompts in How do I add custom prompts to the AI Visibility Tracker?

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Key Terms

Column	Description
Prompt	The exact question asked to the AI provider
User Intent	The nature of the question asked to the AI provider, classified into four categories: Recommendations: Asking for information to provide a recommendation Examples: Asking for evidence and real-world instances around a given subject Topic authority: Asking about topics of interest or about companies with respect to a certain topic.  Competitive comparison: Asking about competitive standing on a given topic based on industry and persona - may use specific company names
Prompt Topic	Subject matter of the query (e.g., business ethics, employee wellbeing, environmental stewardship)
Company	The company being tracked
Response Text	The full text of the AI response
Response Sentiment	Qualitative sentiment label for the response
Response Model	The AI provider that generated the response
Citations	Source URLs or references included in the response
Target Competitors	Set of competitor companies tracked, if set

Full Metrics Reference

The following table lists all currently defined metrics in the AI Visibility Tracker. 

The following table lists supporting metrics within the dataset that are used to calculate other metrics but are not displayed directly. 

Important Caveats

This methodology is observational, not causal. The Visibility Score reflects how AI platforms represent a brand in their responses at the time of measurement. It does not predict or guarantee future outcomes.

Any responses generated by AI are shown as-is to the user, with active consideration not to influence the response in any way.  Generated responses may contain mistakes. 3BL does not verify the accuracy of model responses; it only shares the results.

Metric definitions, score weights, and thresholds are subject to change as the product develops. We will communicate any significant methodology changes through your account team and in product release notes.

Questions about this methodology or your results? Contact your 3BL Client Success Manager.

Related articles

• AI Visibility Tracker Definitions
• How do I set up and update my AI Visibility Tracker settings?