AI tools today play a vital role in increasing the traffic to a website that determines a brand’s
success. If a brand doesn’t appear in AI-driven results, a business will miss out on a critical
segment of the audience. AI platforms such as ChatGPT, Gemini, and Perplexity use large
language models (LLMs), allowing businesses to extract the necessary data effectively,
which relate to brand building and other elements. However, measuring AI visibility across
LLM platforms enables a business to make informed decisions accordingly.
6 AI metrics to use for tracking visibility
1. Share of voice
Share of voice is an important metric that provides ways to measure how often a brand
appears in AI responses compared to competitors. At the same time, it is wise to know how to share the voice in LLMO (large language model optimization) strategy. This, in turn, gives ways to achieve better results in AI searches. The first step is to identify competitive gaps if the share of voice drops during certain periods that help implement the right strategies. A website can utilize share of voice for a content improvement, technical optimization, and other LLMO strategies. Hence, it is wise to monitor changes to determine whether they make a big difference or not. Apart from that, the brand comparison share of voice enables a business to know where it stands among others in the market.
2. Brand visibility
AI brand visibility lets a business know the frequency of brand occurrences that show ways to understand how often a brand appears. LLMO strategies allow a business to get high visibility on search engines. Monitoring the metric gives ways to measure the reach of awareness-building campaigns. A downward visibility trend indicates competitors are
gaining ground, which enables a business to investigate the type of content that needs more changes.
3. AI mentions
AI mentions track the raw count of brand appearances in AI responses across all monitored prompts. On the other hand, a business or company should understand how to utilize AI mention volume in LLMO strategy. A low count means a website is missing important industry conversations, and creating comprehensive content that addresses customer points will bring the best results.
The next step is to measure the reach of awareness campaigns through PR campaigns,
product launches, and content marketing. Furthermore, it is necessary to look for patterns to determine the type of content or topics that generate the most brand mentions.
4. AI answer citations
When AI platforms link the content in their responses, AI answer citations occur, which refer to a brand without linking to a website. The number of citations allows a website to know the authority and trustworthiness of content considered by AI tools. Citation is an indication of content quality and topical authority, and different AI platforms handle citations differently.
A website should focus more on content quality after monitoring content types with more
attention. Since citations provide reliable data to users, it is important to create high-
performing content with comprehensive coverage and a clearer structure.
5. AI rankings
AI rankings allow a website to know a brand’s response when AI platforms list multiple
options. They provide methods to view overall competitive positioning in AI responses to a larger extent. Evaluating competitors will help understand who ranks consistently in
positions. Tracking position trends enables a business to monitor whether the average
positions improve over time.
6. AI referral traffic
AI referral traffic refers to measuring the actual website visitors generated from AI
platforms that help get more insights. It shows whether AI visibility might convert into
website engagement. A website should identify high-converting AI platforms that help focus on optimization efforts.
