The Trackbase dashboard metrics in one place: what each one measures, how it is calculated and how to read them together.
Trackbase turns every AI answer into a small set of metrics. Each one answers a different question. This page gives the short version of each and explains how the dashboard numbers are built.
Every answer a model gives to one of your prompts is analyzed for:
Brand mention: whether your brand name or one of its aliases appears, and how often. Matching is on whole words, so a brand name is not counted when it is only part of another word.
Position: where your brand first appears in the answer, counted from 1.
Sentiment: how positively the answer describes your brand, from 0 to 100. Sentiment is only scored when your brand is mentioned.
Sources: the URLs the model cited.
Competitors: which competitors are mentioned, and how often.
How prominently your brand appears, from 0 to 100. Being mentioned counts most, followed by sentiment and position, then how often you are mentioned relative to the length of the answer. An answer that does not mention you scores 0.
Share of voice
Your brand mentions as a share of all mentions of your brand and your competitors.
Presence rate
The share of your prompts on which your brand appears at least once.
Average position
The average place where your brand first appears, in the answers that mention you. Lower is better.
Low presence rate, good average position: when you appear you are placed well, but you are missing from many answers. The work is coverage.
High presence rate, late average position: you appear often, but after your competitors. The work is prominence.
Mentions without citations: models know your brand from other sources, but your own pages are not feeding the answers. The work is content and crawlability.
Good visibility, weak sentiment: you are visible but described negatively. Read the answers to see which claims cause it.
When several metrics fall on the same day, check first whether you added prompts or models. New prompts where you are not mentioned yet lower the average without any change in how AI sees you.
For definitions of the terms used here, see the glossary.
Last modified on November 12, 2026
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