Understanding the Scores

ReachLLM runs your tracked prompts against each enabled AI platform on a schedule (daily or weekly depending on your settings) and analyzes every response. This page explains each metric that comes out of those runs: what it measures, how it is computed, and what moves it.

Visibility Score

What it measures: how often a platform's answers mention your brand at all.

How it is computed: for each platform, the Visibility Score is the percentage of your tracked prompts where that platform's answer mentions your brand. The overall Visibility Score is the average across platforms that ran at least one prompt.

Worked example: you track 20 prompts. ChatGPT mentions your brand in 12 of the 20 answers, so your ChatGPT Visibility Score is 12 / 20 = 60%. If Gemini scores 50%, Perplexity 40%, and Google AI Overviews 70%, your overall Visibility Score is the average of the four: (60 + 50 + 40 + 70) / 4 = 55%.

How to improve it: make sure your tracked prompts reflect the questions your buyers actually ask (see Working with Prompts), fix the issues your GEO audit surfaces so AI platforms can read and trust your pages, and publish content that directly answers the prompts where you are absent. The Responses tab shows the raw answers, so you can see exactly which prompts you are missing from.

What it measures: your slice of the conversation compared with competitors.

How it is computed: across all analyzed answers, ReachLLM counts every brand appearance (yours plus your competitors'), then reports the percentage that are yours. It is presence-based: a brand counts once per answer, no matter how many times it is repeated within that answer.

Worked example: across a run, the analyzed answers contain 40 brand appearances in total. 10 of those are your brand and 30 belong to competitors. Your Share of Voice is 10 / 40 = 25%. If one answer repeats your brand name five times, that still counts as a single appearance for you in that answer.

How to improve it: Share of Voice is relative, so it rises when you appear in more answers or when competitors appear in fewer of the answers you both compete for. Target the prompts where competitors show up and you do not, and keep your competitor list accurate: competitors are auto-discovered during brand analysis and fully editable, including aliases for alternate spellings. Wrong mentions can be removed and metrics recomputed, which keeps this score honest.

Average Rank (Visibility Rank)

What it measures: when your brand does appear in an answer, how early it is mentioned relative to other brands. Lower is better.

How it is computed: for each answer that mentions your brand, ReachLLM records the position at which your brand appears among the brands in that answer. Average Rank is the average of those positions. Answers where your brand does not appear are not part of this calculation.

Worked example: your brand appears in three answers. In the first it is the first brand mentioned (position 1), in the second it is second (position 2), and in the third it is third (position 3). Your Average Rank is (1 + 2 + 3) / 3 = 2.0.

How to improve it: being mentioned is step one; being mentioned first is the goal. Strengthen the signals that make AI platforms treat you as the primary answer: a complete brand knowledge base, authoritative content on the topics behind your prompts, and off-page authority (see the off-page checks in the GEO Audit). The trends charts show rank history per platform and per brand versus competitors, so you can confirm movement over time.

Sentiment

What it measures: the tone of what AI platforms say when they mention you.

How it is computed: each mention of your brand is classified as positive, neutral, or negative. The Sentiment tab shows the distribution and the drivers behind it, per platform.

Worked example: across a run your brand is mentioned 10 times. 6 mentions are classified positive, 3 neutral, and 1 negative. The Sentiment tab shows that 60 / 30 / 10 split and surfaces what is driving each bucket on each platform.

How to improve it: read the drivers in the Sentiment tab to see which themes pull negative or neutral. Then address the source material: customer reviews, comparison content, and the pages AI platforms draw from. Content that clearly states your strengths, backed by your knowledge base, gives platforms more positive material to work with.

Citation rate

What it measures: how often AI platforms cite your own domain as a source. This is different from being mentioned: an answer can name your brand without linking to your site, and it can cite your site without naming you prominently.

How it is computed: the percentage of prompt runs where the platform cited your domain as a source.

Worked example: across 100 prompt runs on a platform, your domain appears as a cited source in 8 of them. Your citation rate on that platform is 8 / 100 = 8%.

How to improve it: citations go to pages that are easy for AI platforms to retrieve and quote. Use the GEO Audit to fix on-page issues like structured data, snippetability, and llms.txt, and check the Sources tab to see which domains AI platforms cite for your prompts today. PR Outreach helps you earn placements on the publications that already get cited, and publishing content that directly answers your tracked prompts gives platforms a reason to cite you instead.

Where to see these metrics

The results views include five tabs: Overview, Sources (which domains AI cites, content types, and source detail), Sentiment, Query Fanout, and Responses (raw answers). Trends over time charts show score, share of voice, and rank history per platform and per brand versus competitors.

Next steps

AI Visibility Tracking: how tracking runs work end to end.
Working with Prompts: tune the prompt set these scores are built on.
GEO Audit: fix the on-page and off-page issues holding your scores back.