{"id":2869,"date":"2026-10-02T03:14:13","date_gmt":"2026-10-02T03:14:13","guid":{"rendered":"https:\/\/maxaeo.ai\/blog\/calculate-llm-share-of-voice\/"},"modified":"2026-10-02T03:14:13","modified_gmt":"2026-10-02T03:14:13","slug":"calculate-llm-share-of-voice","status":"publish","type":"post","link":"https:\/\/maxaeo.ai\/blog\/calculate-llm-share-of-voice\/","title":{"rendered":"Calculate LLM Share of Voice: Cross-Engine Formula and Position Weights"},"content":{"rendered":"

By maxaeo.ai \uff5c Published 2026-10-02 \uff5c Updated 2026-10-02<\/em><\/p>\n

To calculate LLM share of voice<\/strong>, measure how often your brand appears in a fixed set of buyer prompts, compare those mentions with competitors, and then apply consistent weights for position, engine, sentiment, and citations. The basic percentage is useful, but a defensible measurement model must separate visibility from recommendation quality.<\/p>\n

\"calculate<\/figure>\n

What is LLM share of voice?<\/h2>\n

LLM share of voice is the percentage of competitive brand mentions owned by one brand across a defined set of AI-generated answers. Unlike traditional search share of voice, one answer can mention several brands, recommend one brand first, cite another brand\u2019s website, and describe a third brand negatively.<\/p>\n

A simple formula is:<\/p>\n

Mention Share of Voice =\nYour Brand Mentions \u00f7 Total Tracked Brand Mentions \u00d7 100\n<\/code><\/pre>\n
For example, if a prompt set produces 240 total mentions across your brand and four competitors, and your brand appears 48 times:<\/p>\n
48 \u00f7 240 \u00d7 100 = 20% mention share\n<\/code><\/pre>\n
This is a zero-sum share of mentions<\/strong> model. It differs from mention rate, which measures your appearances against the number of valid answers:<\/p>\n
Mention Rate =\nAnswers Mentioning Your Brand \u00f7 Valid Answers \u00d7 100\n<\/code><\/pre>\n
The distinction matters. If an answer mentions five companies, each company can increase its mention rate, but the share of total mentions still has to be allocated among the brands being tracked. Current measurement guides commonly treat these as separate metrics, although many platforms use \u201cshare of voice\u201d for only one of them. (llmpulse.ai<\/a>)<\/p>\n
What data do you need before calculating it?<\/h2>\n
A reliable calculation starts with a controlled measurement set. Record the following fields for every prompt and answer:<\/p>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n
Field<\/th>\nWhat to record<\/th>\n<\/tr>\n<\/thead>\n
Prompt<\/td>\nThe exact buyer question used<\/td>\n<\/tr>\n
AI engine<\/td>\nChatGPT, Perplexity, Gemini, Claude, or another target engine<\/td>\n<\/tr>\n
Date<\/td>\nWhen the answer was collected<\/td>\n<\/tr>\n
Brand mentions<\/td>\nEvery tracked brand named in the answer<\/td>\n<\/tr>\n
Position<\/td>\nFirst, second, third, or another clearly defined position<\/td>\n<\/tr>\n
Citation<\/td>\nWhether the brand or its domain was cited<\/td>\n<\/tr>\n
Sentiment<\/td>\nPositive, neutral, negative, or inaccurate<\/td>\n<\/tr>\n
Prompt intent<\/td>\nCategory, comparison, alternatives, use case, or pricing<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
Use the same competitor set throughout a reporting period. Changing competitors halfway through the month can create a false increase or decrease in share.<\/p>\n
The prompt set should represent real buying situations rather than only branded queries. A SaaS team might track prompts such as:<\/p>\n