What is a good share of voice percentage?

There is no universal benchmark. A share of voice figure is only readable with its competitor set, prompt set, counting unit and null-answer rule attached, and it is judged by direction over time within one consistent series. Engines deliberately name alternatives in their answers, so even dominant brands share the surface; compare your trend, not an absolute target.

How do I calculate share of voice?

Divide your brand's metric by the category total and multiply by one hundred. On social that means your mentions over all tracked brands' mentions; in paid search, impressions over eligible impressions; in AI search, your brand's mentions in answers to a fixed prompt set over all category brand mentions in those same answers, counted per engine.

What is the difference between share of voice and share of market?

Share of voice measures presence: the portion of category conversation, coverage or ad spend a brand holds relative to competitors. Share of market measures sales. The gap between the two, called excess share of voice, is what Binet and Field's long-running campaign analysis, summarised by Nielsen, links to growth: brands whose voice exceeds their market share tend to gain market share over time.

How do I track my share of voice in AI search versus competitors?

Write and save a fixed set of real buyer prompts, run it in each engine separately, and tally every tracked brand's mentions or citations in the answers. Divide your count by the category total, per engine. Keep the competitor set, unit and null-answer rule constant, repeat the runs in one sitting, and report a dated low-high range rather than a single number.

Is share of answer the same as share of voice?

Often, yes: most tools use share of answer for the same competitive arithmetic, your mentions divided by all category brand mentions in AI answers. Some use it differently: shareofanswer.is, for example, averages the scores of five repeated runs of one search into a percentage, which measures one brand's consistency rather than a competitive share. Check which definition a figure uses before comparing it.

What Share of Voice Is and How to Calculate It, Including AI Search

Share of voice is the portion of a category's conversation, coverage, ad spend or answer mentions that one brand holds relative to its competitors. The formula is your brand's metric divided by the category total, multiplied by one hundred. The same arithmetic now applies inside ChatGPT, Gemini, Perplexity and Google AI Overviews answers.

What share of voice is, and the one formula behind it

Share of voice is the portion of attention a category gives one brand relative to its competitors. It began as an advertising-spend metric; today it covers social mentions, press coverage, search visibility and, most recently, brand mentions inside AI-generated answers. Whatever the channel, the formula is constant: your brand's metric, divided by the category total across all tracked brands, multiplied by one hundred.

A worked example, explicitly hypothetical: a project-management tool collects two hundred social mentions in a month. All tracked brands in its category, itself included, collect one thousand. Two hundred divided by one thousand, times one hundred, is a twenty percent share of voice.

Share of voice is not share of market. Voice measures presence in the category conversation; market measures sales. The gap between them, excess share of voice, is what the classic research links to growth: Binet and Field studied 171 campaigns across top categories between 1980 and 2010 and established that a brand's share of market typically gained 0.5% for every 10% in excess share of voice, as summarised in Nielsen's write-up of the research, published in March 2025 and consulted on 4 October 2026.

Two cautions before the channel detail. The same arithmetic now applies inside ChatGPT, Gemini, Perplexity and Google AI Overviews answers, and the second half of this article measures it there: one metric, not two. And a share is only as reliable as its denominator — when the full category cannot be counted, the honest fix is a named competitor set, treated below.

The channels where share of voice is counted, AI answers included

The core reason to track the metric on any channel is competitive benchmarking: an absolute mention count says little, but your slice of the category total says whether you are gaining or losing ground against named competitors. Each channel has its own numerator and denominator.

Channel	What you count	Denominator	Data source
Social media	Your brand's mentions	All tracked category brands' mentions	Social listening tool
Organic search	Your visibility on a tracked keyword set	Total visibility across the set	SEO platform
Paid search	Impressions your ads received	Total eligible impressions	Google Ads impression share report
PR and media	Coverage mentioning your brand	Category coverage over the same period	Media monitoring tool
AI answers	Your mentions or citations in answers to a fixed prompt set	All category brand mentions in those answers	Manual runs or an AI visibility tool

Paid search is the one channel where the share comes built in. Google's Ads Help page on impression share, consulted on 4 October 2026, defines it as impressions divided by total eligible impressions — share of voice computed for you, with no listening tool involved.

For social and media, rely on a listening or monitoring tool rather than manual counting. Tallying your own mentions by hand is merely tedious; tallying every competitor's mentions across a whole category, continuously, fails at scale — and the denominator is where the metric lives. AI answers are the newest row, with their own unit and data source; the rest of this article is about that row.

Share of voice in AI search: the same share, a new surface

AI-search share of voice is your brand's mentions or citations divided by all category brand mentions in the answers an engine returns to a fixed prompt set, per engine. The denominator is brand presence in the answers, not the number of prompts, which is what makes the metric competitive by construction.

The answer surface deserves counting because it absorbs attention that used to go to links. Pew Research Center reported in July 2025 that users who encountered an AI summary clicked a traditional search result link in 8% of visits, against 15% of visits when no summary appeared. Where the answer keeps the click, presence in the answer is the visibility.

Before counting, pick one unit and hold it for the whole series — and see how a mention differs from a citation in AI answers if the line between the two is new to you.

Unit	What it counts
Mention	Your brand named in the answer text
Citation	Your page linked as a source for a claim in the answer
Source	Your domain present in the answer's source list

One naming caution. Share of answer usually means this same competitive arithmetic. It sometimes means something narrower: shareofanswer.is, for instance, documents a method where the scores from five runs of one search are added up, divided by five and multiplied by one hundred — a single-brand consistency score, not a share of a category. Establish which definition a figure uses before comparing it to yours.

The metric is also per-engine by construction. ChatGPT, Gemini, Perplexity and AI Overviews retrieve and cite differently, so each engine gets its own share, and any blend across engines must be a documented combination, never a silent average.

How to calculate it by hand, and why the number moves

The manual method has four steps.

First, write a prompt set of real buyer questions — the questions a prospect would ask an assistant before building a shortlist — and save it verbatim. The saved set is the instrument; rewording a prompt changes the measurement.
Second, run the set in each engine, keeping every engine's results separate.
Third, tally every brand's mentions — or citations, one unit, never mixed — across the answers.
Fourth, divide your brand's count by the total for all brands and express the result per engine.

Decide the null-answer rule once: do answers that name no brand at all enter the denominator? Either choice is defensible; mixing the two is not. Note the rule beside every figure you publish.

On sample size, we observe in the scans we run at Namedrop — last confirmed on 4 October 2026 — that a handful of prompts run once swings on noise, while a couple of dozen prompts repeated several times per engine is the workable floor for a stable reading.

Vendor dashboards automate the same arithmetic. Semrush's write-up of the Brand Performance report in its AI Visibility Toolkit, consulted on 4 October 2026, says it calculates AI share of voice based on both how many times your brand is mentioned and how high it appears — mention counting plus prominence weighting. The formula underneath remains the one on this page.

Expect the number to move, for two separate reasons. Across engines, differences are measurement, not noise: Ahrefs' study of Google's two AI surfaces, published in December 2025, found that AI Mode and AI Overviews responses reached 86% semantic similarity on average while sharing only 13.7% of their citations. Two surfaces from one company, agreeing on conclusions, citing different pages — so per-engine figures must sit beside any blend.

Within one engine, same-day runs of the same prompt differ, so report an error band: the low and the high across runs. A score that moves inside that band while citations stay flat is variance, not a lost position. And a falling organic click-through rate does not by itself mean lost answer presence — Pew's figures above show clicks dropping where summaries appear. Clicks and answer share are separate signals that move independently.

What a good share looks like, even with incomplete data

There is no universal benchmark percentage. A share is readable only with its competitor set, prompt set, unit and null rule attached, and it is judged by direction over time within one series. A complete share is also unreachable by design: engines deliberately name alternatives and hedge, so even dominant brands share the answer surface.

When complete competitor data is unavailable — in most categories it is — fix a named competitor set as the proxy denominator. State what the set covers and keep it constant so the series stays comparable; a smaller honest denominator beats a fictitious category total. To choose the set, benchmark your AI visibility against competitors before counting.

Raw counts are not the only reading. Weighting mentions by prominence — Semrush, as cited above, documents scoring by how high the brand appears, not just how often — separates a lead recommendation from a trailing aside. And separate negative or crisis mentions before celebrating a spike: pair the share with sentiment so a volume surge driven by a problem is not reported as a gain.

Finally, match the approach to the question being asked.

Approach	What it measures	The question it answers
Prompt-response share	Your slice of brand mentions in live answers to a fixed prompt set	What do buyers see today when they ask?
Domain-level share	Your presence in the pages that shape what models say	Which sites influence the model's training and retrieval?

Evertune's resource page, consulted on 4 October 2026, illustrates the second approach: it calculates Brand Share of Voice for the top 50 URLs as measured by its AI Education Score — a reading of influence on models, not a live answer tally. Both approaches are legitimate; they answer different questions.

Where to start this week

A first defensible reading — classic and AI — fits in an afternoon, free and by hand.

Name the category and the competitor set that forms your denominator, and write both down.
If you run paid search, pull impression share from Google Ads first: the classic baseline, already computed for you.
Write a couple of dozen real buyer questions and save the prompt set verbatim.
Fix the unit — mention or citation — and the null-answer rule before counting, then name the series and the definitions used so future reports stay comparable.
Run several passes per engine in one sitting. Record the date, prompt count and run count beside the result, and report it as a dated low-high range per engine.
If you later change the prompt set, the unit or the null rule, label the result as a new series, not a continuation.

Once the manual series exists, you can automate these AI visibility checks so the next reading costs minutes rather than an afternoon. Run a free Namedrop scan to see the share of voice AI engines currently give your brand against your competitors.