Loading...
Quick answer: An AI visibility score is a composite metric — usually shown on a 0–100 scale — that estimates how consistently and favourably your brand appears across generative AI engines such as ChatGPT, Claude, Gemini and Perplexity when real customers ask purchase-related questions. It is not a standardised industry metric. Every vendor, including MentionOwl, calculates it slightly differently, so treat any 0–100 number as that provider’s model of visibility rather than a universally comparable figure.
At MentionOwl, we build our version of the AI visibility score from four weighted inputs:
| Input | What it measures | Illustrative weight |
|---|---|---|
| Query coverage | Percentage of relevant questions where you appear at all | 35% |
| Position-weighted citations | How prominently you’re cited when you do appear | 30% |
| Share of voice | Your citation share versus named competitors | 25% |
| Soft mentions | Descriptive, unlinked references — positive, neutral or critical | 10% |
I want to be upfront: those weights reflect our own methodology at MentionOwl, offered here as an illustrative framework so you can understand the mechanics, not as a formula every tool in this category uses. If a vendor won’t tell you its weights or normalisation method, that’s worth asking about directly before you present its AI visibility score to your leadership team.
Marketing teams often meet this number for the first time in a dashboard. They nod along in the meeting where it’s introduced, then struggle three weeks later when a director asks precisely why the score moved from 54 to 61. This guide exists to close that gap. I’ll walk through each input, show a worked composite calculation, and give you a benchmarking approach you can defend with actual reasoning rather than a vague sense that “it’s gone up.”
![Diagram: A simple layered diagram showing four labeled input blocks - Query Coverage, Position-Weighted Citations, Share of Voice for Understanding Your AI Visibility Score, Explained(https://www.mentionowl.com/blog/share-of-voice-in-the-ai-era-a-new-metric-for-marketers), and Soft Mentions - feeding into a single central gauge labeled 'AI Visibility Score 0-100', clean flat design, blue and grey palette]
Caption: The four inputs behind a typical AI visibility score, weighted and combined into one trackable figure. Takeaway: no single input tells the full story — the composite exists to stop a strong result in one area masking weakness in another.
Leadership teams want a KPI they can track over time, similar to how they already track Domain Authority or Net Promoter Score, without reading raw AI transcripts every morning. That’s reasonable. A well-built composite score does the aggregation work so people can focus on interpretation and action.
But a composite is only useful if you can take it apart again. Here’s what each input captures, in isolation, before any weighting is applied:
No single one of these tells the whole story alone. A brand could rank first in one query out of a hundred, producing an eye-catching position score for that single instance, while remaining functionally invisible across the other 99. Another brand might appear across many queries but always as a low-prominence mention several paragraphs deep — high coverage, low actual consideration. The composite exists to stop either partial picture being mistaken for the full one.
It’s worth noting how this differs from the SEO visibility scores UK marketing teams have relied on for the past decade. Traditional SEO visibility tracks where pages rank on Google’s UK results pages — a fixed, rank-ordered list where positions one through ten are the entire game. Generative engine optimisation works differently. There’s no list of ten blue links. An AI model synthesises an answer from training data and, increasingly, live retrieval, and your brand either gets woven into that synthesis or it doesn’t. That’s why AI SEO and AI search strategy need their own measurement framework rather than a repurposed SEO dashboard.
Let’s make this concrete using our illustrative MentionOwl weighting from earlier. Say a mid-sized UK SaaS brand records the following across a 100-question tracked set:
Applying the illustrative weights:
Score = (0.35 × 40) + (0.30 × 55) + (0.25 × 33) + (0.10 × 60)Score = 14 + 16.5 + 8.25 + 6 = 44.75, rounded to 45
Now imagine that brand publishes three new comparison pages, fixes structured data on its pricing page and picks up two additional named mentions the following month. Coverage rises to 46%, position score to 62, share of voice to 38%, while soft mentions stay flat at 60:
Score = (0.35 × 46) + (0.30 × 62) + (0.25 × 38) + (0.10 × 60) = 16.1 + 18.6 + 9.5 + 6 = 50.2
That’s a five-point movement you can trace to two specific inputs — position and share of voice — rather than a vague “the number went up.” This is the level of detail I’d expect any AI visibility vendor to be able to show you on request.
Query coverage is the percentage of realistic customer questions in which your brand shows up anywhere within the AI’s answer — a direct citation, a named recommendation or a brief descriptive mention. It’s the foundation layer. If you’re not appearing at all, position and sentiment become irrelevant, because there’s nothing there to position or feel sentiment about.
At MentionOwl, we generate this question set by modelling the purchase-decision queries a real prospect would plausibly ask ChatGPT or Perplexity. For a project management software company, that might include “best project management tool for small agencies”, “alternatives to Asana for freelancers” or “which project management software integrates with Slack?” We run this set daily against the major AI platforms and record how each one answers.
A brand appearing somewhere in the answer for 40 out of 100 tracked questions has a query coverage rate of 40%. That figure then feeds into the overall AI visibility score with its own weighting, sitting alongside the other three components rather than standing alone. A brand with 40% coverage but consistently strong positioning within that 40% can easily outscore a brand with 70% coverage where every mention is a throwaway line in paragraph four.
When coverage comes back lower than expected, a few things are usually worth checking — though I’d be careful not to overstate any single cause:
That third point is why we run AI legibility audits at MentionOwl — a set of technical checks that flag where a site may be harder for AI crawlers to parse. I’d treat the results as a diagnostic worth investigating, not a guarantee that fixing them will move your coverage number by any specific amount.
Appearing somewhere in an AI answer is necessary but not sufficient. Being named as the primary recommendation, or cited in the first sentence, is worth substantially more than being listed sixth in a rundown of seven options most users won’t read in full.
Before going further, it’s worth being precise about what “position” actually means here, because generative answers don’t have a fixed slot structure the way a Google results page does. At MentionOwl, we define position using a combination of signals:
We apply a decaying weight to each position. An illustrative version of that curve looks like this:
| Position | Illustrative weight |
|---|---|
| 1st named / primary recommendation | 1.0 |
| 2nd named | 0.7 |
| 3rd named | 0.45 |
| 4th named | 0.25 |
| 5th named or lower | 0.1 |
Worth flagging: this curve is our own approach, not an industry standard, and different AI engines structure answers differently enough that a “3rd position” in a short Perplexity answer isn’t necessarily equivalent to a “3rd position” in a longer ChatGPT response. We normalise for answer length where possible, but I’d encourage some scepticism toward any vendor that presents position weighting as a perfectly clean, universally consistent measurement.
This logic mirrors something UK marketing teams already understand from traditional search: click-through-rate curves. The top organic result on a Google results page has historically captured roughly a quarter to a third of all clicks, with each subsequent position falling off sharply. Generative AI answers seem to behave similarly, arguably more starkly, because there’s no scrollable list for a user to work through. Two brands can post identical 45% coverage figures and land at very different overall scores, because one is the consistently named first recommendation and the other is perpetually fourth or fifth in a crowded list.
Caption: Illustrative position-decay curve used to weight citations. Takeaway: a first-named mention typically carries several times the value of a fifth-named mention, which is why coverage alone can be misleading.
These two components often get conflated, but they measure different things and deserve to be reported separately to leadership.
Share of voice is your citation share relative to named competitors within the same query set. We calculate it at the query level first, then aggregate. Take three queries as an example:
Averaged across those three queries, your raw share of voice sits around 19%, before any position weighting is applied to reflect that you were named first in Query 3. This query-level aggregation matters, because a brand that wins big in a handful of high-value queries can post a respectable share of voice even with patchy coverage elsewhere.
Soft mentions capture instances where your brand is referenced descriptively or implicitly, without a direct citation, link or explicit recommendation. To be precise about something the introduction glossed over: soft mentions aren’t automatically positive. They carry contextual signal that can read as positive, neutral or mildly critical, and a properly built score should tag sentiment separately rather than assuming every unlinked reference is a good one. A neutral factual mention (“tools in this space include X, Y and Z”) is treated differently from a mildly critical aside, even though both are technically “soft.”
| Dimension | Share of Voice | Soft Mentions |
|---|---|---|
| What it measures | Citation share versus named competitors | Descriptive or implicit brand references |
| Format | Direct, attributable citation or recommendation | Unlinked, contextual mention |
| Sentiment handling | Assumed neutral-to-positive because it’s a named recommendation | Tagged positive, neutral or negative separately |
| Weighting in score | Higher — 25% in our illustrative model | Lower — 10% in our illustrative model |
| Leadership relevance | Closest AI-era equivalent to market share | Secondary trend indicator |
I’d argue share of voice is the single figure leadership should care about most, because it’s the closest AI-era equivalent to market share and it’s directly comparable across your competitor set in a way raw coverage percentages aren’t. Two brands in different niches can both claim 60% query coverage while facing entirely different competitive intensity; share of voice normalises for that by measuring you against the rivals actually being named in the same answers.
Caption: How share of voice and soft mentions differ in format and weighting. Takeaway: share of voice reflects active recommendations; soft mentions reflect passive brand awareness that still needs sentiment context.
A visibility score reported once, in isolation, tells leadership almost nothing. The value comes from tracking the trend with enough methodological discipline that you can defend movements when someone asks why the number changed.
Before you trust any benchmark number — from us or anyone else — I’d ask a vendor to confirm:
Caption: An eight-week trend line with content and technical changes annotated at the point they were made. Takeaway: pairing score movement with a dated list of specific actions is what makes a trend defensible in a leadership meeting.
There’s no universal benchmark, because an AI visibility score isn’t a standardised metric across vendors. Rather than anchoring to a fixed range, benchmark against your own category cohort: the same competitor set, the same query list and the same engines, tracked over the same period. In our own tracking at MentionOwl, established brands with strong content coverage and clean technical foundations tend to cluster in the upper half of the scale, while brands with real AI legibility gaps or very new market presence tend to sit lower — but I’d treat any specific numeric range you’re quoted, including ours, as a starting point for questions rather than a settled fact.
At MentionOwl, we calculate it as a weighted composite: query coverage (35%), position-weighted citations (30%), share of voice (25%) and soft mentions (10%) in our illustrative model, each normalised to a 0–100 scale before weighting. Each component is measured across ChatGPT, Claude, Gemini, Copilot and Perplexity and rolled into a single figure. This is our methodology specifically — other vendors may weight or define these inputs differently, so ask any tool you’re evaluating to show its formula, not just its final number.
Weekly for reporting purposes, since day-to-day AI answer variance can create noise that doesn’t reflect a genuine shift in visibility. Daily crawls feeding into a weekly digest tend to give a more stable trend line than a jumpy daily number.
Frame it as the AI-era equivalent of market share and search visibility combined: it estimates what percentage of relevant customer questions your brand wins, how prominently you’re recommended versus named competitors and whether that trend is improving month over month. Pair the score with a before-and-after chart tied to specific content or technical changes, and be upfront that it’s one vendor’s model of visibility rather than an audited industry standard — that honesty tends to land better than presenting the number as gospel.
A real-world case study on how an e-commerce brand quietly lost AI visibility to a competitor—and the data-backed recovery plan that brought its share
Track your AI visibility score automatically, monitor ChatGPT citations, and save UK solo founders hours every week with automated AI tracking.
Learn how ChatGPT citations work, which sources influence AI recommendations, and how brands can improve visibility, trust, and share of voice.
