How metrics are calculated

Every answer metric is computed over all answers in the range, pooled: each answer counts once, whichever engine gave it. For per-engine figures, add platform to group_by and each row is computed over that engine’s answers alone. The API and the dashboard run the same code, so the same range and filters give the same number.

Which answers count

Answers collected successfully on a day inside the range (UTC).
Not Google result pages that showed no AI Overview. They are dropped before anything is counted, from the numerators and from answers.
When you pass a filter, only answers matching it.

Formulas

Mention rate

\text{mention\_rate} = \frac{\text{answers naming the brand}}{\text{answers}}

An answer names you when it uses your brand name or one of its aliases. 0–1.

Citation rate

\text{citation\_rate} = \frac{\text{answers linking to at least one page on the brand's site}}{\text{answers}}

Ten links to your site in one answer count once. For a competitor, “its site” is the domains on its competitor entry. 0–1.

Prompt coverage

\text{prompt\_coverage} = \frac{\text{distinct prompts where the brand was named at least once}}{\text{distinct prompts asked}}

A prompt named on any day, on any engine, in the range counts as covered. That is why coverage over a month is usually higher than coverage on any one day. 0–1.

Visibility Score

\text{visibility\_score} = 100 \times (0.45 \cdot \text{mention\_rate} + 0.35 \cdot \text{citation\_rate} + 0.20 \cdot \text{prompt\_coverage})

The three rates are rounded to two decimals (whole percentage points) first, and the score is rounded to a whole number. 0–100.

\text{share\_of\_voice} = \frac{\text{answers naming the brand}}{\text{brand mentions across all tracked brands}}

Each brand counts at most once per answer. Tracked brands are you and every competitor on the site’s list. 0–1.

Average position

\text{average\_position} = \frac{\text{sum of the brand's positions}}{\text{answers naming the brand}}

Position is the order in which an answer first names each tracked brand: the first is 1. Lower is better. Rounded to one decimal; null when the brand was never named.

Rank

Order by Visibility Score among you and your confirmed competitors, within each group of rows. 1 is best.

\text{citation\_share} = \frac{\text{citations of this domain (or page)}}{\text{all citations in the same group that pass the filter}}

Counts every link, so a page linked twice in one answer counts twice. Rounded to four decimals. 0–1.

Sentiment

\text{positive} = \frac{\text{answers naming you in a positive tone}}{\text{answers naming you}}

The same for neutral and negative. The three add up to 1 (give or take rounding). Answers not yet judged count as neutral.

Worked example

A site with five prompts collects 24 answers in a week. Four are Google result pages with no AI Overview, so 20 answers count.

Input	Value
Answers naming you	9
Answers linking to your site	5
Prompts asked / prompts where you were named	5 / 3
Brand mentions in those 20 answers: you, rival A, rival B, rival C	9, 12, 6, 3 (30 in all)
Sum of your positions in the 9 answers naming you	18
Citations of `example.com` / all citations	8 / 50
Tone of the 9 answers naming you: positive, neutral, negative	6, 2, 1

Metric	Calculation	Result
`mention_rate`	9 / 20	0.45
`citation_rate`	5 / 20	0.25
`prompt_coverage`	3 / 5	0.60
`visibility_score`	100 × (0.45 × 0.45 + 0.35 × 0.25 + 0.20 × 0.60) = 100 × (0.2025 + 0.0875 + 0.12)	41
`share_of_voice`	9 / 30	0.30
`average_position`	18 / 9	2.0
`citation_share` (`example.com`)	8 / 50	0.16
`positive` / `neutral` / `negative`	6 / 9, 2 / 9, 1 / 9	0.6667 / 0.2222 / 0.1111
`answers`		20

Averaging seven daily Visibility Scores does not give this week’s 41. Coverage in particular is 3 of 5 for the week even if no single day covered more than 2. Ask for the week in one call. See Conventions & gotchas.

Reproducing the numbers from raw answers

POST /v1/answers returns one row per answer with everything the formulas need:

Field	Contents
`id`, `date`, `platform`, `prompt`	Which answer, and its labels.
`brand_mentioned`, `brand_position`	Whether and where it named you.
`mentions`	Every tracked brand it named, in order of first appearance, each once.
`citations`	Each link, with `domain` and `own_site`.
`sentiment`	`positive`, `neutral`, `negative`, or `null` when not judged.
`text`	The answer itself. Empty on `ai_overviews` and `ai_mode` when the result page showed no AI Overview.

This example computes the four headline figures for a range and checks them against the visibility report.

import { RankRaft } from "rankraft-js";

const rr = new RankRaft();
const q = { site_id: process.env.RANKRAFT_SITE_ID, start_date: "2026-09-01", end_date: "2026-09-30" };

const all = await rr.answers.query({ ...q, include: ["prompt", "platform", "text", "brand_mentioned", "citations"], limit: 50 }).all();

// Google pages that showed no AI Overview are not answers.
const serp = new Set(["ai_overviews", "ai_mode"]);
const answers = all.filter((a) => !(serp.has(a.platform) && a.text === ""));

const r2 = (n) => Math.round(n * 100) / 100;
const named = answers.filter((a) => a.brand_mentioned);
const mention_rate = r2(named.length / answers.length);
const citation_rate = r2(answers.filter((a) => a.citations.some((c) => c.own_site)).length / answers.length);
const prompt_coverage = r2(new Set(named.map((a) => a.prompt.id)).size / new Set(answers.map((a) => a.prompt.id)).size);
const visibility_score = Math.round((0.45 * mention_rate + 0.35 * citation_rate + 0.2 * prompt_coverage) * 100);

const report = await rr.reports.visibility({ ...q, metrics: ["visibility_score", "mention_rate", "citation_rate", "prompt_coverage", "answers"] });
console.log({ answers: answers.length, mention_rate, citation_rate, prompt_coverage, visibility_score });
console.log(report.data[0]); // the same figures

from rankraft import RankRaft
import os

q = dict(site_id=os.environ["RANKRAFT_SITE_ID"], start_date="2026-09-01", end_date="2026-09-30")

with RankRaft() as rr:
    rows = rr.answers.query(**q, include=["prompt", "platform", "text", "brand_mentioned", "citations"], limit=50).all()
    report = rr.reports.visibility(**q, metrics=["visibility_score", "mention_rate", "citation_rate", "prompt_coverage", "answers"])

# Google pages that showed no AI Overview are not answers.
answers = [a for a in rows if not (a["platform"] in ("ai_overviews", "ai_mode") and a["text"] == "")]

named = [a for a in answers if a["brand_mentioned"]]
mention_rate = round(len(named) / len(answers), 2)
citation_rate = round(sum(any(c["own_site"] for c in a["citations"]) for a in answers) / len(answers), 2)
prompt_coverage = round(len({a["prompt"]["id"] for a in named}) / len({a["prompt"]["id"] for a in answers}), 2)
visibility_score = round((0.45 * mention_rate + 0.35 * citation_rate + 0.20 * prompt_coverage) * 100)

print(dict(answers=len(answers), mention_rate=mention_rate, citation_rate=citation_rate,
           prompt_coverage=prompt_coverage, visibility_score=visibility_score))
print(report.data[0])  # the same figures

Python’s round() rounds halves to the nearest even number, so on an exact half (0.125) it can land one point away from the report. The report rounds halves up.

Compared with Profound

If you are moving code across from Profound’s API:

Profound’s visibility_score is a 0–1 share of answers that mention any brand. Ours is the 0–100 composite above.
Profound computes each metric per engine, then averages across engines. Ours pools every answer; group by platform for per-engine figures.
Profound counts days in US Eastern time. Ours are UTC.

Get started

Metrics

Visibility

Citations

More recipes

Which answers count

Formulas

Mention rate

Citation rate

Prompt coverage

Visibility Score

Average position

Rank

Sentiment

Worked example

Reproducing the numbers from raw answers

Compared with Profound

​Which answers count

​Formulas

​Mention rate

​Citation rate

​Prompt coverage

​Visibility Score

​Share of voice

​Average position

​Rank

​Citation share

​Sentiment

​Worked example

​Reproducing the numbers from raw answers

​Compared with Profound

Which answers count

Formulas

Mention rate

Citation rate

Prompt coverage

Visibility Score

Share of voice

Average position

Rank

Citation share

Sentiment

Worked example

Reproducing the numbers from raw answers

Compared with Profound