AI Visibility Report: Methodology

The South African AI Visibility Report measures what the leading AI assistants tell consumers when asked real buyer-intent questions. The method is the same across all 18 industries, so results are directly comparable.

What we asked, and how

For each industry we wrote 20 buyer-intent questions - the questions South Africans genuinely ask when choosing a provider - grouped into seven intents:

Ranking ("what is the best X in South Africa?")
Ease ("which is easiest to sign up / claim / switch?")
Comparison ("Brand A vs Brand B")
Segment ("best X for a student / family / business")
Criteria ("cheapest", "best value", "most reliable")
Trust ("most trusted / reputable")
Problem ("I need X, who should I choose?")

Each question was put to three AI assistants - ChatGPT (OpenAI), Claude (Anthropic), and Gemini (Google) - with live web search / grounding switched on, so the answers reflect current, sourced information rather than stale training data. Every question was asked three times per assistant to smooth out run-to-run variation.

That is 20 questions x 3 assistants x 3 runs = 180 answers per industry, and 3,240 answers across the index. For every answer we captured the full response and every source the assistant cited.

How brands are extracted

A second AI pass (Claude Haiku) reads each answer and extracts every brand named, where it ranks if the answer is an ordered list, and whether it is an active recommendation, a neutral mention, or a caveated one. Brand names and their variants (for example "VW" / "Volkswagen", or "FNB Private Wealth" / "FNB Private") are mapped to a single canonical brand so visibility is never split or missed.

The AI Visibility Quality Score

Every brand gets a single score from 0 to 100. It rewards being named, weighted by how prominently:

In each answer, a brand scores 1 ÷ its rank when it appears in a ranked list (1st = 1.0, 2nd = 0.5, 3rd = 0.33, and so on, capped at 10th)
A brand mentioned in passing, with no ranked position, scores just behind the last ranked item
A brand absent from an answer scores 0
The score is the average across every answer, including the ones where the brand was absent, multiplied by 100

So a brand recommended first in every single answer scores 100. A brand mentioned often but always near the bottom scores low. This is deliberate: it rewards genuine, prominent visibility rather than mere frequency. Recommendation strength (recommended vs neutrally listed vs caveated) is tracked as a separate measure, not folded into the score.

Mentions, recommendations and citations

Alongside the Quality Score, each report shows:

Mention rate - the share of answers that name the brand at all
Recommendation rate - how often, of all answers, the brand is the active recommendation
Results across the three assistants - because the models often disagree, "AI visibility" is really three different audiences
Sources cited - the publisher domains the assistants drew on, counted by the number of answers that cited each (so a page cited several times in one answer counts once)

Important notes

These are AI model outputs at a point in time. They will shift as the models and the live web change. The index is a snapshot, designed to be re-run to track movement.
This is a measure of AI visibility, not market share, revenue, or product quality. A brand can be excellent and under-visible, or modest and over-visible.
Where one assistant occasionally returned an unusable response, that observation is noted; reported figures are built on complete answers.