Australian AI Search Visibility Index Methodology

The Australian AI Search Visibility Index is a repeatable framework for measuring whether brands are discoverable, cited, accurately described, and commercially visible across organic search and AI-assisted answer surfaces.

It is designed to avoid two common failures: treating one prompt screenshot as a market trend and combining incompatible metrics into a number that looks precise but cannot be reproduced.

What the index measures

The index has five components.

Component	Weight	What it measures
Commercial organic visibility	30%	Rankings and estimated visibility for agreed buyer-intent queries
Answer-surface inclusion	25%	Brand inclusion across a fixed, versioned prompt set
Citation and source quality	20%	Linked or named sources supporting answer appearances
Entity accuracy	15%	Whether answers describe the brand, offer, audience, and location correctly
Authority breadth	10%	Relevant referring domains and corroborating third-party sources

The weights are published before each measurement run. We do not adjust them after seeing which brand wins.

Market and sampling rules

Every edition records:

• country: Australia;
• language: English;
• search location used by the data provider;
• desktop or mobile device;
• query and prompt list version;
• collection dates;
• participating domains;
• unavailable results;
• model or product tested;
• whether a result is live, cached, or provider-modelled.

The first edition will focus on commercially relevant Australian SEO and AI search agency queries. Future vertical editions should use their own query sets and must not be compared directly without normalisation.

Commercial organic visibility

Organic visibility uses a bounded query set rather than every keyword a provider happens to detect.

Queries are grouped into:

• category terms;
• service terms;
• location-plus-service terms;
• pricing and selection terms;
• AI search, AEO, and GEO terms;
• comparison and alternative terms.

Each query receives a demand weight and an intent weight. Position is converted into a visibility curve, then multiplied by those weights. Provider estimates are labelled as modelled. First-party Google Search Console data is used only for the domain that owns it and is never implied to be available for competitors.

Answer-surface inclusion

Prompts are fixed before collection and grouped by buyer job:

1. recommendation;
2. comparison;
3. alternative;
4. pricing;
5. risk or objection;
6. category education;
7. local selection.

A brand receives inclusion credit only when it appears in the answer itself, not because its name was present in the prompt. We record answer position, wording, linked sources, unlinked mentions, and whether the answer was reproducible.

Volatile answers are sampled more than once. A single appearance is reported as an observation, not stable visibility.

Citation and source quality

Not all citations are equal. Sources are classified as:

• owned website;
• independent editorial;
• government or academic;
• industry association;
• partner or customer;
• review platform;
• directory;
• community;
• low-quality network or unclear source.

The score rewards relevant independent corroboration and useful owned source pages. It does not reward raw backlink volume or repeated links from one network.

Entity accuracy

An answer can mention a brand and still harm it.

We check whether the answer correctly states:

• brand name;
• main service category;
• Australian market presence;
• intended audience;
• service scope;
• material differentiators;
• current public facts.

Unsupported superlatives do not earn accuracy credit. Ambiguous or contradictory descriptions reduce the score.

Authority breadth

Authority breadth uses unique relevant domains, not total backlinks. Sitewide links, obvious directory networks, and duplicated domains are discounted.

The purpose is to measure how many independent public sources can corroborate a brand's category and claims.

Normalisation and scoring

Each component is converted to a 0–100 score within the edition's declared market and sample. The weighted total is:

organic × 0.30 + answer inclusion × 0.25 + citation quality × 0.20 + entity accuracy × 0.15 + authority breadth × 0.10

Scores from different editions are comparable only when the query set, prompt set, providers, locations, devices, and scoring version remain unchanged.

What the index does not claim

• It does not measure every answer a buyer could receive.
• It does not prove revenue or lead quality.
• It does not reveal a platform's private ranking formula.
• It does not treat missing provider data as zero demand.
• It does not guarantee that an observed citation will persist.
• It does not replace first-party Search Console, analytics, CRM, or sales evidence.

Reproducibility standard

Every public edition should include:

• retrieval date;
• provider;
• market, language, and device;
• query and prompt lists;
• score version;
• domain list;
• exclusions;
• known data gaps;
• a change log.

The methodology will be versioned when weights or collection rules change. Historical results keep their original version rather than being silently recalculated.

FAQs

Why combine organic and AI visibility?

Buyers move between search results and answer systems, and many AI products rely on web retrieval. Keeping both in one framework shows the shared source layer while preserving separate component scores.

Can a small brand outperform a larger one?

Yes, especially on focused commercial prompts or entity accuracy. The authority component is only 10% so raw domain scale cannot decide the whole index.

Will Searchmaxxed rank itself?

If included, Searchmaxxed will use the same declared rules and disclose that it owns the research. Competitor first-party data will never be implied.

When will the first edition be published?

After the domain set, query list, prompt set, and reproducibility files pass review. The methodology is published first so the scoring cannot be reverse-engineered around a preferred winner.

Explore AI Search Optimization or request an AI visibility audit to apply the same measurement discipline to your market.