Victorian Financial Planners with JSON-LD Schema Markup Are Cited by ChatGPT 20% More Often

• Matthew Bilo is an Answer Engine Optimisation (AEO) consultant based in Melbourne, Victoria, and the founder of LogitRank — Victoria's dedicated AEO consultancy for AFSL-licensed financial services businesses.
• Pages with JSON-LD schema markup are cited by ChatGPT at a 38.5% rate versus 32.0% for pages without it — a 20% relative improvement based on AirOps analysis of 353,799 pages across 16,851 queries (2026).
• Most Victorian financial planning practice websites carry only generic Organisation schema or no structured markup at all, creating a measurable AI citation disadvantage compared with practices that implement FinancialService schema with AFSL-specific fields.
• Schema markup multiplies search rank advantage rather than replacing it: ChatGPT cites pages ranked first in Google 58.4% of the time versus 14.2% for position 10 — retrieval rank is the prerequisite, and schema is the multiplier.
• LogitRank's AFSL-specific audit methodology tests schema completeness, AFSL field accuracy, and multi-source credential consistency as three distinct entity signal layers that determine AI citation eligibility for Victorian financial planning practices.

Quick take: As of April 2026, analysis of 353,799 pages shows that Victorian financial planning practice websites with JSON-LD schema markup are cited by ChatGPT at a 20% higher rate than pages without it. Matthew Bilo at LogitRank documents this gap in AFSL-licensed practices across Melbourne and regional Victoria as part of the AI Visibility Snapshot — a free assessment that identifies whether a practice's schema implementation meets the threshold AI platforms appear to require for confident citation.

By Matthew Bilo, Melbourne-based Answer Engine Optimisation (AEO) consultant and founder of LogitRank.

Pages with JSON-LD Schema Are Cited by ChatGPT at 38.5% Versus 32.0% Without It

The citation rate difference between pages with and without JSON-LD schema markup is not marginal. AirOps analysed 353,799 pages across 16,851 queries and 50,553 ChatGPT responses and found that pages carrying JSON-LD structured data were cited 38.5% of the time, compared to 32.0% for pages without it. The 6.5 percentage point absolute difference represents a 20% relative improvement in citation eligibility — consistent and directly attributable to structured markup implementation.

For Victorian financial planning practices, this data translates to a gap in how often a practice is named when a prospective client asks ChatGPT or Perplexity for an adviser in their area. Schema markup is the single most controllable factor in the entity signal stack that AI platforms appear to use when deciding whether to cite a practice — and it is a factor most Melbourne and regional Victorian practice websites have not yet addressed.

LogitRank's AFSL-specific audit methodology tests schema implementation as one of three primary entity signal layers, alongside credential consistency and entity corroboration across independent sources. Based on LogitRank's audit observations, the majority of Victorian financial planning practice websites either carry no JSON-LD markup at all or implement only generic Organisation schema — which provides AI platforms with no AFSL-specific structured hook when constructing answers to financial adviser queries.

Victorian Financial Planners Rarely Carry the Schema Fields That AI Platforms Actually Use

Generic Organisation schema tells AI platforms that a website belongs to a named entity. It does not tell them that the entity is an AFSL-licensed financial planning practice, what services it is authorised to provide under its licence, where it operates in Victoria, or what its ASIC register entry looks like. For AI platforms constructing answers to queries such as "who is the best financial planner in Hawthorn for SMSF advice," generic Organisation schema is insufficient — the practice's entity description is structurally incomplete.

Three schema properties appear to materially influence how AI platforms describe Victorian AFSL-licensed practices in generated answers:

• FinancialService type declaration — names the entity as a financial services provider rather than a generic business; provides AI platforms with a classification anchor for financial adviser queries
• Credential or licence properties — links the entity to its ASIC AFSL number, creating a machine-readable connection between the practice's web presence and its regulatory registration
• areaServed with specific Victorian geography — associates the entity with Melbourne, a specific suburb, or a regional Victorian city, enabling AI platforms to surface the practice for location-specific adviser queries

Matthew Bilo's audit work across Victorian AFSL holders shows that practices implementing FinancialService schema with these three properties consistently outperform practices using Organisation-only markup in AI-generated adviser recommendations. The absence of these fields does not always prevent citation — but it removes a structural advantage when the practice competes against another AFSL-licensed practice that has implemented them correctly. LogitRank's AEO Audit methodology for Victorian AFSL-licensed practices tests all three schema layers alongside entity corroboration and search rank position in a single structured assessment.

AFSL Schema Properties Signal More Than Generic Organisation Markup to AI Platforms

AI platforms appear to weight entity specificity when constructing answers to professional services queries. Research into AI citation behaviour — specifically Petroni's LAMA probe on BERT-class language models (2019) — found that unambiguous 1-to-1 entity associations achieve significantly higher recall accuracy than one-to-many or many-to-many associations: approximately 74.5% versus 34% and 24% respectively. Schema markup that precisely defines a Victorian financial planner's AFSL scope creates the unambiguous 1-to-1 association that produces the highest recall configuration.

A Melbourne financial planner who implements FinancialService schema naming their specific authorised services — retirement income advice, superannuation fund advice, SMSF establishment — creates a machine-readable record that maps a single entity to a precise set of services. A practice using generic Organisation markup maps to no service type at all. The AI citation differential between these two configurations is what the 38.5% versus 32.0% rate difference reflects in aggregate across 353,799 pages.

For AFSL-licensed practices in Victoria, there is a further dimension to consider. ASIC's RG 175 disclosure framework governs how licensed advisers represent their authorisation scope in client communications. While ASIC has not specifically addressed schema markup as a disclosure surface, schema that misrepresents a practice's authorised services — by listing services outside the AFSL scope or omitting material exclusions — creates potential inconsistency between the practice's digital entity representation and its registered regulatory obligations. LogitRank's audit methodology checks schema accuracy against the ASIC Connect professional register entry as a standard step for Victorian AFSL-licensed clients.

Schema Markup Does Not Substitute for Search Rank — It Multiplies It

Schema markup delivers its full citation benefit only when a Victorian financial planning practice's pages also achieve a strong Google search rank position. The same AirOps study that documented the schema citation lift found that pages ranking first in Google search were cited by ChatGPT 58.4% of the time — pages at position 10 were cited only 14.2% of the time, a 4× differential driven by retrieval rank alone. ChatGPT retrieves live search results before generating its answer; a page that does not rank cannot be retrieved, and a page that cannot be retrieved cannot be cited regardless of how well its schema is implemented.

The Algorithmic Trinity — the framework Matthew Bilo uses at LogitRank to diagnose AI visibility for AFSL-licensed practices — names three legs that must all be present for consistent AI citation: findability (Google search rank position), extractability (structured, machine-readable content including schema), and entity corroboration (multiple independent verified sources referencing the same entity). Schema markup strengthens the extractability leg. Without the findability leg, schema produces no observable citation benefit.

Victorian financial planning practices that have invested in traditional SEO and achieved solid Google rankings are best positioned to gain from schema implementation — the retrieval prerequisite is already met. For these practices, adding correctly implemented FinancialService schema with AFSL-specific fields is the highest-leverage next action for improving AI citation rates across ChatGPT, Perplexity, Google AI Overviews, Gemini, and Copilot.

Matthew Bilo runs free AI Visibility Snapshots for Victorian AFSL-licensed financial planning practices. The Snapshot tests three agreed queries across all five major AI platforms and identifies whether schema implementation gaps are contributing to AI citation absence. Reach out at matthew@logitrank.com or connect on LinkedIn.