Original research on how buyers find businesses
What these studies are for
We publish what the client work teaches us: how buyers search, what AI systems cite, and which changes move rankings, mentions and recommendations. Each study ships with its method and its data, so you can run it yourself or argue with the finding. 5 studies sit below, each with a review date.
Australian AI Search Visibility Index framework
A transparent framework for measuring how Australian brands appear in AI-assisted commercial answers without collapsing every observation into one inflated score.
Read the study →Mentioned, cited and recommended are reported separately.
Category, problem, comparison and brand-verification questions are defined before collection.
Answer, source URL, platform, market, date and retrieval conditions.
The rest of the set
Templates, blueprints, checklists and benchmarks, each built from work we ran and each safe to reuse.
Search market opportunity map
Turn the searches your market makes into an ordered commercial plan. This template connects demand, customer intent, page ownership, proof, value and effort before anyone starts publishing.
Use this when you have a keyword list, a large content backlog or several teams asking for pages, but no defensible order for what should be built first.
Website page system blueprint
Design a large website around customer decisions, not URL volume. The blueprint gives every page family a distinct job and connects the whole site to commercial outcomes.
Use this when your site has outgrown its navigation, several page families overlap or a planned expansion risks creating thin, disconnected or duplicated routes.
AI search source readiness checklist
Check whether your public record is accessible, clear, consistent and supported before chasing mentions or citations in AI-assisted search.
Use this when AI answers omit the business, confuse its facts, cite the wrong page or recommend competitors whose public evidence is easier to verify.
StudioHawk website footprint benchmark
StudioHawk's public website shows how commercial pages, proof, people and useful resources can reinforce one another. The lesson is decision completeness, not page-count imitation.
Use this when evaluating whether your website needs more pages, better page families or stronger connections between commercial intent, proof and expertise.
How each study is built
Every study names its data source, its window and its sample before the findings. Client data is anonymised and approved first. Limits sit inside the study rather than in a footnote.
A finding that fails replication gets corrected in public, and each study carries the date it was last reviewed. Where a figure comes from a paid data provider, the provider, the market, the device and the retrieval date are stated with it.