{ "schemaVersion": 1, "title": "AuditHQ historical Australian website screening study: recorded aggregate query results", "studyUrl": "https://audithq.com.au/learn/australian-websites-ai-readiness-study", "snapshotDate": "2026-09-14", "sampleDates": { "first": "2026-06-22", "last": "2026-09-10", "basis": "Database date-range summary recorded at the time; the cohort queries did not specify an explicit date filter." }, "methodology": { "unit": "Exact domain string ending in .au", "selection": "Completed Quick Scans with non-null overall_score; latest created_at per exact domain string.", "segmentSelection": "One matching prospect segment per domain; four reported segments cover 500 of the 515 domains.", "scores": "Historical screening category scores, not measured rankings, visits, AI recommendations or citations.", "population": "Non-random prospect-heavy sample; not representative of all Australian businesses." }, "provenance": "Original Supabase aggregate query responses recorded on 14 September 2026, recovered from the archived execution record on 5 October 2026.", "reproducibility": "These are recorded aggregate outputs, not a frozen row-level database export. The live database can change through new scans and retention. Re-running the SQL now need not reproduce the full historical cohort. Query outputs contain no individual domains or contacts.", "queries": [ { "id": "category-summary", "executedAt": "2026-09-14T03:45:15.962Z", "sql": "with au as (\n select distinct on (domain) domain, overall_score, bucket_scores, issue_counts\n from quick_scans\n where status='completed' and overall_score is not null and domain like '%.au'\n order by domain, created_at desc\n), b as (\n select a.domain, x->>'key' as bucket, (x->>'score')::int as score\n from au a, jsonb_array_elements(a.bucket_scores) x\n)\nselect bucket,\n count(*) as sites,\n round(avg(score),1) as avg_score,\n count(*) filter (where score < 50) as under_50,\n round(100.0*count(*) filter (where score < 50)/count(*),1) as pct_under_50,\n count(*) filter (where score < 30) as under_30\nfrom b group by bucket order by avg_score;", "rows": [ { "bucket": "ai_readiness", "sites": 515, "avg_score": "44.4", "under_50": 341, "pct_under_50": "66.2", "under_30": 141 }, { "bucket": "reputation_signals", "sites": 515, "avg_score": "57.9", "under_50": 183, "pct_under_50": "35.5", "under_30": 70 }, { "bucket": "hiring_signals", "sites": 515, "avg_score": "61.7", "under_50": 133, "pct_under_50": "25.8", "under_30": 53 }, { "bucket": "technical_health", "sites": 515, "avg_score": "65.4", "under_50": 78, "pct_under_50": "15.1", "under_30": 9 }, { "bucket": "trust", "sites": 515, "avg_score": "71.4", "under_50": 113, "pct_under_50": "21.9", "under_30": 37 }, { "bucket": "visibility", "sites": 515, "avg_score": "73.4", "under_50": 78, "pct_under_50": "15.1", "under_30": 7 } ] }, { "id": "headline-summary", "executedAt": "2026-09-14T03:45:54.688Z", "sql": "with au as (\n select distinct on (domain) domain, overall_score, bucket_scores\n from quick_scans\n where status='completed' and overall_score is not null and domain like '%.au'\n order by domain, created_at desc\n), p as (\n select domain, overall_score,\n max(case when x->>'key'='visibility' then (x->>'score')::int end) as visibility,\n max(case when x->>'key'='ai_readiness' then (x->>'score')::int end) as ai_readiness,\n max(case when x->>'key'='technical_health' then (x->>'score')::int end) as tech\n from au, jsonb_array_elements(bucket_scores) x group by domain, overall_score\n)\nselect count(*) as sites,\n round(avg(visibility),1) as avg_visibility,\n round(avg(ai_readiness),1) as avg_ai,\n round(avg(visibility-ai_readiness),1) as avg_gap,\n count(*) filter (where visibility>=70 and ai_readiness<50) as strong_search_weak_ai,\n round(100.0*count(*) filter (where visibility>=70 and ai_readiness<50)/count(*),1) as pct_strong_search_weak_ai,\n count(*) filter (where ai_readiness < visibility) as ai_below_vis,\n round(100.0*count(*) filter (where ai_readiness < visibility)/count(*),1) as pct_ai_below_vis,\n round(avg(overall_score),1) as avg_overall,\n count(*) filter (where overall_score>=80) as grade_a, count(*) filter (where overall_score<50) as under_50\nfrom p;", "rows": [ { "sites": 515, "avg_visibility": "73.4", "avg_ai": "44.4", "avg_gap": "29.0", "strong_search_weak_ai": 149, "pct_strong_search_weak_ai": "28.9", "ai_below_vis": 471, "pct_ai_below_vis": "91.5", "avg_overall": "62.4", "grade_a": 77, "under_50": 100 } ] }, { "id": "prospect-coverage", "executedAt": "2026-09-14T03:46:13.779Z", "sql": "with au as (\n select distinct on (domain) domain from quick_scans\n where status='completed' and overall_score is not null and domain like '%.au'\n order by domain, created_at desc\n)\nselect\n (select count(*) from au) as au_domains,\n (select count(*) from au a where exists (select 1 from growth_accounts g where g.domain = a.domain)) as from_growth_pipeline,\n (select count(*) from au a where not exists (select 1 from growth_accounts g where g.domain = a.domain)) as not_in_pipeline;", "rows": [ { "au_domains": 515, "from_growth_pipeline": 503, "not_in_pipeline": 12 } ] }, { "id": "segment-counts", "executedAt": "2026-09-14T03:46:26.466Z", "sql": "with au as (\n select distinct on (domain) domain from quick_scans\n where status='completed' and overall_score is not null and domain like '%.au' order by domain, created_at desc\n), j as (\n select a.domain, g.segment, g.location_state, g.source\n from au a join lateral (select * from growth_accounts g where g.domain=a.domain limit 1) g on true\n)\nselect coalesce(segment,'(none)') as segment, count(*) n from j group by 1 order by n desc limit 15;", "rows": [ { "segment": "marketing-agency", "n": 143 }, { "segment": "real-estate-agent", "n": 139 }, { "segment": "web-developer", "n": 135 }, { "segment": "accountant", "n": 83 }, { "segment": "saas", "n": 1 }, { "segment": "saas-marketing-lead", "n": 1 }, { "segment": "digital marketing agency", "n": 1 } ] }, { "id": "segment-scores", "executedAt": "2026-09-14T03:46:43.373Z", "sql": "with au as (\n select distinct on (domain) domain, overall_score, bucket_scores from quick_scans\n where status='completed' and overall_score is not null and domain like '%.au' order by domain, created_at desc\n), j as (\n select a.*, g.segment from au a join lateral (select segment from growth_accounts g where g.domain=a.domain limit 1) g on true\n where g.segment in ('marketing-agency','real-estate-agent','web-developer','accountant')\n), p as (\n select domain, segment, overall_score,\n max(case when x->>'key'='visibility' then (x->>'score')::int end) as vis,\n max(case when x->>'key'='ai_readiness' then (x->>'score')::int end) as ai\n from j, jsonb_array_elements(bucket_scores) x group by domain, segment, overall_score\n)\nselect segment, count(*) sites,\n round(avg(overall_score),1) avg_overall,\n round(avg(vis),1) avg_visibility,\n round(avg(ai),1) avg_ai_readiness,\n round(avg(vis-ai),1) avg_gap,\n round(100.0*count(*) filter (where ai<50)/count(*),1) pct_ai_under_50,\n round(100.0*count(*) filter (where ai