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Case 03 — Terence Tsang · QQS · 燒鳥串

Mis à jour août 2026

Winning consideration for “best omakase” and “best yakitori” in Hong Kong

F&B · Omakase & Yakitori

Client : QQS Hospitality Consulting — Terence Tsang

Premium omakase and yakitori discovery in Hong Kong was dominated by listicles and review platforms. The venues had excellent product — but thin, non-citeable web structure, so AI answers defaulted to generic “best of” roundups.

Ligne de base

AI shortlists cited third-party lists. No Restaurant/LocalBusiness schema, no bilingual “what defines great omakase / 燒鳥串” blocks, weak entity disambiguation vs similarly named venues.

Intervention

  • Built answer architecture around comparison criteria AI can quote: chef narrative, course structure, sourcing, price-band transparency, reservation path
  • Deployed Restaurant / LocalBusiness schema with menu and offer clarity
  • Created bilingual FAQ and educational blocks for omakase and 燒鳥串 / yakitori
  • Strengthened entity disambiguation vs similarly named venues and city-wide list pages
  • Aligned booking CTAs so AI summaries could point to a clean next step

Chronologie

12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.

Ce qui a fonctionné

F&B GEO is local entity work. Signature dishes, chef, neighbourhood and booking path have to be machine-legible or the listicle remains the source.

what changed — 12-week programme

queries
best omakase in Hong Kong · best yakitori · best 燒鳥串
eligibility
Clearer eligibility in AI shortlists for premium dining intent
density
Stronger on-site answer density for comparison queries
citation_path
Reduced reliance on third-party listicles as the only citeable source

Votre ligne de base ensuite

Obtenez les trois mêmes métriques pour votre marque.

L'audit gratuit de visibilité IA établit votre Citation Frequency, Share of Voice et Recommendation Rank de référence — le même point de départ avec lequel tous les cas ci-dessus ont commencé.

Audit gratuit de visibilité IA Toutes les études de cas
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Rédigé par James Huang, Fondateur & PDG, Mercury Technology Solutions · Révisé par la pratique Mercury GAIO · Mis à jour août 2026