Case 03 — Terence Tsang · QQS · 燒鳥串
2026년 8월 업데이트됨
Winning consideration for “best omakase” and “best yakitori” in Hong Kong
F&B · Omakase & Yakitori
클라이언트: 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.
기준선
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.
개입
- 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
타임라인
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
성공 요인
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
다음 기준선은 귀사의 차례
귀하의 브랜드에 대한 동일한 세 가지 지표를 확인하세요.
무료 AI 가시성 감사를 통해 귀하의 Citation Frequency, Share of Voice, Recommendation Rank 기준선을 설정합니다. 모든 성공 사례가 시작했던 동일한 출발점입니다.
작성자: James Huang, Founder & CEO, Mercury Technology Solutions · 검토: Mercury GAIO Practice · 업데이트: 2026년 8월