Case 03 — Terence Tsang · QQS · 燒鳥串
更新于 2026 年 8 月
Winning consideration for “best omakase” and “best yakitori” in Hong Kong
F&B · Omakase & Yakitori
客户： 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.
基线
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.
干预
- 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
时间线
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
成功之处
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
您的下一条基线
为您的品牌获取相同的三个指标。
免费的 AI 可见性审计将建立您的 Citation Frequency、Share of Voice 和 Recommendation Rank 基线——这是所有上述案例开始的共同起点。
作者：James Huang，创始人兼首席执行官，Mercury Technology Solutions · 由 Mercury GAIO Practice 审核 · 更新于 2026 年 8 月