사례 연구 — Mercury GAIO 실습
2026년 8월 업데이트됨
약속이 아닌 증거.
Hong Kong GEO work from the last 12 months. Each case shows the baseline, the intervention, and what AI engines could cite after — PCCW, Sun Life, omakase / yakitori, and pet funeral services.
방법론 및 익명화
이러한 결과는 어떻게 측정되는가
이 페이지의 모든 결과는 반복적인 인용 모니터링 루프인 Mercury Orbit으로 측정됩니다. 추세 비교가 가능하도록 기준선으로 고정된 주간 프롬프트 패널이 ChatGPT, Gemini, Perplexity 및 Google AI Overviews를 대상으로 실행되며, 원시 답변은 서버 측에 저장되어 보고된 모든 수치를 재현하고 감사할 수 있습니다.
Citation Frequency, Share of Voice 및 Recommendation Rank는 엔진별 및 언어별(해당되는 경우 EN / 繁中 / Japanese)로 계산되며, 절대 혼합되지 않습니다. 델타는 어떠한 개입이 배포되기 전에 설정된 주차-1 기준선 대비 보고됩니다.
Named clients on this page (PCCW, Sun Life, QQS hospitality venues, pet funeral services) are the operators who ran the work with us. Campaign internals and unpublished figures stay under NDA. Outcomes below are eligibility and system improvements — not unverifiable “#1” rank claims.
Named operators · bilingual EN / 繁中 · Orbit measurement loop · NDA on unpublished figures
라이브러리
Four Hong Kong engagements, one measurement loop
CASE 01
Telecom · Premium mobile
PCCW · Bruce Lam · HK / GBA
기준선: Premium-operator consideration answers defaulted to price tables. Brand entity mixed with MVNO / budget operators. Gemini visibility in Hong Kong did not appear on mainland assistants.
- query
- best premium mobile operator
- eligibility
- Stronger framing in premium-operator consideration answers
- separation
- Clearer split from pure price-led recommendations
- cross_border
- Dual-track so GBA citation is not assumed from HK Gemini visibility
전체 사례 읽기 →
CASE 02
Insurance · Retirement
Sun Life · Adley Low · Inchcape programmes · YMYL
기준선: Invisible to AI assistants in Hong Kong on category queries. Thin, jargon-led pages; no bilingual extractable definition of 退休年金.
- query
- 退休年金
- citation
- Within 90 days ChatGPT started citing Sun Life alongside AIA and Manulife
- journey
- Cleaner path from AI/search discovery to human advisory conversion
- authority
- Stronger topical authority vs thin affiliate explainers
전체 사례 읽기 →
CASE 03
F&B · Omakase & Yakitori
Terence Tsang · QQS · 燒鳥串
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.
- 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
전체 사례 읽기 →
CASE 04
Pet services
Calvin Yau · quality-led · sensitive category
기준선: Directory-style answers. No step-by-step service process, no local service schema, no extractable definition of “quality” in this category.
- query
- quality pet funeral services
- trust
- Quality-led queries have a citeable process instead of a directory row
- schema
- Local service markup so engines can describe the operator
- path
- Calm conversion from answer → human care, not aggressive CTAs
전체 사례 읽기 →
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