Case 02 — Sun Life · Adley Low · Inchcape programmes · YMYL
2026년 8월 업데이트됨
Making “退休年金” understandable — and findable — in AI answers
Insurance · Retirement
클라이언트: Sun Life — Adley Low, CMO
Retirement annuity content was jargon-heavy and fragmented. Assistants struggled to extract plain-language definitions, eligibility logic and trustworthy process steps — so competitors and generic explainers owned the answer space, and the brand was invisible to AI assistants in Hong Kong.
기준선
Invisible to AI assistants in Hong Kong on category queries. Thin, jargon-led pages; no bilingual extractable definition of 退休年金.
개입
- Designed a bilingual answer hub: definitions, who it’s for, how it works, decision criteria
- Structured glossary and FAQ for LLM extraction without overclaiming returns
- Mapped Organization / FinancialService-style entities and supporting product explainers
- Improved internal linking from educational content to consultation paths
- Compliance-aware copy rules: clarity first, no guaranteed-outcome language
타임라인
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
성공 요인
YMYL answers are won with extractable process and named entities, not volume. The 90-day citation movement followed the answer hub — not a content blast.
what changed — 12-week programme
- 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
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