Case 02 — Sun Life · Adley Low · Inchcape programmes · YMYL
更新於 2026 年 8 月
Making “退休年金” understandable — and findable — in AI answers
Insurance · Retirement
客戶： 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.
基線
Invisible to AI assistants in Hong Kong on category queries. Thin, jargon-led pages; no bilingual extractable definition of 退休年金.
干預措施
- 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
時間軸
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
成功之處
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
您的下一條基準線
為您的品牌獲取相同的三個指標。
免費的 AI 可視性審計將建立您的 Citation Frequency、Share of Voice 和 Recommendation Rank 基線——這是每個案例都始於的相同起點。
作者：James Huang，創辦人兼 CEO，Mercury Technology Solutions · 審閱者：Mercury GAIO Practice · 更新於 2026 年 8 月