Case 02 — Sun Life · Adley Low · Inchcape programmes · YMYL
更新于 2026 年 8 月
Making “退休年金” understandable — and findable — in AI answers
Insurance · Retirement
客户： 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.
基线
Invisible to AI assistants in Hong Kong on category queries. Thin, jargon-led pages; no bilingual extractable definition of 退休年金.
干预
- 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
时间线
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
成功之处
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