Case 02 — Sun Life · Adley Low · Inchcape programmes · YMYL
2026年8月更新
Making “退休年金” understandable — and findable — in AI answers
Insurance · Retirement
クライアント: 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.
ベースライン
Invisible to AI assistants in Hong Kong on category queries. Thin, jargon-led pages; no bilingual extractable definition of 退休年金.
介入
- 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
タイムライン
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
成功の要因
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
次なるあなたのベースライン
貴社のブランドに同じ3つのメトリクスを。
無料のAI可視性監査により、Citation Frequency、Share of Voice、Recommendation Rankのベースラインを確立します。これは、すべてのケースが始まったのと同じ出発点です。
James Huang 著、Mercury Technology Solutions 創設者兼CEO · Mercury GAIO Practice によるレビュー · 2026年8月更新