Case 02 — Sun Life · Adley Low · Inchcape programmes · YMYL
Updated August 2026
Making “退休年金” understandable — and findable — in AI answers
Insurance · Retirement
Client: 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.
Baseline
Invisible to AI assistants in Hong Kong on category queries. Thin, jargon-led pages; no bilingual extractable definition of 退休年金.
Intervention
- 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
Timeline
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
What made it work
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
Your baseline next
Get the same three metrics for your brand.
The free AI-visibility audit establishes your Citation Frequency, Share of Voice and Recommendation Rank baseline — the same starting point every case above began with.
Written by James Huang, Founder & CEO, Mercury Technology Solutions · Reviewed by the Mercury GAIO Practice · Updated August 2026