Case 02 — Sun Life · Adley Low · Inchcape programmes · YMYL
Mis à jour août 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.
Ligne de base
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
Chronologie
12-week programme. Eligibility and answer-structure outcomes below — unpublished Orbit figures stay under NDA.
Ce qui a fonctionné
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
Votre ligne de base ensuite
Obtenez les trois mêmes métriques pour votre marque.
L'audit gratuit de visibilité IA établit votre Citation Frequency, Share of Voice et Recommendation Rank de référence — le même point de départ avec lequel tous les cas ci-dessus ont commencé.
Rédigé par James Huang, Fondateur & PDG, Mercury Technology Solutions · Révisé par la pratique Mercury GAIO · Mis à jour août 2026