Role: Senior Product Designer (end-to-end)
Company: Estée Lauder Companies — La Mer
Timeline: 2023–2025
Team: Retail Ops, PM, iOS Eng, Data/ML, Brand, Research
Constraints: On-counter time box, privacy in-store, low-light conditions, multi-region locales
Platforms: iPadOS (in-store), handoff to mobile follow-ups
Tools: Figma, proto testing, analytics
Outcomes: Standardized consults; +15% AOV; higher associate confidence
Designed a guided, ML-assisted in-store consultation that standardizes La Mer recommendations at the counter.
Mapped the end-to-end retail journey (greeting → diagnosis → plan → checkout), reducing variance across consultants.
Introduced bundle logic and clearer rationale to increase attachment of complementary products.
Shipped a production iPad flow used by store associates; +15% AOV from bundled recs.
Created reusable patterns & governance later reused by other brands.
Luxury skincare consults were inconsistent and time-intensive. Associates relied on personal heuristics, leading to variability in recommendations, explanation quality, and basket size. We needed a guided, trustworthy experience that:
makes associates faster and more confident
grounds recommendations in visible signals and routine templates
gently increases attachment rate/AOV without feeling salesy.
Success criteria: increase bundle attachment, reduce idle/decision time, improve perceived professionalism and trust.
Methods: store shadowing, contextual inquiry, 1:1 interviews with associates and clients, desk research on consultation heuristics; later, moderated on-device prototype sessions.
What changed the design:
Explainability → trust. Customers commit more when they see why a product fits their concern (signal → rationale → product).
Time boxes are real. Typical consults run 8–12 minutes; any step >60s without progress feels long.
Attachment at “plan” time. Framing routines as AM/PM steps with clear roles (cleanse → treat → moisturize) improves bundle acceptance.
A/B concept tests (list vs. routine framing → routine wins on comprehension/attachment intent).
Copy tests for “Why this” (evidence-first vs. benefit-first). Week-long pilots in two stores tracking time-to-plan, Save/Share opt-in, and associate feedback.
Concept A/B: Routine framing outperformed a plain list on both comprehension (71% → 84%, +13pp) and attachment intent (44% → 58%, +14pp); n≈36, moderated.
Copy tests: “Why this” written evidence-first increased perceived trust (3.8 → 4.3, +0.5) and optional add-on selection (27% → 34%, +7pp).
Operational pilots (2 stores, 1 week): Time to plan dropped from 9m20s to 7m40s (−100s, −18%).
Attachment rate rose 44% → 56% (+12pp). Save/Share opt-in reached 41%. Associate confidence increased by +0.6 on a 5-point scale; plan completion improved 82% → 91% (+9pp).
Impact: +15% AOV from bundled recommendations (pilot basket mix vs. historical). Consistency up (standardized steps reduced variance). Associate confidence up (qualitative).
Risks & mitigations: over-prescription → optional clearly marked + diminishing-returns messaging; autonomy → substitutions and good/better/best presets; privacy → explicit consent, local processing where possible, clear deletion.
My role: led end-to-end IA, flows, interaction design, and pattern system; co-ran research synthesis & pilots with Research/Retail Ops; partnered with iOS & Data/ML on feasibility/explainability; authored governance for content updates.