I explored how our Dynamic Company Sector Classification (DCSC) and multi‑level taxonomy could strengthen a Revolut‑style investing experience.

The goal was to shorten the path from theme → discovery → allocation, while improving transparency and control.

Why this matters to European product orgs

• Supports thematic investing while keeping methodology transparent and readable.
• Designed to fit European regulatory expectations (clear risk language, methodology explainer) and GDPR‑sensitive handling (no PII required for core features, opt‑in analytics).
• Built to slot into existing design systems with minimal re‑theming and easy localization (labels, numbers, dates).

Problem & Opportunity

Pain points I targeted

• Discovery pain: Users think in themes, but must research across sources to find relevant companies.
• Transparency gap: True exposure by sector is hard to see beyond a single label.
• Action gap: Even with ideas, allocating across a theme is fiddly and time‑consuming.

Opportunity‍

Use DCSC’s L1–L4 taxonomy and a Relevance score to:

1. find companies aligned with a theme, 2) reveal composition & concentration, and 3) enable guided allocation.

Solution (Three Modules)

A. Portfolio Builder

• Input: pick themes/sectors or type your own; start from popular lists.
• Output: a curated, relevance‑ranked list of companies (public/private toggle).
• Controls: Market cap, country, relevance, max securities, max exposure; allocation by relevance or equal weight.
• Feedback: Inline portfolio stats and a short loop: Add → Cap exposure → Allocate.

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B. Portfolio Analysis

• Composition: Multi‑level sector breakdown (L1–L4) with top‑sector callouts and blind‑spot flags.
• Signals: Performance, risk, and performance‑to‑risk for quick comparison.

C. Instrument Sector Panel

• For any stock, surface sector weights and similar companies by sector overlap to speed idea exploration.

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Process & Key Decisions

• Mental model: Let users start from themes, not tickers; keep Relevance first‑class (sorting, badges, allocation).
• Key decisions: Levelled taxonomy (L1–L4), a short discovery → action loop, and a consistent metric trio (performance, risk, performance‑to‑risk).
• Trust surface: Add a "What is Relevance?" explainer and data timestamping near controls.
• Integration blueprint: Where the feature can live (Watchlists, Portfolio creation, Details), event hooks, and ownership of computation vs. storage.
• Pitch storyline: Idea → ranked list → filters → add → allocate → analyse, with a light technical slide for effort and analytics.

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Outcome & Learnings

Although not pursued, the pitch hardened our integration patterns, clarified the data‑to‑UI contract, and produced a reusable partner story.

What worked: The two‑module story (Builder + Analysis), relevance‑driven allocation, and multi‑level composition.

What I’d tighten next time for EU orgs:

• Make the pilot plan explicit (markets, audience size, duration, responsibilities).
• Bring methodology & risk copy earlier (MiFID‑aligned disclaimers, plain language).
• Quantify expected business outcomes (activation lift, AUM influenced, retention via portfolio stickiness).

Next Steps & Success Metrics (Pilot‑ready)

Scope: 2–3 themes (e.g., AI, Clean Energy, FinTech) in 1–2 EU markets; 4–6 weeks; targeted retail cohort.

Primary KPIs: time‑to‑portfolio, add‑to‑portfolio rate, avg. positions per theme, diversification index, revisit rate; secondary: AUM influenced.

Rollout plan: limited A/B, then gated expansion; measure lift vs. control; publish a short methodology note.

Risks & mitigations: data trust (explainers, timestamps), over‑selection (caps & presets), brand fit (modular components).