03 — Phase 1A: The Localization Matrix
Goal: decide what stays global (build once, serve everywhere) vs. localized (adapt per market) so we maximize insight quality while minimizing engineering bloat.
The governing principle: localize the edge, standardize the engine
A trend-intelligence platform is a pipeline: collect data → process with ML → surface insights → generate & screen concepts → deliver in a workflow. The intelligence (ML models, ranking, generative concepting) is universal. What's local is the data going in, the language it's in, the categories it maps to, the laws around it, and the workflow it plugs into. So we standardize the middle and localize the two ends.
GLOBAL (build once) LOCALIZED (adapt per market)
┌──────────────────────────────┐ ┌────────────────────────────────────────┐
IN → Data ingestion framework → ML/NLP core → Insight & concept engine → OUT
▲ localized data SOURCES localized WORKFLOW ▼
localized LANGUAGE/NLP packs localized COMPLIANCE
localized TAXONOMY/categories localized GTM/support
The matrix
Each capability is scored on two axes — does localizing it materially increase customer value? and how expensive/ongoing is it to localize? That yields a verdict.
| Capability | Localize? | Why | Localization cost | Verdict |
|---|---|---|---|---|
| ML/forecasting core, ranking algorithms | Global | Math is market-agnostic | n/a | Standardize 100% |
| Concept Genie generative engine (model) | Global | Same model; only prompts/data differ | Low | Standardize; localize inputs |
| Data ingestion framework | Global | One pipeline, many connectors | n/a | Standardize |
| Data sources (local e-com, menus, retailers, social) | Localize | Insight quality = local data depth; this is the #1 value driver | High, ongoing | Localize (config, not code) |
| Language / NLP packs (already 18 langs) | Localize | Trends are expressed in local language | Medium, ongoing | Localize via language packs |
| Category taxonomy (e.g., "snacking", "ayurvedic", "halal", "wellness") | Localize | Categories are culturally specific | Medium | Localize as config/data |
| Brand/competitor reference sets | Localize | Local brands differ | Medium | Localize as data |
| Compliance / data residency / privacy (GDPR, PDPA, DPDP) | Localize | Legal requirement to sell enterprise | Medium, ongoing | Localize by region (infra + policy) |
| Enterprise workflow integrations (their PLM/innovation stack, SSO) | Localize (tiered) | Big-enterprise buyers demand it; SMBs don't | High per integration | Localize only at Enterprise tier |
| Reporting / export templates, currency, units | Localize | Low effort, high polish | Low | Localize (i18n) |
| UI translation | Localize (light) | Expected but not the value driver | Low | Localize last; English-first acceptable in many B2B markets |
| Pricing, contracting, invoicing currency | Localize | WTP & finance norms differ | Low-Med | Localize commercially (doc 05) |
| Onboarding & support content/hours | Localize (tiered) | Service overhead driver | Medium | Localize by tier + region (doc 06) |
Config-not-code: the bloat firewall
The single most important engineering rule for avoiding bloat: localization must be data/configuration, never a code fork. Build a market pack abstraction:
A Market Pack = { language model(s), data-source connectors, category taxonomy, brand reference set, compliance profile, currency/format, support config }. Adding a market = shipping a new pack, not branching the product.
This keeps ~80% of the codebase shared (Assumption #2), turns "enter a market" from an engineering project into a configurable, repeatable operation, and is exactly what the automated workflow provisions.
The trade-off, made visual (cost-to-serve vs. revenue-uplift)
high │ Compliance/ │ Local data sources
rev │ residency │ Language/NLP packs
uplift│ (do it — required) │ Taxonomy (DO FIRST — high ROI)
──────┼─────────────────────┼──────────────────────────────
low │ UI full translation│ Deep per-customer workflow
rev │ (defer) │ integrations (Enterprise-only,
uplift│ │ charge for it)
└─────────────────────┴──────────────────────────────
low loc. cost high loc. cost
Do-first quadrant (high uplift, manageable cost): local data sources, language, taxonomy. Charge-for-it quadrant (high uplift, high cost): deep integrations → gate behind Enterprise tier and price as services. Defer quadrant: full UI translation. Required regardless: compliance/residency — it's table stakes to even sell.
Machine-readable version: frameworks/localization-matrix.csv.