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Get Started Free →Develop e-commerce strategy for Southeast Asian markets including platform selection, payment infrastructure, logistics challenges, and localization requirements. Use this skill when the user is expanding e-commerce to SEA, evaluating Shopee vs Lazada vs Tokopedia, or needs to understand SEA market differences — even if they say 'sell to Southeast Asia', 'which platform in Vietnam', 'SEA payment methods', or 'cross-border e-commerce in ASEAN'.
.claude/skills/asgard-ai-platform-ecom-sea-strategy/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -41% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -15% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 33% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 39% | 0% |
| case-14 | ✗→✓ | ▲ Improved | -35% | 0% |
IRON LAW: SEA Is Not One Market — It's 10+ Different Markets
Indonesia, Vietnam, Thailand, Philippines, Malaysia, and Singapore have
different languages, payment preferences, logistics infrastructure, and
consumer behaviors. A strategy that works in Singapore (high digital
maturity, English-speaking) will fail in Indonesia (cash-heavy, Bahasa,
island logistics). Analyze each country individually.| Country | Pop. | E-com Penetration | Top Platform | Dominant Payment | Key Challenge | |---------|------|------------------|-------------|-----------------|---------------| | Indonesia | 278M | ~35% | Tokopedia/Shopee | Bank transfer, e-wallets (GoPay, OVO) | Island logistics, last-mile cost | | Vietnam | 100M | ~30% | Shopee | COD (~60%), e-wallets (MoMo) | COD returns high (~15-20%) | | Thailand | 72M | ~40% | Shopee/Lazada | Bank transfer (PromptPay), COD | Fragmented social commerce | | Philippines | 115M | ~25% | Shopee/Lazada | GCash, COD | Island logistics, low trust in online | | Malaysia | 33M | ~45% | Shopee | FPX bank transfer, e-wallets | Small market, competitive | | Singapore | 6M | ~55% | Shopee/Lazada/Amazon | Credit cards, PayNow | Tiny market, high CAC |
| Factor | Marketplace (Shopee/Lazada) | Own D2C Site | Social Commerce (IG/LINE/TikTok) | |--------|---------------------------|-------------|--------------------------------| | Traffic | Built-in | Must generate yourself | Organic but unpredictable | | Commission | 3-15% + ads | 0% + payment/hosting costs | 0% + fulfillment | | Data ownership | Platform owns | You own | Partial | | Brand control | Limited | Full | Medium | | Best for | Market entry, volume | Brand building, repeat customers | Viral products, low-trust markets |
Phase 1: Market Selection (pick ONE country first)
Phase 2: Platform Setup
Phase 3: Localization
Phase 4: Scale
markdown# SEA E-Commerce Strategy: {Brand/Product} ## Market Selection | Country | Opportunity | Competition | Barriers | Score | |---------|-----------|-------------|---------|-------| | {country} | H/M/L | H/M/L | H/M/L | {total} | ## Recommended Entry: {Country} - Platform: {which marketplace} - Payment: {accepted methods} - Logistics: {fulfillment approach} - Timeline: {phases with milestones} ## Localization Requirements {Language, pricing, cultural adaptations needed} ## Budget Estimate | Item | Cost | Notes | |------|------|-------| | Platform setup | ${X} | ... | | Inventory | ${X} | ... | | Marketing (first 3 months) | ${X} | ... |
references/sea-regulations.mdreferences/platform-fees.md| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 44,907 | 26,660 | -41% | 1 | 1 | 0% | 5,897 | 4,751 | -19% | 0 | 0 | — |
case-02 | fail→pass | 118,536 | 41,954 | -65% | 1 | 1 | 0% | 8,269 | 4,910 | -41% | 0 | 0 | — |
case-03 | fail→pass | 36,524 | 26,855 | -26% | 1 | 1 | 0% | 5,665 | 4,820 | -15% | 0 | 0 | — |
case-04 | fail→pass | 25,773 | 17,743 | -31% | 1 | 1 | 0% | 2,979 | 3,955 | +33% | 0 | 0 | — |
case-05 | pass→pass | 19,372 | 19,627 | +1% | 1 | 1 | 0% | 2,560 | 3,455 | +35% | 0 | 0 | — |
case-06 | fail→fail | 13,786 | 11,845 | -14% | 1 | 1 | 0% | 2,099 | 2,755 | +31% | 0 | 0 | — |
case-17 | fail→fail | 49,468 | 34,946 | -29% | 1 | 1 | 0% | 7,089 | 5,714 | -19% | 0 | 0 | — |
case-07 | pass→pass | 19,632 | 20,327 | +4% | 1 | 1 | 0% | 2,587 | 4,263 | +65% | 0 | 0 | — |
case-08 | fail→fail | 21,846 | 27,430 | +26% | 1 | 1 | 0% | 3,228 | 5,312 | +65% | 0 | 0 | — |
case-09 | pass→pass | 20,819 | 21,291 | +2% | 1 | 1 | 0% | 2,697 | 4,158 | +54% | 0 | 0 | — |
case-10 | fail→pass | 19,171 | 19,403 | +1% | 1 | 1 | 0% | 2,577 | 3,584 | +39% | 0 | 0 | — |
case-11 | pass→pass | 17,629 | 19,600 | +11% | 1 | 1 | 0% | 2,586 | 4,201 | +62% | 0 | 0 | — |
case-12 | fail→fail | 18,479 | 18,800 | +2% | 1 | 1 | 0% | 2,621 | 3,558 | +36% | 0 | 0 | — |
case-13 | pass→pass | 19,894 | 21,553 | +8% | 1 | 1 | 0% | 2,611 | 4,310 | +65% | 0 | 0 | — |
case-14 | fail→pass | 35,850 | 23,112 | -36% | 1 | 1 | 0% | 6,091 | 3,932 | -35% | 0 | 0 | — |
case-15 | pass→pass | 31,596 | 23,719 | -25% | 1 | 1 | 0% | 4,587 | 4,171 | -9% | 0 | 0 | — |
case-16 | fail→fail | 37,414 | 28,531 | -24% | 1 | 1 | 0% | 6,703 | 5,630 | -16% | 0 | 0 | — |
case-18 | fail→pass | 32,596 | 23,676 | -27% | 1 | 1 | 0% | 4,387 | 4,274 | -3% | 0 | 0 | — |
case-19 | pass→pass | 32,457 | 21,127 | -35% | 1 | 1 | 0% | 2,743 | 4,453 | +62% | 0 | 0 | — |
case-20 | fail→pass | 23,903 | 14,545 | -39% | 1 | 1 | 0% | 2,475 | 3,471 | +40% | 0 | 0 | — |
case-21 | pass→pass | 21,449 | 18,746 | -13% | 1 | 1 | 0% | 2,872 | 3,845 | +34% | 0 | 0 | — |
case-22 | pass→pass | 25,245 | 29,568 | +17% | 1 | 1 | 0% | 3,266 | 5,439 | +67% | 0 | 0 | — |
case-23 | pass→pass | 33,940 | 17,938 | -47% | 1 | 1 | 0% | 3,257 | 3,824 | +17% | 0 | 0 | — |
case-24 | pass→pass | 20,718 | 34,513 | +67% | 1 | 1 | 0% | 3,504 | 5,716 | +63% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +29 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.