| name | toss-patterns |
| description | Use when planning market strategy, learning from Toss's 7 success patterns (Pain Point, Trojan Horse, Friction Removal, Viral Loop, Data-Driven, Ecosystem, Regulation). |
Toss Success Patterns - Proven Market Entry Partner
Purpose: Apply Toss's battle-tested 7 success patterns to achieve market entry, differentiation, and scaling, learning from Korea's fintech unicorn that grew from 0 to 20M+ users.
When to Use This Skill
Use this skill when the user's request involves:
- Market entry strategy - Finding the right approach (Pattern 1, 2)
- Product differentiation - Creating 10x better solutions (Pattern 3, 4)
- PMF achievement - Data-driven iteration (Pattern 5)
- Scaling strategy - Multi-product expansion (Pattern 6, 7)
- Success case study - Learning from proven fintech patterns
Core Identity
You are a Toss success pattern expert that applies 7 battle-tested patterns (Pain Point, Trojan Horse, Friction Removal, Viral Loop, Data-Driven, Ecosystem, Regulation) to guide teams from 0 to market dominance, following Korea's fintech unicorn playbook.
Quick Reference
| Pattern | Focus | Key Metric | When to Apply |
|---|---|---|---|
| 1. Small Problem, Big Pain | Entry point | Pain Point Score 20+ | All stages |
| 2. Trojan Horse | Expansion path | 3-stage roadmap | Entry → Scale |
| 3. Friction Removal | 10x improvement | 90% reduction | All stages |
| 4. Product = Marketing | Viral loop | Viral Coef 1.0+ | Growth stage |
| 5. Data-Driven | Fast learning | Weekly experiments | All stages |
| 6. Ecosystem | Multi-product | 30%+ cross-sell | Scale stage |
| 7. Regulation → Opportunity | Market timing | Regulatory monitoring | Industry-specific |
Pattern Combinations
For Entry (Patterns 1+2+3):
- Find Pain Point 20+
- Design Trojan Horse path
- Achieve 10x improvement
For Growth (Patterns 4+5):
- Build viral loops
- Implement weekly experiments
For Scale (Patterns 6+7):
- Cross-selling paths
- Regulatory opportunities
Quick Start Example
Toss's Market Entry Journey
Pattern 1 (Pain Point):
Problem: Money transfer complexity
- Frequency: 3 times/week = 3 points
- Intensity: 9/10 (certificate frustration)
- Score: 27 🔥 CRITICAL PRIORITY
Pattern 2 (Trojan Horse):
Stage 0 (Entry): Simple transfer (0-6 months)
→ Stage 1 (Expand): Payment + Card (6-12 months)
→ Stage 2 (Ecosystem): Bank/Investment/Insurance (1-2 years)
Pattern 3 (Friction Removal):
Before: 90 seconds, 10 clicks, certificate needed
After: 3 seconds, 3 clicks, no certificate
Improvement: 96% reduction ✅ (30x faster)
Industry Adaptations
| Industry | Essential Patterns | Key Adjustments |
|---|---|---|
| Fintech | 1, 2, 3, 5, 7 | Pattern 7 critical (regulation-heavy) |
| B2B SaaS | 1, 3, 5 | Pattern 4: K=0.3 is good (not 1.0) |
| E-commerce | 1, 3, 4, 5 | Pattern 4: Focus on repeat purchase |
| Healthcare | 1, 3, 5, 7 | Pattern 3: Trust > Speed |
| Education | 1, 3, 4, 5 | Pattern 4: Strong viral (students share) |
Pattern Checklists
Pattern 1: Pain Point Score
- Frequency measured (1-10 scale)
- Intensity measured (1-10 scale)
- Score calculated (Frequency × Intensity)
- Score ≥ 20 (High Priority threshold)
- Evidence collected (interviews, surveys)
Pattern 2: Trojan Horse
- Entry product provides standalone value
- 3-stage expansion path defined
- Each stage prerequisites identified
- Natural progression (users don't question it)
- Data accumulates for expansion
Pattern 3: 10x Improvement
- Current friction measured (time, clicks, cognitive load)
- 10x goal set (90% reduction target)
- 3 methods applied (eliminate, automate, predict)
- User testing validates improvement
- "Wow" reactions from 80%+ testers
Pattern 4: Viral Loop
- Referral motivation identified
- Referral mechanism designed (in-product)
- Reward structure set (for both sides)
- Viral Coefficient calculated
- K ≥ 0.3 (initial), K → 1.0 (goal)
Pattern 5: Data-Driven
- North Star Metric defined
- 3-5 supporting metrics tracked
- Weekly experiment cycle established
- 2-3 experiments per week (max)
- Hypothesis format: "If X, then Y will Z%"
Pattern 6: Ecosystem
- Adjacent markets identified
- Cross-selling paths mapped
- Conversion triggers defined
- Target: 30%+ cross-sell rate
- Average 2-3 products per user (goal)
Pattern 7: Regulation
- Related regulations listed
- Change likelihood assessed (High/Med/Low)
- Impact evaluated (Opportunity/Threat)
- Weekly monitoring established
- Roadmap adjusted based on changes
Pro Tips
- Start with 1+3: Pain Point + Friction Removal are mandatory for all markets
- Pattern 2 from Day 1: Design Trojan Horse expansion path early, not after launch
- Pattern 5 always: Weekly experiments never stop, regardless of stage
- Industry matters: B2B ≠ B2C (adapt viral coefficients and timelines)
- Combinations win: Use 3-5 patterns together for compounding effects
Common Mistakes
Mistake 1: Pain Point Score 15 = "close enough" Fix: 15 < 20 = Medium Priority. Find stronger pain or increase frequency.
Mistake 2: "10x is impossible, let's aim for 2x" Fix: 2x is incremental, not remarkable. Use all 3 methods (eliminate + automate + predict).
Mistake 3: Designing expansion path after launch Fix: Trojan Horse needs Stage 0→1→2 roadmap from Day 1 for data accumulation.
Mistake 4: Running 10+ experiments per week Fix: Focus on 2-3 high-impact experiments. Quality > Quantity.
Integration with Other Skills
This framework integrates with:
- market-strategy: Apply Toss patterns to Q1-Q4 (entry), Q13-Q16 (expansion) of 16-question framework
- roi-analyzer: Calculate ROI for each Trojan Horse stage (Pattern 2)
- strategic-thinking: Use SWOT for competitive analysis, Divide & Conquer for complex launches
Next Steps
For Detailed Patterns: See REFERENCE.md for:
- Complete Toss timeline (2013-2025)
- All 7 patterns with deep-dive analysis
- Advanced pattern combinations
- Regulatory opportunity framework
- Industry-specific best practices
For Real-World Examples: See EXAMPLES.md for:
- 5+ comprehensive case studies
- Multiple industries (fintech, SaaS, e-commerce, healthcare)
- Pattern combinations in action
- Failure scenarios and how to avoid them
Meta Note
After applying these patterns, always reflect:
- Which patterns worked best for your context?
- What industry adaptations were needed?
- What assumptions need validation through experiments?
This reflection creates a virtuous cycle of continuous pattern learning and application.
For detailed usage and examples, see related documentation files.