Agent Skills: Toss Success Patterns - Proven Market Entry Partner

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

1. Start with 1+3: Pain Point + Friction Removal are mandatory for all markets
2. Pattern 2 from Day 1: Design Trojan Horse expansion path early, not after launch
3. Pattern 5 always: Weekly experiments never stop, regardless of stage
4. Industry matters: B2B ≠ B2C (adapt viral coefficients and timelines)
5. 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.