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ADR-004: Viral Mechanics Implementation

Status​

Accepted

Context​

The CRM must achieve a K-factor of 2.5+ through viral mechanics. We need a system that incentivizes sharing and data contribution while preventing abuse and maintaining sustainable unit economics.

Decision​

Implement a multi-loop viral system with credit rewards, gamification, and progressive value unlocking tied directly to user contributions.

Viral Architecture​

User Actions → Viral Engine → Rewards
     ↓              ↓           ↓
  Analytics    Credit System  Features
     ↓              ↓           ↓
  K-Factor     Balance Mgmt  Unlocking

Viral Loops Design​

1. Import-to-Earn Loop​

  • Import contacts → Earn 10 credits/contact
  • Verify data → Earn 5 credits/verification
  • Share enriched data → Earn 2 credits/share

2. QR Scan Viral Loop​

  • Create QR → Recipients scan → Creator earns 2 credits
  • Scanner imports contact → Both earn credits
  • Chain reaction through network

3. Team Amplification Loop​

  • Invite team members → 50 credit bonus
  • Shared contact pool → Collective enrichment
  • Team achievements → Multiplier effects

4. Introduction Success Loop​

  • Request intro → Successful connection → 20 credits
  • Both parties benefit → Network strengthening
  • Trust score increases → More intro opportunities

5. Data Quality Loop​

  • Contribute accurate data → Verification rewards
  • Community validates → Reputation points
  • Higher reputation → Bonus multipliers

Implementation Details​

Credit Economy​

pub struct CreditSystem {
    // Earning rates
    import_credit: i32 = 10,
    verify_credit: i32 = 5,
    referral_credit: i32 = 50,
    scan_credit: i32 = 2,
    
    // Spending rates
    enrichment_cost: i32 = 3,
    export_cost: i32 = 10,
    premium_feature_cost: i32 = 20,
    
    // Limits
    daily_earn_cap: i32 = 500,
    max_balance: i32 = 10_000,
}

Gamification Elements​

  1. XP System: Actions earn experience points
  2. Achievements: Unlock badges and rewards
  3. Leaderboards: Optional competitive elements
  4. Streaks: Daily active use bonuses
  5. Levels: Progressive feature unlocking

Anti-Abuse Measures​

  1. Rate Limiting: Max 50 imports/day
  2. Quality Checks: Invalid data = no credits
  3. Fraud Detection: ML-based pattern detection
  4. Cooling Periods: Prevent credit farming
  5. Account Limits: Max 10k credit balance

Consequences​

Positive​

  • Multiple reinforcing viral loops
  • Clear value exchange model
  • Sustainable unit economics
  • Natural quality control
  • Strong network effects

Negative​

  • Complex tracking requirements
  • Potential gaming of system
  • Requires careful balancing
  • Credit inflation risk

Success Metrics​

  1. K-Factor: Target 2.5+ within 6 months
  2. Viral Cycle Time: <7 days
  3. Credit Velocity: 80% monthly active earning
  4. Feature Adoption: 70% using viral features
  5. Retention: 60% 6-month retention

Alternatives Considered​

  1. Pure Referral: Rejected - single loop insufficient
  2. Pay-to-Play: Rejected - limits viral growth
  3. Unlimited Free: Rejected - unsustainable
  4. Points-Only: Rejected - no clear value exchange