Viral Loops for Usage-Based AI/ML (Public)
Viral Loops playbook for usage-based AI/ML companies at Public. Tailored to the usage-based business model with implementation steps and expert guidance.
Timeline: 1-2 weeks
Prerequisites
- ✓ Product-market fit
- ✓ Analytics tracking key events
- ✓ Budget for 1-2 weeks
Step-by-Step Guide
Discovery & Audit phase for viral loops in ai-ml. Focus on understanding the landscape and planning.
Strategy Design phase for viral loops in ai-ml. Focus on understanding the landscape and planning.
Initial Implementation phase for viral loops in ai-ml. Focus on execution and iteration.
Measurement Setup phase for viral loops in ai-ml. Focus on execution and iteration.
Optimization Cycle phase for viral loops in ai-ml. Focus on execution and iteration.
Scale & Systematize phase for viral loops in ai-ml. Focus on execution and iteration.
Expected Outcomes
- ✓ Validated viral loops for usage-based AI/ML
- ✓ KPI baselines established
- ✓ Growth process documented
KPIs to Track
- ● Viral Coefficient (K-factor)
- ● Cycle Time
- ● Invite Rate
- ● Acceptance Rate
- ● Network Effect Multiplier
- ● Organic Share Rate
Common Mistakes to Avoid
Ehsan's Growth Commentary
The data from 260 companies shows Viral Loops generates 39% of pipeline for AI/ML companies at Public. But only when implemented with discipline. Scale what works, kill what does not. No emotional attachment to channels.
AI/ML companies at Public should allocate 15-25% of growth budget to Viral Loops. Track weekly, evaluate monthly, pivot quarterly. The winning rhythm is 2-week sprints with clear hypotheses.
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council