Referral Programs for Usage-Based AI/ML (Pre-Seed)
Referral Programs playbook for usage-based AI/ML companies at Pre-Seed. Tailored to the usage-based business model with implementation steps and expert guidance.
Timeline: 3-6 months
Prerequisites
- ✓ Working MVP
- ✓ Analytics tracking key events
- ✓ Budget for 3-6 months
Step-by-Step Guide
Discovery & Audit phase for referral programs in ai-ml. Focus on understanding the landscape and planning.
Strategy Design phase for referral programs in ai-ml. Focus on understanding the landscape and planning.
Initial Implementation phase for referral programs in ai-ml. Focus on execution and iteration.
Measurement Setup phase for referral programs in ai-ml. Focus on execution and iteration.
Optimization Cycle phase for referral programs in ai-ml. Focus on execution and iteration.
Scale & Systematize phase for referral programs in ai-ml. Focus on execution and iteration.
Expected Outcomes
- ✓ Validated referral programs for usage-based AI/ML
- ✓ KPI baselines established
- ✓ Growth process documented
KPIs to Track
- ● Viral Coefficient
- ● Referral Revenue %
- ● Time to First Referral
- ● Referral Rate
- ● Referred User LTV
- ● Program Participation Rate
Common Mistakes to Avoid
Ehsan's Growth Commentary
After working with 93+ AI/ML companies, the pattern is clear: Referral Programs at the Pre-Seed stage requires founder-driven execution. The teams that win start smaller than they think they should and iterate 3x faster than their competitors.
With 1-3 people and $0-50K budget, focus Referral Programs efforts on the single highest-ROI activity. Do not spread thin across multiple sub-channels. Validate one approach before adding another.
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