AI Growth Experiment Portfolio Allocator
Turn experiment backlog sprawl into one ranked monetization portfolio with owner accountability, MRR-impact scoring, and sprint-capacity guardrails.
Allocate limited experiment capacity to the highest-return lanes across activation, pricing, expansion, and retention. Generate one ranked backlog with owner accountability and export-ready outputs.
Portfolio summary
Pressure score: 71.0 / 100 (High)
Estimated quarterly new MRR baseline: $189,000
Estimated monthly churn-risk MRR: $3,087
Recommended cadence: Run one weekly portfolio board with owner-level escalation for blocked lanes.
| Lane | Owner | Expected MRR impact | Priority score | Sprint | Supporting workflow |
|---|---|---|---|---|---|
| Pipeline leakage recovery | RevOps Director | $17,250 | 3277.5 | Sprint 1 | AI Pipeline Leakage Audit Generator |
| Experiment governance | Growth Lead | $10,500 | 2730 | Sprint 3 | AI A/B Test Manager Generator |
| Activation friction reduction | Product Activation Lead | $13,500 | 1944 | Sprint 1 | AI Trial-to-Paid Conversion Rescue Generator |
| Expansion packaging | RevOps Director | $14,250 | 1909.5 | Sprint 2 | AI Expansion Revenue Playbook Generator |
| Pricing and package experiment | Growth Lead | $15,750 | 1785 | Sprint 1-2 | AI Pricing and Packaging Experiment Planner |
| Churn and win-back defense | Lifecycle Marketing Lead | $16,500 | 1760 | Sprint 2-3 | AI Win-Back Reactivation Playbook Generator |
# AI Growth Experiment Portfolio - AI Growth Portfolio Team ## Baseline - Quarter: 2026 Q2 - Monthly qualified trials: 2,400 - Trial-to-paid conversion: 12.5% - Current ARPA: $210 - Gross margin: 74.0% - Expansion rate: 8.2% - Monthly logo churn: 4.9% - Experiment capacity per sprint: 5 - Primary constraint: Low trial-to-paid conversion - Monetization pressure score: 71.0 / 100 (High) - Recommended cadence: Run one weekly portfolio board with owner-level escalation for blocked lanes. ## Revenue context - Estimated new MRR per month: $63,000 - Estimated new MRR per quarter: $189,000 - Estimated monthly churn-risk MRR: $3,087 ## Owner model - Growth owner: Growth Lead - Product owner: Product Activation Lead - RevOps owner: RevOps Director - Lifecycle owner: Lifecycle Marketing Lead ## Prioritized experiment portfolio (6 lanes) | # | Lane | Owner | Expected MRR impact | Effort | Confidence | Priority score | Sprint | Supporting route | |---|---|---|---|---|---|---|---|---| | 1 | Pipeline leakage recovery | RevOps Director | $17,250 | 4 | 76% | 3277.5 | Sprint 1 | AI Pipeline Leakage Audit Generator (/ai-pipeline-leakage-audit-generator) | | 2 | Experiment governance | Growth Lead | $10,500 | 3 | 78% | 2730 | Sprint 3 | AI A/B Test Manager Generator (/ai-ab-test-manager-generator) | | 3 | Activation friction reduction | Product Activation Lead | $13,500 | 5 | 72% | 1944 | Sprint 1 | AI Trial-to-Paid Conversion Rescue Generator (/ai-trial-to-paid-conversion-rescue-generator) | | 4 | Expansion packaging | RevOps Director | $14,250 | 5 | 67% | 1909.5 | Sprint 2 | AI Expansion Revenue Playbook Generator (/ai-expansion-revenue-playbook-generator) | | 5 | Pricing and package experiment | Growth Lead | $15,750 | 6 | 68% | 1785 | Sprint 1-2 | AI Pricing and Packaging Experiment Planner (/ai-pricing-packaging-experiment-planner) | | 6 | Churn and win-back defense | Lifecycle Marketing Lead | $16,500 | 6 | 64% | 1760 | Sprint 2-3 | AI Win-Back Reactivation Playbook Generator (/ai-winback-reactivation-playbook-generator) | ## Next sprint capacity cut (5 lanes) 1. Pipeline leakage recovery - Owner: RevOps Director; Impact: $17,250; Priority: 3277.5 2. Experiment governance - Owner: Growth Lead; Impact: $10,500; Priority: 2730 3. Activation friction reduction - Owner: Product Activation Lead; Impact: $13,500; Priority: 1944 4. Expansion packaging - Owner: RevOps Director; Impact: $14,250; Priority: 1909.5 5. Pricing and package experiment - Owner: Growth Lead; Impact: $15,750; Priority: 1785 ## Weekly operating ritual 1. Re-rank all active experiments by priority score and unresolved blocker count. 2. Freeze capacity for low-score experiments until higher-score lanes are unblocked. 3. Publish one decision memo with stop/continue/scale decisions and expected MRR impact. 4. Update backlog confidence based on real outcomes, not hypothesis confidence alone.
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