Your AI-generated assessment includes:
| Metric | Healthy Loop | At-Risk Loop | Critical Risk | Interpretation |
| Loop Completion Rate | 60%+ | 30–59% | <30% | Measures how consistently users finish the loop cycle |
| Reward Fatigue Index | <20% | 20–40% | >40% | High fatigue means users lose motivation quickly |
| Repeat Participation | 3x+ | 1–2x | <1x | Indicates how often users re-trigger the loop |
| Viral Conversion Rate | 10–15% | 5–9% | <5% | Shows how effectively each user brings new users |
| Decay Rate Over 90 Days | <25% | 25–50% | >50% | Measures loop durability over time |
Note: Benchmarks are based on aggregated SaaS, content, and consumer app data from 2025. Actual values vary depending on the industry and the level of audience engagement.
Example 1 – Reward Referral Loop:
Users can invite friends to receive a 20% discount on their next purchase. The loop passes with a Green Flag since incentives are aligned, and each invite leads to visible new sign-ups.
Example 2 – Content Creation Loop:
Users publish three posts to earn a badge. The loop receives a Yellow Flag due to low repeatability and limited social visibility, suggesting the need for more potent sharing triggers.
Example 3 – Reward Fatigue Risk:
Users earn small credits for every referral they make. The loop gets a Red Flag since the incentive is too low for sustained engagement, leading to drop-off after early trials.
Business Impact: Teams that regularly assess and optimise loops reduce user churn, increase viral coefficient stability, and achieve up to 40% higher engagement efficiency across growth initiatives.

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Growth marketers, product managers, and founders designing viral or retention loops.
Simply describe your loop idea or behaviour sequence. The AI handles the rest.
They represent levels of loop health, from critical risk (Red) to highly sustainable (Green).
Yes, you can input and assess multiple loops to compare which has stronger compounding potential.
Yes, it adapts to both models, from referral loops to user-generated content ecosystems.
It checks structure, motivation, timing, and repeatability against tested viral and retention frameworks.
At least once every quarter or before launching major incentive or product changes.