Growth Loop Optimiser

growth-loop-optimiser

Agent: Growth Lead

L1 growth leader (1x) responsible for distribution strategy, activation signal definition, retention modelling, growth model design, and growth loop optimisation.

Department ethos: ideal-data-growth.md

Skill Description

The growth loop optimiser analyses active growth loops — viral, content, paid, and product-led — to identify friction points, measure loop throughput (users in vs. users generated), and design experiments that increase the loop's compounding rate.

When to Use

• When a growth loop is active but throughput (viral coefficient, content amplification factor, or referral rate) is declining.
• When the growth model shows that optimizing an existing loop yields higher ROI than opening a new channel.
• When a referral or sharing mechanic is live but underperforming the benchmark for the product category.
• When the team needs to decide which loop to prioritize for the next experiment cycle.

Workflow

1. Map active loops: Document each active growth loop as a cycle: trigger action, user output, distribution mechanism, new user input, and activation step. Quantify the throughput at each node.
2. Measure loop metrics: For viral loops, calculate the viral coefficient (k = invites sent * conversion rate) and cycle time. For content loops, measure content creation rate, SEO impressions, CTR, and signup conversion. For paid loops, measure ROAS and reinvestment rate.
3. Identify bottleneck node: Find the node in each loop with the largest drop-off. Rank loops by the potential throughput gain from fixing the bottleneck.
4. Design experiments: For the top bottleneck, design 2-3 experiments with clear hypotheses, success metrics, and minimum detectable effect. Prioritize by expected impact and implementation effort.
5. Model compounding impact: Project the long-term user growth impact of each experiment assuming success. A 10% improvement in viral coefficient compounds over each cycle — model the 6-month trajectory.
6. Execute and measure: Run experiments with proper A/B testing and statistical significance tracking. After conclusion, update the loop metrics and re-rank bottlenecks.

Anti-Patterns

• Optimizing a broken loop: Spending experiment cycles on a loop whose fundamental mechanic does not work (k < 0.1 for viral) before fixing structural issues. Why: optimizing a 0.05 viral coefficient to 0.07 is meaningless; the loop needs a mechanic redesign, not conversion optimization.
• Ignoring cycle time: Focusing only on conversion rates without reducing the time between loop iterations underestimates the compounding effect of speed. Why: a loop with k=0.5 and a 2-day cycle time outperforms k=0.7 with a 30-day cycle time over 6 months.
• Single-loop dependency: Optimizing only one loop creates fragility. Why: if the primary loop degrades (platform change, competitive response), growth stalls without a fallback.
• No compounding model: Evaluating loop improvements as linear gains instead of compound growth understates the value of small improvements. Why: a 5% improvement per loop cycle compounds to 50%+ over 10 cycles.

Output

Success:

• A loop optimization report containing loop maps with node-level metrics, bottleneck ranking, experiment designs with projected compound impact, and a prioritized experiment roadmap.

Failure:

• No active loop has a throughput above the minimum viable threshold. Report the loop analysis, recommend structural changes to loop mechanics, and flag whether a new loop type should be explored.

Related Skills

• growth-model-designer -- the growth model provides the framework within which loops operate.
• growth-loop-activator -- implements the loop mechanics that this skill optimizes.
• activation-moment-validator -- the activation step within each loop must be validated as a retention predictor.
• funnel-analyser-growth -- funnel analysis within each loop node reveals specific conversion bottlenecks.