Tools Required
This skill runs using CORE memory only. No integrations required.Step 1: Identify the Core User Behavior
Pinpoint the behavior that drives growth:- Aha moment: What action signals the user gets value?
- Frequency: How often does this behavior occur?
- Virality potential: Does this behavior naturally involve other users?
- Data signals: What metrics prove this is the driver?
Step 2: Map the Growth Loop Mechanics
Document each stage:- Trigger: What initiates the loop? (external, habitual, product-driven)
- Action: What does the user do?
- Reward: What value do they receive?
- Viral moment: Where/how do they invite others?
- Re-entry: How do they come back?
Step 3: Quantify Loop Economics
Calculate the multiplier effect:- Viral coefficient: If 1 user invites X others, what’s the average?
- Conversion rate per stage: % who trigger → action → reward → invite
- Loop time: Days/hours between entry and re-entry
- Lifetime value contribution: What % of LTV comes from growth loop vs. paid?
Step 4: Identify Friction Points
For each stage, ask:- Is the trigger compelling? Easy to understand why they should act?
- Is the action frictionless? Can they do it in <30 seconds?
- Is the reward immediate? Do they feel value right away?
- Is the invite natural? Does inviting others feel organic, not transactional?
Step 5: Generate Loop Optimization Ideas
Brainstorm improvements:- Trigger: Make it more salient, frequent, or obvious?
- Action: Remove steps, simplify UI, add guidance?
- Reward: Amplify the reward, add social proof, show progress?
- Viral moment: Make sharing easier, add incentives, reduce friction?
- Re-entry: Add reminders, new content, social follow-ups?
Step 6: Design the Variant Loop
For each high-potential optimization:- What changes?
- What’s the predicted impact on viral coefficient?
- How long to test and measure?
- Success metric (e.g., “increase invite rate from 5% to 8%”)?
Output Format
Growth Loop Design — [Product Name] Core Growth Loop
- Trigger: [External / Habitual / Product-driven] — [Specific trigger]
- User action: [What they do]
- Reward: [What they gain]
- Viral moment: [How others are invited]
- Re-entry: [How they return]
- Viral coefficient: [Average X new users per existing user]
- Conversion funnel:
- Trigger → Action: [X%]
- Action → Reward: [X%]
- Reward → Invite: [X%]
- Invite acceptance: [X%]
- Loop time: [X days average]
- Current growth contribution: [X% of new user acquisition]
| Stage | Drop-off rate | Bottleneck | Severity |
|---|---|---|---|
| Trigger | [%] | [What stops them] | High / Med / Low |
| Action | [%] | [What stops them] | High / Med / Low |
| Reward | [%] | [What stops them] | High / Med / Low |
| Viral moment | [%] | [What stops them] | High / Med / Low |
| Stage | Current state | Proposed change | Expected impact | Effort |
|---|---|---|---|---|
| [Stage] | [Current metric] | [Change to make] | [Expected % improvement] | Easy / Med / Hard |
| [Stage] | [Current metric] | [Change to make] | [Expected % improvement] | Easy / Med / Hard |
- Hypothesis: If [we change X], then [metric] will improve from [A%] to [B%]
- Duration: [X weeks]
- Success threshold: [Metric target]
Edge Cases
- Weak viral coefficient: Loop doesn’t naturally create network effects. Consider adding explicit incentives (referral rewards) or pivoting to a different core behavior.
- Long loop time: Days between entry and re-entry. Users may forget or churn. Add reminder notifications or increase reward frequency.
- No clear aha moment: User value is distributed across multiple actions. Map secondary loops or focus messaging on the single strongest value driver.
- Network effects missing: Product doesn’t require or benefit from others using it. Build loop around social proof (leaderboards, badges) or asynchronous sharing instead.
- Incentive decay: Reward loses appeal over time. Refresh reward type, introduce scarcity, or tie to leveling/progression systems.