| name | growth-systems |
| description | Growth loops, retention frameworks, viral mechanics, and acquisition models. Use when designing growth engines, diagnosing retention problems, or evaluating acquisition strategies. |
Growth Systems Skill
Purpose
Growth is a system of interconnected loops, not a set of tactics. This skill provides frameworks for understanding, measuring, and improving the loops that drive product growth.
Based on Reforge growth frameworks and Web 2.0/mobile-era viral loop mechanics.
Growth Loop Fundamentals
What Is a Growth Loop?
A closed system where the output of one cycle becomes the input of the next.
Input → User Action → Output → [Output becomes next cycle's Input]
Unlike funnels (which have a start and end), loops are self-reinforcing. The best products have one dominant loop that drives the majority of growth.
Types of Growth Loops
Acquisition Loops
| Loop | Mechanism | Example |
|---|
| Viral | Users invite/share → new users join | Dropbox referral, WhatsApp sharing |
| Content | Users create → content discovered → new users create | YouTube, Instagram, TikTok |
| Paid | Revenue → ad spend → new users → revenue | Subscription apps, SaaS |
| SEO/AEO | Content indexed → discovered via search/AI → conversion | Wikipedia, Stack Overflow |
| Sales | Success → referrals/case studies → new customers | Enterprise SaaS |
| Community | Users help users → community grows → attracts new users | Reddit, Discord |
Engagement Loops
| Loop | Mechanism | Example |
|---|
| Habit | Trigger → action → reward → investment | Duolingo streaks, social feeds |
| Social | User acts → others notified → they act → notification | Slack, Instagram likes |
| Progress | Advance → unlock → new reasons to return | Gaming, learning platforms |
| Recommendation | Consume → algorithm learns → better suggestions | Netflix, Spotify |
Monetization Loops
| Loop | Mechanism | Example |
|---|
| Expansion | Success → needs grow → upgrade → more success | Slack seats, AWS usage |
| Network pricing | More users → more value → higher WTP → investment | Marketplace pricing |
Viral Mechanics
Viral Factor Calculation
Viral Factor = New users acquired via existing users / Original cohort size
Practical measurement:
- Take a cohort of users from a specific time period
- Track shared URLs with user IDs (e.g.,
product.com/share?ref=[user_id])
- Count new signups attributed to that cohort
- Ratio = viral factor
| Viral Factor | Interpretation |
|---|
| >1.0 | Self-sustaining viral growth (very rare) |
| 0.5-1.0 | Meaningful viral amplification |
| 0.1-0.5 | Some organic sharing |
| <0.1 | Not meaningfully viral |
Optimization Levers
- Increase shares/invites per user — Make sharing contextual and valuable
- Increase conversion on shared links — Optimize landing experience
- Reduce time to first share — Get users to value (and share) faster
- Improve targeting — Help users share with people likely to convert
Viral Loop Anti-Patterns
- Address book imports → Short-term growth, long-term spam reputation
- Forced sharing → Users resent mandatory social actions
- Incentive-only referrals → Attracts low-quality users motivated by reward, not product
- Growth without retention → Viral spikes followed by mass churn
Retention Framework
The 10 Stickiness Signals
| # | Signal | Healthy Threshold |
|---|
| 1 | Cohort retention curves flatten | Yes — plateau exists |
| 2 | Actives / Registered users | >25% |
| 3 | Power user curve (smile shape) | Concentration at high end |
| 4 | Viral factor | >0.5 |
| 5 | DAU/MAU ratio | >50% consumer, >30% B2B |
| 6 | D1/D7/D30 retention | >60/30/15 |
| 7 | Revenue per user expansion | Positive trend |
| 8 | Organic acquisition at scale | >60% |
| 9 | Subscription annual retention | >65% |
| 10 | Older markets more engaged | Yes |
6+ green: Strong retention → invest in growth loops
3-5 green: Adequate → fix gaps before scaling
<3 green: Weak → growth investment is premature
Retention Curve Shapes
Healthy: Weak: Critical:
100%─┐ 100%─┐ 100%─┐
│\ │\ │\
│ \___ │ \ │ \
│ | │ \ │ \
└───── │ \__ │ \
└────── └────→0
New User Retention (First 7 Days)
The most leveraged retention period:
- D1 retention predicts long-term retention more than any other metric
- Time to Aha should be <5 min (consumer) or <1 day (B2B)
- Activation rate is the conversion from "signed up" to "experienced value"
- The magic number = the behavior that predicts retention (find it, optimize toward it)
Resurrection Framework
| Strategy | When to Use | Mechanism |
|---|
| Product improvement email | After shipping something relevant to churned users | "Here's what changed since you left" |
| Social trigger | When a churned user's connections are active | "Your colleague just..." |
| Pain recurrence | When the original problem resurfaces | "Still dealing with [problem]?" |
| Win-back offer | When price was a churn factor | Time-limited discount |
| Seasonal/contextual | When product usage is cyclical | "It's [season], time to [action]" |
AI-Specific Growth Considerations
AI Acquisition Data Pyramid
- Foundation: Product data (usage, features, engagement)
- Middle: AI interaction data (queries, outputs, feedback)
- Top: Value-creation data (outcomes, results, ROI)
AI Retention Challenges
- Novelty effect: Initial excitement fades → usage drops
- Trust erosion: One bad output can destroy confidence
- Habit disruption: AI changes workflows, creating friction
- Value plateau: After initial wow, incremental value unclear
AI Retention Strategies
- Variable reward (different helpful output each time)
- Progressive capability revelation
- Personalization that improves with use
- Clear value quantification ("saved you X hours this week")
