growth-systems

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:

1. Take a cohort of users from a specific time period
2. Track shared URLs with user IDs (e.g., product.com/share?ref=[user_id])
3. Count new signups attributed to that cohort
4. 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

1. Increase shares/invites per user — Make sharing contextual and valuable
2. Increase conversion on shared links — Optimize landing experience
3. Reduce time to first share — Get users to value (and share) faster
4. 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

1. Foundation: Product data (usage, features, engagement)
2. Middle: AI interaction data (queries, outputs, feedback)
3. 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")