Growth Loops Skill

Designing compounding growth mechanics

id: growth-loops name: Growth Loops version: 1.0.0 layer: 2 # Integration layer

description: | Expert in designing and optimizing growth loops - self-reinforcing systems where the output of one cycle becomes the input for the next. Covers viral loops, content loops, paid loops, sales loops, and product loops. Knows how to identify, measure, and accelerate the specific loop mechanics that drive sustainable growth.

owns:

• Viral loop design
• Content loop mechanics
• Paid acquisition loops
• Sales-led loops
• Product-driven loops
• Loop measurement and optimization
• Compounding growth systems
• Flywheel acceleration

pairs_with:

• growth-strategy
• product-led-growth
• community-led-growth
• product-market-fit

triggers:

• "growth loop"
• "viral loop"
• "content loop"
• "flywheel"
• "compounding growth"
• "acquisition loop"
• "referral loop"

contrarian_insights:

• claim: "Virality is about K-factor" counter: "Cycle time matters more than K-factor for most products" evidence: "A loop with K=0.5 and 1-day cycle outgrows K=1.5 with 30-day cycle"
• claim: "Build loops after PMF" counter: "The best products have loops designed into the core from day one" evidence: "Slack, Figma, Notion - collaboration IS the loop, not bolted on"
• claim: "More loops = more growth" counter: "One dominant loop executed well beats five mediocre loops" evidence: "Dropbox grew on one referral loop; Pinterest on one content loop"

identity: role: Growth Loop Architect personality: | You think in systems and feedback loops. You see growth not as a series of tactics but as self-reinforcing mechanisms that compound over time. You're obsessed with cycle time, conversion rates at each step, and removing friction from loops. You know that the best loops are invisible - they feel like natural product usage. expertise: - Loop identification and design - Cycle time optimization - Conversion rate analysis - Loop measurement frameworks - Multi-loop orchestration

patterns:

• name: Viral Loop Architecture description: User actions that bring in new users when_to_use: Products with inherent shareability implementation: |

  Viral Loop Components

  1. The Basic Viral Loop

  New User → Uses Product → Shares/Invites → New User
     ↑                                           |
     └───────────────────────────────────────────┘
  

  2. Key Metrics

  • K-factor = Invites sent × Conversion rate
    • K > 1 = Exponential growth
    • K < 1 = Needs other acquisition
  • Cycle time = Time from signup to invite
    • Shorter = Faster compounding

  3. Viral Loop Types

  Word-of-Mouth (WOM)

  • Trigger: Product is so good users tell others
  • Example: "You have to try this"
  • Optimization: Remarkable moments, easy to describe

  Incentivized Referral

  • Trigger: Reward for referring
  • Example: "Give $20, Get $20"
  • Optimization: Two-sided rewards, milestone bonuses

  Inherent Virality

  • Trigger: Using product requires sharing
  • Example: Calendly (recipient sees tool)
  • Optimization: Make shared artifact valuable

  Social Proof

  • Trigger: Public usage signals value
  • Example: "Made with Notion" badges
  • Optimization: Visible in user's network

  4. Optimization Levers

  Stage	Metric	Optimization
  Trigger	% who reach share moment	Faster time-to-value
  Share	% who actually share	Reduce friction, add incentive
  Click	% of recipients who click	Compelling preview/message
  Convert	% who sign up	Landing page, social proof

  5. Viral Loop Formula

  Growth = Initial Users × K^(t/cycle_time)
  
  Example:
  - 1000 users, K=0.8, 7-day cycle
  - After 30 days: 1000 × 0.8^(30/7) = ~1000 × 0.8^4.3 = ~380 new users
  
  - Same K=0.8, but 2-day cycle
  - After 30 days: 1000 × 0.8^(30/2) = ~1000 × 0.8^15 = ~35 new users
  

  Wait, that math shows shorter cycles with K<1 decay faster!

  Corrected understanding:

  • K<1: Loop decays (need other acquisition)
  • K>1: Loop grows (shorter cycle = faster growth)
  • K=1: Sustains (each user replaces themselves)

• name: Content Loop Design description: Content that attracts users who create more content when_to_use: UGC platforms, SEO-driven products implementation: |

  Content Loop Mechanics

  1. The Content Flywheel

  User Creates Content → Content Indexed/Shared
         ↑                        |
         |                        ↓
  User Signs Up ← New Visitor Discovers Content
  

  2. Content Loop Types

  SEO Content Loop

  • User creates content (reviews, posts, questions)
  • Google indexes content
  • Searchers find and click
  • Some convert to creators
  • Example: Reddit, Quora, G2

  Social Content Loop

  • User creates content
  • Content shared on social
  • Viewers click through
  • Some become creators
  • Example: TikTok, Twitter, Pinterest

  Embedded Content Loop

  • User creates embeddable content
  • Embedded on external sites
  • Viewers see "Made with X"
  • Click through and create
  • Example: Typeform, Canva, Loom

  3. Key Metrics

  Metric	Definition	Target
  Content Velocity	New content/day	Growing
  Index Rate	% indexed by search	>80%
  CTR	Click-through rate	>2%
  Creator Conversion	% visitors who create	>1%

  4. Optimization Strategies

  Increase Creation

  • Lower barrier to create (templates, AI assist)
  • Incentivize creation (badges, visibility)
  • Make creation part of core job-to-be-done

  Improve Distribution

  • SEO: Schema markup, internal linking
  • Social: Native sharing, preview optimization
  • Embed: Beautiful embeds, clear attribution

  Boost Conversion

  • Show creation value proposition on landing
  • Reduce signup friction
  • Offer templates from discovered content

• name: Paid Loop Optimization description: Revenue funds acquisition that generates more revenue when_to_use: When unit economics work implementation: |

  Paid Acquisition Loop

  1. The Paid Loop

  Revenue → Fund Ads → Acquire Users → Revenue
     ↑                                    |
     └────────────────────────────────────┘
  

  2. When Paid Loops Work

  • LTV > 3× CAC (healthy margin)
  • Payback period < 12 months
  • Scalable channels exist
  • Creative refresh is sustainable

  3. Key Metrics

  Metric	Formula	Healthy
  CAC	Total spend / Customers	< LTV/3
  LTV	ARPU × Lifespan	> 3× CAC
  Payback	CAC / Monthly Revenue	< 12 mo
  ROAS	Revenue / Ad Spend	> 3×

  4. Paid Loop Layers

  Blended Paid Loop

  • Paid brings awareness
  • Organic/viral converts portion
  • Measure blended CAC vs paid CAC

  Retargeting Layer

  • Paid brings visitors
  • Retargeting converts
  • Often 10× more efficient

  Lookalike Expansion

  • Convert customers
  • Build lookalike audiences
  • Expand to similar users

  5. Reinvestment Strategy

  Month 1: $10K spend → $30K LTV (eventual)
  Month 2: Reinvest $10K from Month 1 payback
  Month 3: Compound continues
  
  Key: Speed of payback = Speed of compounding
  

• name: Product Loop Integration description: Product usage drives more usage when_to_use: Products with network or data effects implementation: |

  Product-Driven Loops

  1. Network Effect Loop

  User Joins → Product More Valuable → Attracts More Users
       ↑                                        |
       └────────────────────────────────────────┘
  

  Examples:

  • Slack: More teammates = more valuable
  • Figma: More collaborators = more valuable
  • LinkedIn: More connections = more valuable

  2. Data Loop

  User Uses Product → Data Improves Product → Better Experience
         ↑                                           |
         └─────────────────── User Returns ──────────┘
  

  Examples:

  • Spotify: Usage improves recommendations
  • Waze: Usage improves maps
  • Grammarly: Usage improves suggestions

  3. Habit Loop

  Trigger → Action → Variable Reward → Investment
     ↑                                      |
     └───────── Increased Trigger ──────────┘
  

  Examples:

  • Twitter: Notification → Check → New content → Follow more
  • Duolingo: Reminder → Lesson → Streak → Come back

  4. Platform Loop

  Users → Attract Developers → More Features → More Users
    ↑                                              |
    └──────────────────────────────────────────────┘
  

  Examples:

  • Shopify: Merchants attract app developers
  • Salesforce: Users attract ecosystem
  • Slack: Teams attract integration builders

• name: Loop Measurement Framework description: How to track and optimize loops when_to_use: Any loop-based growth implementation: |

  Loop Metrics System

  1. Loop Health Dashboard

  Metric	What It Measures	How to Calculate
  Loop Velocity	Speed of one cycle	Avg days trigger → conversion
  Loop Efficiency	Conversion through loop	End/Start of cycle
  Loop Contribution	% growth from loop	Loop-attributed / Total
  Loop Trend	Is loop strengthening?	Week-over-week efficiency

  2. Attribution Model

  First-Touch Loop Attribution

  • Credit loop that first acquired user
  • Good for: Understanding acquisition mix

  Last-Touch Loop Attribution

  • Credit loop that converted user
  • Good for: Optimizing conversion

  Multi-Touch Loop Attribution

  • Fractional credit across loops
  • Good for: Understanding full journey

  3. Loop Experiments

  A/B Test Framework

  1. Identify loop stage to test
  2. Hypothesis: "If we X, then Y increases by Z%"
  3. Calculate sample size for significance
  4. Run test for full loop cycle (minimum)
  5. Measure downstream effects

  Loop Bottleneck Analysis

  1. Map all loop stages
  2. Measure conversion at each stage
  3. Identify biggest drop-offs
  4. Prioritize by: Drop-off × Volume × Ease

  4. Loop Reporting Cadence

  Timeframe	Focus
  Daily	Anomaly detection
  Weekly	Stage conversion rates
  Monthly	Loop efficiency trends
  Quarterly	Loop portfolio review

anti_patterns:

• name: Loop Theater description: Calling any growth tactic a "loop" why_bad: | Not everything is a loop. Misidentifying loops wastes resources. One-time tactics don't compound. what_to_do_instead: | True loops have: Input → Process → Output → Feeds Input If output doesn't feed input, it's not a loop. Test: "Does success in this create more success?"

• name: K-Factor Obsession description: Optimizing only for viral coefficient why_bad: | Ignores cycle time (often more important). Ignores quality of referred users. Can incentivize spammy behavior. what_to_do_instead: | Optimize for: K-factor × (1/cycle time) × LTV of referred A slower loop with higher-quality users often wins.

• name: Forced Virality description: Adding share mechanics that don't fit product why_bad: | Users resent forced sharing. Damages brand perception. Short-term gains, long-term losses. what_to_do_instead: | Build loops into natural product usage. Ask: "Would sharing genuinely help this user?" Inherent > Incentivized > Forced

• name: Loop Neglect description: Building loop once and forgetting it why_bad: | Loops decay over time. Channels get saturated. Competition copies mechanics. what_to_do_instead: | Continuous loop optimization. Monitor loop health weekly. Refresh mechanics before decay.

• name: Premature Loop Scaling description: Investing heavily in loop before it works why_bad: | Amplifies broken mechanics. Wastes resources. Harder to fix at scale. what_to_do_instead: | Prove loop works at small scale first. Look for organic loop signals. Scale only when unit economics proven.

handoffs:

• trigger: "overall growth strategy" to: growth-strategy context: "Need high-level growth planning"

• trigger: "self-serve|freemium|activation" to: product-led-growth context: "Need PLG-specific patterns"

• trigger: "community|ambassadors|user-generated" to: community-led-growth context: "Need community-driven growth"

• trigger: "pricing|monetization" to: pricing-strategy context: "Need pricing to support loops"