growth-loops

Every growth loop has four components:

```

[1. NEW USER] --> [2. ACTION] --> [3. OUTPUT] --> [4. REINVESTED AS INPUT]

^ |

|____________________________________________________|

```

1. New User: The person entering the loop. They may come from any acquisition source.

2. Action: The core behavior the user performs that creates value for themselves AND generates a growth output. This is critical -- the action must be something users do for their own benefit, not something you ask them to do for your benefit.

3. Output: The artifact, content, signal, or invitation that the action produces. This output must be visible or accessible to potential new users.

4. Reinvestment: The mechanism by which the output reaches new potential users and motivates them to enter the loop. This is where the loop "closes."

Loop Health Metrics

For any loop, measure:

• Conversion rate at each step: What percentage of users at step N advance to step N+1?
• Cycle time: How long does one full loop iteration take? (Hours? Days? Weeks?)
• Loop coefficient: How many new users does each existing user generate per cycle?
• Output quality: Are the outputs (content, invitations, etc.) high-quality enough to convert?
• Saturation point: At what scale does the loop begin to slow down?

---

3.1 Viral Loops

The output is a direct invitation or share that brings in new users.

Subtypes:

a) Inherent Virality (Collaboration)

• The product requires multiple users to deliver value
• Loop: User signs up -> Invites teammates to collaborate -> Teammates sign up -> They invite their teammates
• Examples: Slack (team messaging), Figma (multiplayer design), Google Docs (shared editing)
• Strength: Highest-quality loop because invitation is essential to product value
• Key metric: Invites sent per user within first 7 days

b) Artificial Virality (Incentivized)

• Users are incentivized to invite others through rewards
• Loop: User signs up -> Refers friends for reward -> Friends sign up -> They refer for rewards
• Examples: Dropbox (free storage), PayPal (cash bonus), Robinhood (free stock)
• Strength: Can be engineered regardless of product type
• Risk: Incentive-driven users may have lower retention
• Key metric: Referral conversion rate, referred user retention vs organic

c) Word-of-Mouth Virality (Organic)

• Users tell others because the product is remarkable
• Loop: User signs up -> Has great experience -> Tells friends/colleagues -> Friends sign up
• Strength: Highest trust, lowest cost
• Weakness: Hard to measure, hard to accelerate directly
• Key metric: NPS, organic mention volume, branded search growth

d) Exposure Virality (Output Sharing)

• Users create content/output that is visible to non-users and includes product branding
• Loop: User creates content -> Content is shared/published -> Viewers see product branding -> Some viewers sign up
• Examples: Loom (video sharing), Calendly (scheduling links), Typeform (forms)
• Strength: Scales with user activity
• Key metric: Views per shared output, signup rate from shared content

For deep viral loop design, see viral-loops skill.

3.2 Content / UGC Loops

Users create content that attracts new users through search or social channels.

Loop: User signs up -> Creates content (questions, answers, projects, templates) -> Content is indexed by search engines / shared on social -> New users discover content via search/social -> They sign up to engage

Examples:

• Pinterest: Users pin images -> Pins are indexed by Google -> Users find pins via image search -> They sign up to save/pin
• Stack Overflow: Users ask/answer questions -> Answers rank in Google -> Developers find answers -> They sign up to ask/answer
• Notion: Users create templates -> Templates are shared/indexed -> New users discover templates -> They sign up to use them
• Canva: Users create designs -> Designs include Canva branding -> Viewers see Canva -> They sign up to create

Key Design Principles:

1. User-generated content must be publicly accessible (not behind login walls)
2. Content must be high-quality enough to rank or get shared
3. The path from content consumption to signup must be clear and low-friction
4. Content creation must be a natural part of the product experience, not a separate ask

Key Metrics: Content created per user, percentage of content indexed, organic traffic from UGC, signup conversion from content pages

3.3 Paid Loops

Revenue from users funds acquisition of more users.

Loop: User signs up -> User pays for product -> Revenue is reinvested in paid acquisition -> Ads acquire new users -> They sign up and pay

Key Requirement: The loop only works if LTV > CAC with enough margin to fund reinvestment.

Loop Coefficient Calculation:

```

Revenue per user (first-year) = $X

Reinvestment rate = Y% of revenue allocated to paid acquisition

CAC via paid channels = $Z

New users per dollar reinvested = 1 / Z

Paid loop coefficient = (X * Y) / Z

```

If the coefficient is > 1.0, the paid loop is self-funding and can scale. If < 1.0, it requires external fuel (venture capital, profits from other sources).

Examples: Most SaaS companies with strong unit economics use paid loops as one engine.

Key Metrics: CAC payback period, LTV/CAC ratio, blended CAC trend, reinvestment rate

3.4 Sales Loops

Revenue funds sales capacity to close more deals.

Loop: Sales team closes deal -> Revenue funds additional sales headcount -> New reps close more deals -> Revenue grows -> More reps hired

Key Metrics: Sales rep ramp time, quota attainment, revenue per rep, sales efficiency ratio (net new ARR / sales & marketing spend)

Distinction from Paid Loops: Sales loops have higher friction (human-dependent) but can handle higher ACV and more complex sales cycles.

---

Diagnostic Questions

Ask the user these questions to uncover loops already operating in their business:

1. Where do your best users come from? Trace back the path. Did they come from another user? From content? From a shared link?
2. What do your users create or produce in the product? Is any of that output visible to non-users?
3. Do your users invite others? Why? Is it inherent to the product or incentivized?
4. What percentage of new signups mention they heard about you from an existing user?
5. Is any user-generated content indexed by search engines? How much organic traffic does it drive?
6. What is your revenue reinvestment rate into paid acquisition? What is the payback period?
7. Do you have a sales team? What is their efficiency ratio?

Loop Discovery Exercise

For each potential loop, fill in:

```

Loop Name: ___

Type: [Viral / Content / Paid / Sales]

Step 1 - New User: How do they enter? ___

Step 2 - Action: What do they do? ___

Step 3 - Output: What does their action produce? ___

Step 4 - Reinvestment: How does the output reach new potential users? ___

Current Conversion Rates:

Step 1 -> Step 2: ___%

Step 2 -> Step 3: ___%

Step 3 -> Step 4: ___%

Step 4 -> Step 1: ___%

Overall Loop Coefficient: ___

Cycle Time: ___

```

---

Step-by-Step Process

Step 1: Start with the user action, not the growth output.

What does the user naturally want to do in your product? The best loops are built on actions users are already motivated to take. Do not design actions users must be persuaded to take.

Step 2: Identify the natural output of that action.

When the user takes that action, what artifacts, content, or signals are produced? List every possible output:

• Documents, designs, or content created
• Links shared
• People invited
• Data generated
• Status updates or activity signals

Step 3: Determine which outputs can reach non-users.

For each output, ask: Can this output be made visible to people who are not yet users? How?

• Public sharing (links, embeds)
• Search indexing
• Social media posting
• Email/messaging to non-users
• Marketplace or gallery listing

Step 4: Design the conversion path from output to new user.

How will a non-user who encounters the output be motivated to sign up?

• Call-to-action on shared content ("Made with [Product]")
• Landing page for shared links with signup prompt
• Invitation flow for collaboration
• Template gallery with "use this template" signup

Step 5: Minimize friction at every step.

For each transition in the loop, identify and remove friction:

• Can the user complete the action faster?
• Can the output be shared more easily?
• Can the non-user sign up with fewer steps?
• Can the new user reach the same action faster?

Step 6: Instrument and measure.

Set up tracking for every step of the loop. Calculate your loop coefficient and cycle time. Set improvement targets.

---

Loop Coefficient Formula

```

Loop Coefficient (K) = (Users at Step 1) (Rate 1->2) (Rate 2->3) (Rate 3->4) (Rate 4->1) / (Users at Step 1)

Simplified: K = r1 r2 r3 * r4

Where r1-r4 are conversion rates at each step

```

Compounding Model

For a loop with coefficient K and cycle time T:

```

After 1 cycle: N * K new users

After 2 cycles: N K + N K^2 new users

After n cycles: N K (1 - K^n) / (1 - K) cumulative new users from the loop

Steady-state multiplier (if K < 1): 1 / (1 - K)

```

Example: 1,000 seed users, K = 0.3, cycle time = 2 weeks

| Cycle | New Users from Loop | Cumulative from Loop | Total Users |

|-------|-------------------|----------------------|-------------|

| 0 | 0 | 0 | 1,000 |

| 1 | 300 | 300 | 1,300 |

| 2 | 90 | 390 | 1,390 |

| 3 | 27 | 417 | 1,417 |

| 4 | 8 | 425 | 1,425 |

| Steady state | - | ~428 | ~1,428 |

Multiplier: 1 / (1 - 0.3) = 1.43x. Every user acquired through any channel is worth 1.43 users.

Hypothetical Maximum Analysis

For each step in the loop, calculate: "What if this step had a 100% conversion rate?"

This reveals which step has the most leverage:

```

Current: K = 0.05 0.30 0.80 * 0.10 = 0.0012

What if Step 1 (action rate) = 100%? K = 1.00 0.30 0.80 * 0.10 = 0.024 (20x improvement)

What if Step 2 (output rate) = 100%? K = 0.05 1.00 0.80 * 0.10 = 0.004 (3.3x improvement)

What if Step 3 (reach rate) = 100%? K = 0.05 0.30 1.00 * 0.10 = 0.0015 (1.25x improvement)

What if Step 4 (conversion rate) = 100%? K = 0.05 0.30 0.80 * 1.00 = 0.012 (10x improvement)

```

In this example, improving Step 1 (action rate) has the most leverage. Focus optimization there.

Cycle Time Impact

Shorter cycle times mean faster compounding. A loop with K=0.3 and a 1-week cycle time produces more growth over 6 months than the same K with a 1-month cycle time.

```

K=0.3, 1-week cycle, 26 cycles in 6 months: multiplier approaches 1.43x faster

K=0.3, 1-month cycle, 6 cycles in 6 months: multiplier is still building toward 1.43x

```

Reducing cycle time is often the most underrated optimization lever.

---

Every growth loop follows an S-curve: slow start, rapid growth, then saturation. Sustainable companies layer new loops as existing ones mature ([Casey Winters](https://www.caseyaccidental.com/p/finding-the-next-wave-of-growth-s)).

The S-Curve Pattern

```

Users

| ___________ <- Saturation

| /

| / <- Rapid growth

| /

| /

| __/ <- Slow start

|___________/

|_________________________ Time

```

Sequencing Strategy

Phase 1: Find your first working loop. Focus all energy on getting one loop to work. Do not diversify too early. Most startups fail because they spread resources across multiple loops before any single one is working.

Phase 2: Optimize the working loop. Once working, optimize conversion rates at each step. Push the loop up the S-curve. Build lubricants (see Racecar Framework in plg-strategy).

Phase 3: Detect saturation signals. Watch for:

• Declining growth rate despite increasing investment
• Rising CAC in paid loops
• Declining invite acceptance rates in viral loops
• Diminishing returns on content production in content loops
• Market penetration exceeding 30-40% of addressable segment

Phase 4: Invest in the next loop. Before the current loop fully saturates, invest in developing the next growth engine. The timing is critical: too early and you starve the working loop; too late and growth stalls.

Common Sequencing Patterns

| Company | Loop 1 | Loop 2 | Loop 3 |

|---------|--------|--------|--------|

| Dropbox | Viral (referral program) | Content (SEO for storage-related terms) | Paid (enterprise marketing) |

| Slack | Viral (team invites) | Sales (enterprise outbound) | Content (community/ecosystem) |

| HubSpot | Content (inbound marketing blog) | Freemium (free CRM) | Sales (partner channel) |

| Figma | Viral (multiplayer design) | Content (community templates) | Sales (enterprise) |

---

Micro Loops (Tactical)

Micro loops are the specific, measurable growth loops operating at the product level. These are the loops described in the sections above: viral loops, content loops, paid loops, and sales loops. They are:

• Directly measurable (conversion rates, cycle time, coefficient)
• Optimizable through product and growth experiments
• Owned by specific teams
• Operate on cycle times of days to weeks

Macro Loops (Strategic Tailwinds)

Macro loops are broader strategic forces that strengthen over time and create compounding advantages. They are harder to measure directly but create powerful long-term moats:

Network Effects

• Direct: Each new user makes the product more valuable for all existing users (social networks, messaging apps)
• Cross-side: Users on one side of a platform attract users on the other side (marketplaces, platforms)
• Data: More users generate more data, which improves the product for everyone (recommendation engines, AI products)
• Diagnostic: Does adding one more user make the product measurably better for existing users?

Economies of Scale

• More users reduce per-user costs, allowing lower pricing or higher investment in product
• More content reduces content acquisition cost per piece
• More data reduces R&D cost per insight
• Diagnostic: Are your unit economics improving as you scale?

Brand

• Trusted brand reduces acquisition cost (higher conversion rates, more word-of-mouth)
• Brand becomes shorthand for the category ("Slack it to me", "Figma file")
• Diagnostic: Is your branded search volume growing faster than your paid spend?

Switching Costs

• The more users invest in the product (data, workflows, integrations, team adoption), the harder it is to leave
• Switching costs create retention that compounds: longer retention = higher LTV = more fuel for growth
• Diagnostic: What would a user lose if they switched? How much effort would migration require?

---

When the user asks you to map their growth loops, produce this deliverable:

```markdown

# Growth Loop Map: [Company/Product Name]

Loop 1: [Name]

Type: [Viral / Content / Paid / Sales]

Status: [Working / Experimental / Conceptual]

S-Curve Stage: [Slow Start / Rapid Growth / Approaching Saturation / Saturated]

Loop Diagram:

[User Action] -> [Output Created] -> [Distribution Channel] -> [New User Acquisition] -> [Loop Restarts]

Quantitative Model:

| Step | Description | Conversion Rate | Volume (monthly) |

|------|-------------|-----------------|-------------------|

| 1 | [Action] | [X%] | [N] |

| 2 | [Output] | [X%] | [N] |

| 3 | [Distribution] | [X%] | [N] |

| 4 | [New signup] | [X%] | [N] |

Loop Coefficient (K): [X.XX]

Cycle Time: [X days/weeks]

Multiplier: [1/(1-K)]

Hypothetical Maximum Analysis:

| Step Optimized | New K | Improvement |

|---------------|-------|-------------|

| Step 1 -> 100% | [X.XX] | [Nx improvement] |

| Step 2 -> 100% | [X.XX] | [Nx improvement] |

| Step 3 -> 100% | [X.XX] | [Nx improvement] |

| Step 4 -> 100% | [X.XX] | [Nx improvement] |

Highest-Leverage Optimization: [Step N] because [reason]

Loop 2: [Name]

[Same format]

| Timeline | Primary Loop | Status | Next Loop Investment |

|----------|-------------|--------|---------------------|

| Now - Q+1 | [Loop 1] | Optimize | Begin R&D on [Loop 2] |

| Q+2 - Q+3 | [Loop 1] + [Loop 2] | [Loop 1] maintaining, [Loop 2] scaling | Explore [Loop 3] |

| Q+4+ | [All loops] | Portfolio management | [Next frontier] |

| Macro Loop | Strength (1-5) | Evidence | Investment Priority |

|-----------|----------------|----------|-------------------|

| Network Effects | [Score] | [Evidence] | [H/M/L] |

| Economies of Scale | [Score] | [Evidence] | [H/M/L] |

| Brand | [Score] | [Evidence] | [H/M/L] |

| Switching Costs | [Score] | [Evidence] | [H/M/L] |

1. [Top priority optimization with expected impact]
2. [Second priority]
3. [Third priority]

```

---

• Related skills: viral-loops -- for deep design of viral and referral mechanics
• Related skills: plg-strategy -- for the broader PLG strategy context (Racecar Framework, Motions x Levers)
• Related skills: growth-modeling -- for quantitative growth projections and scenario planning
• Related skills: plg-mental-models -- for compounding vs linear growth, S-curves, and network effects
• Related skills: engagement-loops -- for retention-focused loops within the product
• Related skills: referral-program -- for incentivized referral loop design
• Related skills: growth-experimentation -- for testing and optimizing loop conversion rates