Growth Loop Builder

Growth Loop Builder

What a Growth Loop Is

A growth loop is a system where growth feeds itself. Each user, action, or dollar of revenue creates the input for acquiring the next user — without proportional increase in effort or spend.

The opposite of a growth loop: a funnel. Funnels require constant top-of-funnel effort to produce output. When you stop feeding the funnel, growth stops. Growth loops compound — each cycle produces slightly more than the last.

The test: "Does each new user create the conditions for the next new user?" If yes, you have a loop. If not, you have a funnel.

Quick Start

Say: "Build my growth loop" or "How do I grow without paid ads?" or "Design a growth loop for me"

Output: growth-loop.md — the highest-potential loop type, specific design, implementation steps, and the metric that tells you the loop is working.

Parallel Execution

Four loop types. Four independent evaluations. All run simultaneously.

Before spawning, gather:

• founder-context.md — product description, customer profile, current acquisition channels
• customer-profile.md — how the target customer makes decisions, who they influence
• pmf-assessment.md — which signals are strongest (points to which loop type fits best)
• Any data on current user behavior: referrals, content creation, usage patterns

Spawn these 4 agents simultaneously:

Agent 1 — Viral Loop Evaluator A viral loop exists when users invite others as a natural part of using the product.

True viral loops:

• Calendly: sending a booking link exposes the product to a new person
• Dropbox: sharing a folder requires the recipient to have Dropbox
• Loom: watching a loom video shows the product to the viewer
• Zoom: joining a call requires downloading Zoom

Questions to assess fit:

• Does using the product naturally expose it to non-users?
• Is there a feature where value increases when others join?
• Can the invite/share be a core product action rather than a marketing bolt-on?
• What is the current viral coefficient (k-factor)? (invites sent per user × conversion rate)

Returns: Viral loop fit score (1–5) + whether a natural viral mechanic exists + specific design + k-factor estimate + what would need to be true for k > 1

Agent 2 — Content Loop Evaluator A content loop exists when users generate content that attracts new users, who generate more content.

Content loop examples:

• YouTube: creators upload → viewers discover → some become creators
• Yelp: reviewers write → searchers find → some become reviewers
• Reddit: posters share → readers engage → some become posters
• Product Hunt: makers launch → hunters discover → some become makers

Questions to assess fit:

• Does using the product generate content or data that's valuable to non-users?
• Can that content be indexed by search engines or discovered on social platforms?
• Is there a "creator" user type that produces content consumed by a "consumer" type?
• What's the incentive for users to create content? (recognition, SEO benefit, social proof)

Returns: Content loop fit score (1–5) + whether a content creation mechanic exists + specific design + SEO/discovery opportunity + what type of content users already create that could be amplified

Agent 3 — Product Loop Evaluator A product loop exists when usage of the product generates data or network effects that improve the product, attracting more users.

Product loop examples:

• Waze: drivers report traffic → map improves → attracts more drivers → more data
• Spotify: listeners create playlists → algorithm improves → attracts more listeners
• Duolingo: learners practice → AI improves → better app → more learners
• Notion: users create templates → template gallery grows → attracts new users

Questions to assess fit:

• Does more usage generate data that improves the product for all users?
• Is there a "community asset" (template gallery, benchmark data, community answers) that grows with usage?
• Do network effects exist — does the product get more valuable as more users join?
• What unique data asset is the product generating that competitors don't have?

Returns: Product loop fit score (1–5) + whether data/network effects exist + specific design + the unique data asset being generated + competitive moat this creates over time

Agent 4 — Sales Loop Evaluator A sales loop exists when revenue from customers is reinvested into acquisition in a way that generates more revenue than it costs — the loop funds itself.

Sales loop examples:

• HubSpot: content → leads → revenue → more content team → more leads
• Salesforce: revenue → enterprise sales team → bigger contracts → more revenue
• Any SaaS with < 12 month payback period: CAC paid back → reinvest in acquisition → more customers

Questions to assess fit:

• What is the current (estimated) customer acquisition cost?
• What is the estimated LTV / payback period?
• If payback period is < 12 months, is there budget to reinvest in the loop?
• What acquisition channel has the best CAC and is most scalable?

Returns: Sales loop fit score (1–5) + estimated CAC/LTV ratio + whether payback period supports a self-funding loop + specific channel to invest in + when this loop makes sense (usually post-PMF, not pre-PMF)

Wait for all 4 agents. Orchestrator selects and designs.

Synthesis (Orchestrator Only)

1. Rank loops by fit score. The highest-scoring loop gets the full design treatment.

2. Reality check: The best loop type is the one that works with the product's natural mechanics — not the one that sounds most exciting. A marketplace forcing a viral loop that doesn't fit will fail. A B2B SaaS forcing a content loop without writers will fail.

3. Design the primary loop in full detail:

[Input] → [Product Action] → [Output] → [Acquisition Channel] → [New User] → [Input]

4. Write growth-loop.md:

# Growth Loop — [Product Name] — [YYYY-MM-DD]

## Primary Loop: [Viral / Content / Product / Sales]

### The Loop

[Input] → [Action] → [Output] → [Channel] → [New User joins] → [becomes Input]

### Why This Loop, Not the Others
| Loop Type | Fit Score | Reason Not Primary |
|-----------|-----------|-------------------|
| Viral | [X]/5 | [One sentence] |
| Content | [X]/5 | [One sentence] |
| Product | [X]/5 | [One sentence] |
| Sales | [X]/5 | [One sentence] |

### The Design

**Step 1 — [Input]:** [What triggers the loop — a user action, a piece of content, a referral]

**Step 2 — [Product Action]:** [What happens in the product that creates the output]

**Step 3 — [Output]:** [What the loop produces that attracts new users]

**Step 4 — [Channel]:** [How the output reaches potential new users]

**Step 5 — [New User]:** [What the new user does that feeds back into Step 1]

### Implementation Plan

**Week 1:** [The minimum change to create a working version of this loop]
**Week 2–3:** [What makes the loop more efficient]
**Week 4+:** [What scales the loop once it's working]

### The Loop Metric
The single number that tells you the loop is working:
**[Metric]** — Target: [X]. Current: [Y].

A loop is working when this metric is positive and improving cycle-over-cycle.

### What Could Break It
[The one assumption in this loop that must be true for it to work — and how to test it]

Sequential Fallback (Codex)

Evaluate each loop type sequentially:

1. Viral loop → fit score + design notes
2. Content loop → fit score + design notes
3. Product loop → fit score + design notes
4. Sales loop → fit score + design notes
5. Select highest fit → full design → write growth-loop.md

Related Skills

• Use pmf-signal-reader before this — word-of-mouth signal points to viral or content loops; engagement depth points to product loops
• Use north-star-definer — the loop metric should align with the north star
• Use retention-loop-designer alongside — retention and growth loops reinforce each other
• Use founder-partner to decide when to focus on growth vs. when to keep compounding on product quality

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