Builds a driver-based growth model - acquisition, activation, retention, monetization, and a loop factor - that projects users and revenue bottom-up, runs base/upside/downside scenarios, and names the one constraint to work on next. Use when someone asks "build me a growth model", "what should our user projection be", "which lever moves growth most", "model our viral loop", or wants projections driven by real inputs instead of a hockey-stick guess. Do NOT use for the SaaS MRR bridge, NRR, and revenue-forecasting mechanics - use revenue-modeling. Do NOT use for decomposing historical active-user change into new, retained, resurrected, and churned - use growth-accounting. Do NOT use for per-customer CAC/LTV math - use unit-economics.
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name: Growth Model
description: Builds a driver-based growth model - acquisition, activation, retention, monetization, and a loop factor - that projects users and revenue bottom-up, runs base/upside/downside scenarios, and names the one constraint to work on next. Use when someone asks "build me a growth model", "what should our user projection be", "which lever moves growth most", "model our viral loop", or wants projections driven by real inputs instead of a hockey-stick guess. Do NOT use for the SaaS MRR bridge, NRR, and revenue-forecasting mechanics - use revenue-modeling. Do NOT use for decomposing historical active-user change into new, retained, resurrected, and churned - use growth-accounting. Do NOT use for per-customer CAC/LTV math - use unit-economics.
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# Growth Model
A growth model is not a revenue forecast with a hopeful curve. It is a driver-based system that shows how inputs (traffic, conversion, retention) compound into output (users, revenue) - and where the leverage is. The costly mistake this prevents: drawing the output curve first and back-filling assumptions to justify it, which produces a plan nobody can steer because no team owns an input.
## Operating procedure
Order matters: the loop must be identified before the equation is written, and the equation before any projection - otherwise the model quietly becomes a fixed-percent guess with extra columns.
### Step 1: gather inputs
- Current active users and the definition of "active" (a meaningful action, not a login).
- New users per period by channel, with each channel's volume and cost.
- Activation rate: the share of new users reaching first value. From real funnel data where it exists.
- Retention: the share of actives who stay each period - from cohort curves, never a flat assumption. If only aggregate churn exists, label the retention figure a guess.
- Revenue per retained user, including expansion.
- Any referral or loop evidence: invites sent, invite conversion, content-driven signups.
Use real cohort data wherever it exists (wire it from product-analytics); label everything else a guess and flag it for replacement.
### Step 2: identify the primary loop… install to load the full skill