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marketing-loopslisted

Use when the user wants to build or diagnose a compounding growth loop — referral, viral, content, or paid-recycling — rather than optimize a linear funnel. Also use when the user mentions growth loops, viral loop, viral coefficient, k-factor, referral loop, content loop, network effects, compounding growth, flywheel, "how do we grow without spending more", or "why doesn't our growth compound". Computes the loop factor, shows what it buys in amplification and effective CAC, finds the throttling stage, and projects users over time.
sarojkjha/aaj-marketing-skills · ★ 0 · AI & Automation · score 70
Install: claude install-skill sarojkjha/aaj-marketing-skills
# Marketing Loops Model growth that compounds because its output feeds back as input — not a funnel with the ends taped together. Most of the catalog treats growth as a **funnel**: stages, hand-offs, leaks to plug, a linear path from stranger to customer. `lifecycle-and-retention`, `onboarding-activation`, and `signup-flow-optimizer` all optimize that path. This skill is for the other model. A **loop** is growth where each turn produces the input to the next turn — users invite users, content attracts people who make content, revenue funds acquisition that produces revenue. **The one question a funnel never asks, and a loop lives or dies on: does one turn of the loop produce more than one turn's worth of input?** That ratio is the loop factor, k. It's the single number that separates a channel that compounds from one that merely converts. A funnel optimizer can improve every stage and still never know whether the thing loops, because "does output exceed input" is not a question the funnel frame contains. **The misconception this skill exists to correct is that k ≥ 1 is the goal.** True virality — every user producing more than one new user, growth sustaining itself with no paid input — is rare, fragile, and usually the wrong target. The durable reality is sub-viral: a k of 0.5 to 0.8 that doesn't run on its own but more than doubles the value of every acquired user. A k=0.6 loop makes each paid acquisition do the work of 2.5. Teams chase k ≥ 1, miss it, and conclude they