/dm:loop-detect

Purpose

Detect, model, and optimize growth loops in the business. Identify existing compounding loops — viral (users invite users), content (content attracts users who create content), data (more users improve the product which attracts more users), paid (revenue funds ads that generate more revenue), ecosystem (integrations attract users who build integrations), and community (members attract members who contribute value). Model each loop's effectiveness with amplification factors and cycle times, find bottlenecks that limit compounding, and propose new loops based on the business model and current strengths.

Input Required

The user must provide (or will be prompted for):

• Business metrics: Key growth and engagement data — user acquisition numbers (signups, activations, sources), content production volume (blog posts, UGC, social mentions), revenue figures (MRR, ARPU, LTV), referral data (invites sent, referral conversions, viral coefficient), engagement metrics (DAU/MAU, session frequency, feature adoption), and retention rates (weekly, monthly, annual). Historical data across at least 3 months preferred for trend detection
• Business model: The company's primary business model — SaaS (subscription software), eCommerce (product sales), marketplace (connecting buyers and sellers), media (content and advertising), B2B services (consulting, agency), developer tools (API/platform), community/social (network effects), or hybrid. This determines which loop archetypes are most relevant and what amplification factors to expect
• Known growth drivers: What the user already knows about what drives growth — "most customers come from organic search", "referral program drives 30% of signups", "our API marketplace is growing", "content marketing is our main channel". Helps prioritize which loops to model first and calibrate the detection algorithm
• Growth goals (optional): Target growth rate or specific metrics the user wants to achieve — "double MRR in 12 months", "reach 10K DAU", "reduce CAC by 40%". If provided, loop proposals and investment recommendations are optimized toward these goals
• Constraints (optional): Budget limits, team size, technical constraints, or channel restrictions that affect which loops are feasible — "engineering team is 5 people", "marketing budget is $20K/month", "can't do paid social due to industry regulations"

Process

1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply business model, industry benchmarks, known channels, and audience characteristics to calibrate loop detection thresholds and benchmark amplification factors. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/dm:brand-setup)?" — or proceed with defaults.