Turn Etsy seller metrics into prioritized decision scores

Executive Summary

Etsy sellers have lots of metrics but no clear next steps. Provide a persistent decision layer that ingests Etsy data and surfaces ranked, actionable recommendations sellers can run in minutes. API availability and marketplace sophistication make this practical now. Etsy provides programmatic access to listings, orders, and traffic data so you can instrument actions and outcomes. IndieHackers upstream validation scored 85/100, showing recurring seller pain. Operational cadence for Etsy sellers is high - listing edits, reprices, and SEO tweaks happen weekly or daily - so short feedback loops let a scoring engine learn what works fast. Recent improvements in low-latency model hosting and cheap storage mean a solo founder can run per-seller scoring without large infra costs. Position as a prescriptive decision layer, not another dashboard. Concrete evidence from the IndieHackers thread: users explicitly say Im 'data rich but action poor' and want prioritized next steps. Build a scoring engine that combines each sellers historical performance with marketplace signals (search demand, category conversion benchmarks) to produce ranked recommendations. The moat comes from seller-level longitudinal data and outcome labels - if you persistently measure which recommendations were executed and the subsequent lift, you create a seller-specific performance history that improves future scores and is hard for new entrants to replicate quickly. Being solo and stackable with Next.js, Node, and Postgres allows fast iteration to capture early sellers and instrument outcomes.