E-commerce Product Scout

Problem: Sourcing Decisions Lack Checkable Evidence

Small and mid-size e-commerce sellers often choose products from experience: a keyword is appearing, a category looks profitable, or a creator recently promoted it. That approach can miss competition density, return risk, and trend durability, leading to overstocking, low-margin liquidation, or entry into saturated niches. E-commerce Product Scout turns the question “should we launch this?” into an explainable scoring problem.

How It Works: From Signals to Tiered Recommendations

The skill evaluates category signals and produces a 0-100 opportunity score plus a blue ocean / balanced / red ocean tier. The main workflow includes:

• Five-factor scoring: weights demand, competition, margin, trend, and return risk into one candidate score.
• Market tiering: maps scores into 70+ blue ocean, 50-70 balanced, or red ocean bands to rank entry priority.
• Advice and rationale: outputs non-empty reasoning and sourcing recommendations, with a top3 ranked list where blue ocean candidates appear first for manual review.

It is closer to a structured sourcing review template than a live platform scraper. In v2.1, signals still rely on seller-provided or baseline data, and weights are fixed heuristics, so it is best used after category hypotheses exist.

Boundaries and Caveats

Do not treat a single score as a deterministic forecast. For categories with high return sensitivity, or where ranking and review signals matter, validate against historical sales data. v2.2 plans to pull ranking and review signals automatically, cluster categories, and adjust weights based on seller hit rates.