This step-by-step playbook shows a reproducible method to pick winning products for Amazon and Shopify without overthinking. Start by brainstorming niche ideas on eBay Completed, Amazon search results, Pinterest boards, and Etsy categories. Validate demand and SEO difficulty with Ahrefs (Search Volume + KD), then verify real sales on Amazon using Jungle Scout (monthly sales on page-one, average price, review counts, seller concentration). Estimate organic revenue from Google with a simple model: Price × Search Volume × CTR of target position × Conversion Rate (typical 1–3%). Calculate true unit economics with Landed Cost from Alibaba quotes plus ~30% for shipping/insurance/taxes. Differentiate by mining 2–3★ reviews to fix pain points (e.g., larger size, cooling layer, Velcro strap for a knee pillow example: ~4,100 searches/month, KD ≈ 8, $20–30 price). Compare sourcing paths—domestic (lower MOQs, faster communication) vs. international (best per-unit cost, longer lead times). Use quick pilots to validate cash flow on Amazon/eBay while building a Shopify store to own first-party data, improve margin, and grow LTV. Success metrics to green-light a product: KD ≤ 35, page-one average sales ≥ 150–300/month, average selling price ≥ $20, forecast margin ≥ 30%, and at least two clear differentiators. Tools referenced: Ahrefs, Jungle Scout, ShopHunter (Shopify revenue intel), Data Axle (local vendors), and trade fairs like Canton Fair/Global Sources for factory discovery.
