1. Define the market and decision
Choose one TikTok Shop country, a category, an observation period, and a test budget. State whether you need a product shortlist, a creator shortlist, or evidence about an existing product. This keeps the AI workflow from mixing incomparable markets or producing a broad list you cannot act on.
2. Collect comparable evidence
Use a visual tool such as Kalodata or a compatible workflow with FastMoss MCP to gather product, creator, shop, and content signals. Check coverage and access first. Keep metric definitions, sources, and dates beside the output. Estimated marketplace data should be labeled as estimated.
- Look at several creators and content examples for each product.
- Separate sustained activity from one promotional spike.
- Record missing data and uncertainty rather than filling gaps with guesses.
3. Ask AI to organize the shortlist
Ask the model to group evidence by customer problem and explain why each candidate belongs on the shortlist. Require citations to the data you supplied. Treat the answer as an organized research draft. Check the products and creators in the source service before contacting anyone or making a buying decision.
4. Calculate contribution margin
Estimate selling price minus product cost, landed shipping, fulfillment, applicable marketplace fees, creator or advertising cost, and a realistic returns allowance. Confirm delivery times and supply consistency. A high estimated sales figure says little about your profit when these inputs are unknown.
5. Run a bounded test
Set a budget, a measurement period, and an inventory limit before launching. Measure real orders, refunds, fulfillment issues, and contribution margin. Use verified product facts for creative production. Scale only after the test supports the economics. This guide does not include a tested product recommendation or a promised return.
