AI Product Research for Sellers
Use AI to organize product reviews and buyer comments into evidence for a small, testable ecommerce product decision.
Use AI product research to organize evidence about buyer problems. Give the assistant a specific product question, retain the source behind each finding, and decide what to test once you have checked the evidence and sourcing costs.
Scott’s Cohort 1 exercise asks sellers to produce a product opportunity brief with customer quotes, risks, and a next test. In the public sample, 36 of 40 input items matched the research criteria and the report grouped them into seven opportunities. These counts describe the sample, not the size of a market. Open the product research demo
What should you collect?
| Source | What it helps you learn |
|---|---|
| TikTok comments | The language buyers use and the questions they ask after a demonstration |
| Amazon reviews | Specific failures, packaging problems, and unmet expectations |
| Relevant forums | How people describe the problem in context and what they already tried |
| Google Trends | Relative interest over time for selected searches |
| Your supplier | Quote, minimum order, samples, and changes the supplier can make |
Record the link, date, product or discussion, and enough context to interpret each item. Use material you can access and are allowed to process. Keep small excerpts internally to review the evidence. When publishing, summarize findings in your own words and use examples you can share.
What should the brief ask the agent to produce?
Give it a product category, selling region, a set of inputs, and one decision. For example, ask whether a collapsible storage bin should be tested in a deeper size for one specific buyer group. Require four outputs.
- A table of the source, buyer situation, problem, and evidence ID.
- A few grouped problems with supporting evidence and counterexamples.
- A separation of direct observations from the assistant’s interpretation.
- One practical test per promising idea, with the result that would change the decision.
A complaint such as “it doesn’t fit” could refer to the shelf, the item the buyer wanted to store, or the assembly. Keep those possibilities separate until the surrounding details justify grouping them.
How do you keep the report honest?
Read the original context for a sample of entries, including ones that disagree with the finding. Remove duplicates and count observations separately from reviews. Low ratings can reveal problems, but a collection of low ratings cannot estimate how often all buyers see the problem. Relative search interest also cannot tell you whether the product will sell at your price. Google Trends data FAQ
Use a sample, supplier quote, or a small test to check the key assumption. If a deeper storage bin is the idea, ask for a prototype and compare it against real shelf measurements before ordering inventory. If changing the depth pushes the minimum order or shipping cost above your limit, write that down as a reason to stop.
What goes in the final evidence pack?
Keep the brief, source list, evidence table, and decision together. A teammate should be able to trace each claim to an input and explain what would change the decision. Put unresolved questions next to the proposed test. Free TikTok cross-border ecommerce course · TikTok competitor monitoring