Amazon FBA
The video's core claim is that the most effective way to generate new Amazon FBA product ideas is what the presenter calls the "store rate method": once you find one product selling well at a healthy price and demand level, you open that seller's full catalog to find their other products, then repeat the process recursively on each new seller you discover, creating an effectively infinite source of product ideas.
Product research should start with two core checks: monthly unit demand and price point, before any deeper analysis like seasonality.
For new sellers, steady demand in roughly the 100-200+ units/month range that 'runs deep' across many products is a healthier signal than very high demand.
The presenter recommends selling above £20, arguing that below £20 it's difficult to make a profit on Amazon.
Very high demand (500+ units/month) is flagged as risky for new sellers because it implies stocking 1,500-2,000 units on the first batch, a large financial commitment.
The 'store rate method': open a seller's product listing, click 'sold by', scroll to the seller profile, and click 'see all products' to view everything else that seller sells, looking for new product ideas.
The underlying theory of the method: if a seller has one good-selling product, they probably have other good products worth investigating.
The store rate method can be used standalone or to complement Black Box (a Helium 10 product-discovery tool) research covered in an earlier video.
The method is described as a 'rabbit hole': each newly found product can be run through the same process again, opening its sellers, then their products, endlessly.
Products that are generic/plain (e.g., sleeping bags) or not mold-dependent (e.g., play tents, made by workers using machines rather than expensive batch molds) are seen as good customization opportunities.
Product research is characterized as 'a numbers game': you'll sometimes find a seller with no other useful products, and that's expected — you just keep going.
Store Rate Method — The presenter's named technique of treating any single well-performing product listing as an entry point into its seller's full catalog, based on the theory that a seller with one good product likely has other good products. Apply: Open a product listing with healthy price point and demand in X-Ray, click 'sold by' on the buy box, scroll to the bottom of the seller's profile, click 'see all products', review the full list for new product ideas, then repeat the same process on any new sellers found this way.
X-Ray (Helium 10) — A Helium 10 tool used within the browser to pull up product and seller data (demand, price point) directly on Amazon search/listing pages. Apply: Fire up X-Ray on an Amazon search results page for a chosen search term to open and filter product listings by price point and demand level before applying the store rate method to their sellers.
Black Box (Helium 10) — A separate Helium 10 product-discovery tool, covered in an earlier video, that generates a list of product results after the user enters search criteria. Apply: Enter specific criteria into Black Box to surface an initial list of product ideas (e.g., walking poles), which can then be expanded further using the store rate method on the sellers of those products.
Demand and price-point niche check — The presenter's two 'core elements' for a first-pass product research check: monthly unit demand across a niche's listings, and the price point of those listings. Apply: Look for demand that is steady and 'runs deep' across many listings (roughly 100-200+ units/month is a good zone for new sellers) and prioritize products priced above £20, before moving on to other checks like seasonality.
The presenter traces a real, literal idea-generation chain during the session: walking poles (found via Black Box) → seller Underwood Aggregator → that seller's sleeping bags and pop-up tents → a pop-up tent seller's other products → the play tent idea, demonstrating the method live rather than just describing it abstractly.
A product's demand 'running deep' (steady demand spread across many listings) is treated as a distinct and preferable signal from a product simply having high top-line demand, since the latter can drop off quickly and concentrate risk in fewer listings.
The presenter avoids electronics (the sunrise alarm clock) as a personal category preference even though the niche's demand and price stats meet his own stated criteria, showing category preference can override data signals.
'Boring' or commodity-style products (sleeping bags) are framed as attractive precisely because their genericness leaves room to 'do something fairly unique with them,' rather than being a reason to avoid them.
The method's real mechanism isn't finding one good product — it's treating any single found product as an entry point into a seller's entire catalog, which is why the presenter says he isn't 'fussed about the product' itself once he's found a seller worth checking.
«This is what I call it, it's called the store rate method.»
— 04:26
«if a seller has one good selling product, they probably have other good products.»
— 04:20
«this is like an infinite rabbit hole.»
— 04:43
«Product research is always a numbers game.»
— 08:05
«Below £20, it can be quite difficult to make a profit selling on Amazon.»
— 00:57
«that is a huge financial risk.»
— 02:11
«there is so much scope to do something inventive with this.»
— 01:31
This is a practical, tool-specific workflow (using Helium 10's X-Ray) presented as the presenter's personal, years-tested method rather than as data-driven proof that any of the four example products will succeed; useful primarily as a repeatable research process to add to a seller's toolkit.