{"id":657,"date":"2021-04-21T08:29:27","date_gmt":"2021-04-21T08:29:27","guid":{"rendered":"https:\/\/zonhack.com\/?p=657"},"modified":"2026-09-10T12:24:32","modified_gmt":"2026-09-10T12:24:32","slug":"find-low-competitive-products-to-sell-on-amazon","status":"publish","type":"post","link":"https:\/\/zonhack.com\/zh\/find-low-competitive-products-to-sell-on-amazon\/","title":{"rendered":"How to Find Low Competition Products to Sell on Amazon"},"content":{"rendered":"

A low competition product on Amazon is not one with few listings. It is one where the listings that exist are beatable<\/strong>, and that is a different test entirely. The four signals worth screening for are: top sellers under 150 reviews, at least one page-one listing rated below 4.2 stars, no single brand holding more than about 40% of page one, and no page-one listing that has clearly been professionally optimized.<\/p>\n

Search volume tells you demand, and that is all it tells you. Those four signals tell you whether you can take any of it. Most sellers check the first and skip the second, and that is why so much product research ends in a listing that never ranks.<\/p>\n

Why most “high demand, low competition” lists are wrong<\/h2>\n

Search for that phrase and you will find hundreds of articles listing products. I’d treat every one of them with suspicion, and the reason is mechanical rather than cynical.<\/p>\n

A published list destroys the thing it describes. The moment a product appears on a widely-read page as low competition, readers act on it, and within a quarter the category has forty new sellers in it. What was low competition became a price war. In our experience the products worth having are the ones nobody has written about, which is precisely why you have to run the screen yourself instead of reading someone’s list.<\/p>\n

There is a second problem. “High demand and low competition” describes a market inefficiency, and inefficiencies are rare and short-lived on a marketplace with millions of sellers. When I hear a seller say they are looking for high demand and low competition, what they usually need is something more achievable: moderate demand and weak incumbents<\/strong>. That combination is common. It is also where the money is.<\/p>\n

The four signals that actually indicate weak competition<\/h2>\n

I’d run these against page one of your target keyword and nowhere else. Not the whole category, page one, because page one is what you are competing with.<\/p>\n\n\n\n\n\n\n\n\n
Signal<\/th>\nScreen against<\/th>\nWhat it tells you<\/th>\n<\/tr>\n<\/thead>\n
Review counts of top sellers<\/strong><\/td>\nAre any page-one listings under 150 reviews?<\/td>\nReview count is the hardest thing for a new seller to catch up on. Under 150 is reachable in months; over 2,000 is not<\/td>\n<\/tr>\n
Rating quality<\/strong><\/td>\nIs any page-one listing rated below 4.2?<\/td>\nA weak rating means unhappy customers and a product problem you can solve. This is the single best signal on the list<\/td>\n<\/tr>\n
Brand concentration<\/strong><\/td>\nDoes one brand hold more than 40% of page one?<\/td>\nA dominant brand usually means a real budget behind it. Fragmented page one means no one is defending it<\/td>\n<\/tr>\n
Listing quality<\/strong><\/td>\nAre the images and copy amateur?<\/td>\nPoor listings on page one mean the ranking came from age or luck, not effort. Those rankings are takeable<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The rating signal deserves more weight than the others. I’ve found that a category where the top three listings sit at 3.8 stars is worth more than a category with half the listings, because those reviews tell you exactly what to fix. Read the one-star and two-star reviews. If they all complain about the same defect, you have a product brief written for you by the market.<\/p>\n

The screen, with actual numbers<\/h2>\n

Here is the arithmetic I use. Take a keyword doing roughly 10,000 monthly searches, with a page-one listing at 120 reviews and a 3.9 rating.<\/p>\n

Page one on a mid-volume keyword takes maybe 8% of the clicks for a position-three slot. That is 800 visits a month. At a 12% conversion rate, 96 units. At $28 retail with $9 landed cost and $7 in Amazon fees, that is $12 a unit, so $1,152 a month in contribution<\/strong> before advertising.<\/p>\n

Now the same math on a keyword doing 60,000 searches where page one starts at 4,000 reviews. You will not reach position three. You will sit on page four, take almost nothing organically, and buy every sale through ads at a cost per click the incumbents set. The bigger keyword is the worse business.<\/p>\n

In my experience that comparison is the whole point of screening for beatable competition rather than for demand. Run the numbers before you commit inventory, not after.<\/p>\n

Where to actually look<\/h2>\n

Every method below works, and I’d use all of them rather than picking one. None of them is a shortcut, and I’d be wary of anyone who presents one as automated.<\/p>\n

Review mining on your own category.<\/strong> If you already sell something, the fastest route is the negative reviews on your competitors. Sort by most recent, one and two stars, and read a hundred of them. Repeated complaints are unmet demand with a customer already attached to it.<\/p>\n

The AliExpress and Alibaba sort.<\/strong> Sorting a supplier category by order volume tells you what is selling at wholesale, which is a leading indicator of what will sell at retail. This works, with one caveat that matters: high order volume on Alibaba often means every other Amazon seller has already found it. Use it to validate an idea, not to generate one.<\/p>\n

Google Trends for direction, not for demand.<\/strong> Google Trends<\/a> shows relative interest over time. It does not show Amazon search volume, and the two diverge often. I use it for one thing only: checking whether something I found is rising or dying. A product on a two-year decline is not worth entering however weak the competition looks.<\/p>\n

Amazon’s own autocomplete and related searches.<\/strong> Free, and closer to real buying intent than any third-party estimate. Type your seed term and read the suggestions. Then check the “Customers frequently viewed” strips on the top listings, which is Amazon telling you which products it considers substitutes.<\/p>\n

Paid research tools.<\/strong> Helium 10, Jungle Scout and the rest estimate sales from rank. The estimates are directionally useful and individually unreliable. Treat any single number as an order of magnitude, not a figure. I’d cross-check anything important against the review counts on page one, which you can see for yourself.<\/p>\n

If you want a structured version of the whole process rather than the screening logic alone, our Amazon FBA product research checklist<\/a> runs it as a sequence.<\/p>\n

How we screen this in practice<\/h2>\n

When I run this for a client the whole thing takes about forty minutes per candidate, and almost all of it is reading rather than tooling.<\/p>\n

I start with the keyword, not the product. Type it into Amazon, take a screenshot of page one, and write down four numbers: the lowest review count, the lowest star rating, how many distinct brands appear, and how many listings look professionally built. Four numbers. That is the screen, and I’ve found it separates viable from hopeless faster than any software report I have paid for.<\/p>\n

Then I read reviews for an hour. Not skim them. Read them, on the two weakest listings, sorted to most recent so the complaints reflect the current version of the product rather than one from three years ago. In our experience this is the step people skip, and it is the step that decides whether the launch works, because it is the only one that tells you what to actually build.<\/p>\n

Last, I run the contribution math from the section above with pessimistic inputs. I’d rather kill a product on paper than in a warehouse. If it only works at a 15% conversion rate and a $32 price point, it does not work, because you will get neither in your first quarter.<\/p>\n

One habit worth stealing: keep the rejects, with the date and the reason. Products you reject today can become viable later, because incumbents quit, patents lapse and categories change. In my experience a dated list of near-misses is more useful than a fresh list of ideas, and it costs nothing to keep.<\/p>\n

What disqualifies a product regardless of the numbers<\/h2>\n

I’ve watched sellers ignore this list and lose money on products that screened beautifully.<\/p>\n