A Bangkok apparel brand with a team of thirty sits down to do its first TikTok Shop competitor analysis. The obvious move is the sales leaderboard: sort the market by units sold, take the top five, study what they do. Two weeks later the team has a playbook built on a store selling at roughly ฿36 a unit, and their own average order value is seventeen times that. The playbook does not transfer, and the reason is not execution. It is the list.
This is written for the brand or agency running apparel in Thailand with ten to fifty people, where one competitor analysis decides whether the next month of work compounds or wastes itself. The data below is a full pull of the Thai market, sorted two different ways, to show how far apart the two answers land.
The two rankings barely overlap
Pulling the competitor shop list for Thailand returns 17,892 stores. Sorted by units sold, the top of the list is dominated by low-ticket, high-velocity sellers. Sorted by GMV — the same stores, same market, same day — the top of the list is a completely different set of businesses.
| Rank | By units sold | Units | By GMV | GMV (THB) |
|---|---|---|---|---|
| 1 | MS.Bra | 18M | WPN Mobile | 3.72B |
| 2 | Nownow shop 000 | 8.1M | baanthaihardware | 2.04B |
| 3 | Global future mall | 5.7M | 18JEL.THAILAND | 1.41B |
| 4 | Dodolove Thailand | 5.1M | CAIJIANHAN | 1.28B |
| 5 | Global Smart Shopping | 4.9M | MIGU MALL. | 1.21B |
Across the two top-twenty lists, exactly one store appears in both. That is not a rounding error in the data. It is what happens when a market contains two different kinds of business that happen to share a storefront format.
What the gap actually means
Divide GMV by units sold and the shape of each list becomes obvious. The units leader works out to roughly ฿36 of GMV per unit. The GMV leader works out to roughly ฿3,700. The top of the GMV list includes a store at about ฿7,100 per unit — a store that shipped 179,000 units and still cleared ฿1.28 billion.
Two hundred times the price per unit is not a difference in marketing skill. It is a different business. One sells volume through cheap items and lives on repeat purchase and traffic scale. The other sells fewer, more expensive things and lives on assortment, trust, and the ability to hold price. A creator strategy that works for the first actively damages the second: discount-led affiliate offers on a ฿7,000 item destroy the margin that justifies the price.
The practical consequence is that “study the market leader” is an incomplete instruction in this market. You have to say which leader. For an apparel brand at a mid price point, the relevant benchmark is not the units leader and not the GMV leader either — it is the store ranked around its own price band, which is a list you have to build rather than sort.
The gap also explains a common disappointment. Teams copy the content style of a high-volume competitor, run it for a month, and see traffic rise with no revenue behind it. The style was never the mechanism. The mechanism was a supply chain that could profit at ฿36 a unit. DAMI’s product view on a competitor store shows which few items actually carry its volume, and that list usually explains the business faster than a week of watching its videos.
Building the benchmark list you actually need
Three filters do most of the work, and they are faster to apply than a full market scan suggests.
Filter by category first, at the level your products compete in — apparel against apparel, not apparel against general merchandise. Then filter by the price band you sell in, which removes most of the noise from both extreme ends. Then look at follower count and rating together: a store with 500 followers and a billion baht of GMV is running something other than creator-led commerce, usually brand-owned traffic or a distribution arrangement, and copying its content strategy will not reproduce its revenue.
The reason this is a filter problem rather than a sorting problem is that GMV and units sold are separate conditions in DAMI’s competitor search, not one ranking with a toggle. Applying both at once — GMV above your own monthly figure, units sold inside a plausible band — shrinks 17,892 stores to something a team can actually look at in an afternoon.
For apparel specifically in Thailand, the useful signal is a store selling in a comparable price band that also shows recent creator activity. That combination is what makes a competitor replicable. DAMI’s competitor shop search lets you filter by category, GMV range, unit sales, rating and follower count before you look at anyone’s content, and then pull the creators behind a chosen product so the comparison moves from “what do they post” to “who sells it for them”. The reverse-lookup workflow is the version of this that ends in a creator list rather than a spreadsheet.
Two limits on the method, stated plainly. GMV here is platform-reported and gross of cancellations and refunds, which in Thailand with cash on delivery means the real number lands lower, sometimes by a tenth. And a store’s GMV tells you what it achieved, not what it spent; a brand buying its way up the ranking looks identical to one earning it.
FAQ
Should I benchmark against the GMV leader in my category?
Only if your price band matches. The GMV leader is usually the brand with the strongest supply position, and the playbook that got it there — assortment depth, price holding, distribution — is not something a thirty-person team can copy in a quarter.
Why do so many top stores show zero followers and zero rating?
Several stores in the list show blank rating and follower fields. That is usually a data gap on new or recently rebranded shops rather than a real absence of buyers, so treat those fields as missing rather than as a signal.
Is units sold a better metric than GMV for a small brand?
Neither is good on its own. Units sold tells you how many times a buying decision happened, GMV tells you how much each decision was worth. A small brand should track the ratio between them, because that is the number its own strategy is trying to move.
Before the next market scan, decide which question you are asking. If it is “how do high-volume sellers operate”, sort by units. If it is “where is the money”, sort by GMV. If it is “who should I actually study”, filter to your own price band first — the category benchmarks will tell you whether your numbers are normal for that band before you go looking for someone to copy.
Then make it operational: the point of a competitor list is not the list. In the DAMI workspace you can take the shortlist you just built and turn the creators behind those stores into an outreach list, which is the only step where the research starts paying for itself.
