Crowding on TikTok Shop varies widely by category, and so does revenue per shop

1. Introduction

Brands and creators choosing what to sell on TikTok Shop face a basic question: how many others are already selling in the category, and does a handful of products take most of the sales? A crowded category can mean proven demand, but it can also mean thin revenue per seller. A concentrated category can reward a few hits and leave little room for others.

Two strands of prior work frame this. Market concentration is conventionally summarised with the Herfindahl–Hirschman index [1], which rises as sales are held by fewer products or firms. Research on online retail also shows that a long tail of niche products can account for meaningful sales beyond the bestsellers [2], so a low concentration figure may signal room for niche entrants.

This paper describes shop counts, revenue per shop and concentration across the TikTok Shop categories in our analysis. It is descriptive: it does not test why categories differ.

2. Data

The window runs from 2 September to 1 October 2026, with the competition figures taken from the latest week, as of 29 September 2026. The sample covers 29 categories. No videos were tracked or analysed in depth for this paper, so it rests on category-level marketplace figures only.

For each category in the competition table we recorded the number of shops, the number of active products, average revenue per shop in US dollars, the share of top-product revenue taken by the ten biggest tracked products, and the Herfindahl–Hirschman index among those products. The table contains 25 categories.

Weekly category figures for the US (estimated revenue, its split between video, LIVE and the shop tab, affiliate revenue, shops and active products, and a year of weekly revenue) and daily rankings of each category’s top-selling products (unit price, commission rate, launch date and revenue by channel) were collected from several independent sources. Medians of price and commission are weighted by revenue; momentum compares the latest weeks with the same number of weeks before. This paper covers 2026-09-02 to 2026-10-01.

3. Methods

Shops and active products are counted per category in the latest week. Average revenue per shop is the category's revenue in that week divided across its shops, reported in US dollars.

Concentration is measured among the tracked top-selling products, not the whole category. We report the top ten products' share of that group's revenue and the Herfindahl–Hirschman index [1], which is higher when sales are held by fewer products. Because the index is computed on top products only, it describes how evenly sales are spread among the leaders. No statistical models or confidence intervals are used in this paper.

4. Results: shops and concentration across categories

The full table shows large differences in crowding. Home Supplies has the most shops (17,599), followed by Beauty & Personal Care (15,614), Womenswear & Underwear (13,756), Fashion Accessories (12,680) and Sports & Outdoor (12,564). The smallest shop bases are in Jewelry Accessories & Derivatives (1,321), Baby & Maternity (1,501) and Furniture (1,847).

Shop count and concentration do not move together in a simple way. Furniture has few shops and a high Herfindahl–Hirschman index (386), while Fashion Accessories has many shops and the lowest index (114). Home Supplies, with the most shops, sits in the middle on concentration (index 167, top-ten share 30.9%).

5. Results: revenue per shop

Average revenue per shop in the latest week ranges from $956 in Computers & Office Equipment to $9,495 in Furniture, roughly a tenfold gap. Health ($6,166), Womenswear & Underwear ($5,169), Beauty & Personal Care ($5,068), Shoes ($4,985) and Household Appliances ($4,545) follow Furniture.

The lowest figures are in Computers & Office Equipment ($956), Kitchenware ($1,098), Home Supplies ($1,106), Toys & Hobbies ($1,148) and Books, Magazines & Audio ($1,167). Crowding is associated with lower revenue per shop in some categories, such as Home Supplies, but not in others: Beauty & Personal Care and Womenswear & Underwear have thousands of shops and still sit near the top.

6. Results: concentration among top products

The ten biggest products take the largest share of top-product revenue in Textiles & Soft Furnishings (43.3%), Household Appliances (38.9%) and Furniture (38.0%), followed by Kitchenware (36.5%), Pet Supplies (36.3%) and Toys & Hobbies (35.8%). The Herfindahl–Hirschman index is highest in Furniture (386) and Textiles & Soft Furnishings (346).

Sales are most evenly spread in Fashion Accessories (19.1%, index 114), Womenswear & Underwear (23.1%, index 128) and Menswear & Underwear (24.6%, index 142). Health (26.7%) and Baby & Maternity (27.0%) are also on the less concentrated side. Even the highest index values are low in absolute terms, which suggests no single product dominates any of these categories.

7. Discussion

For brands, the figures suggest that shop count alone says little about opportunity. Home Supplies is crowded and has low revenue per shop, whereas Furniture and Health have comparatively few shops and higher revenue per shop. Categories with high top-ten shares, such as Textiles & Soft Furnishings, may be harder to break into if buyers gravitate to a few leading products, while low-concentration categories such as Fashion Accessories may leave more room for niche products, consistent with long-tail findings in online retail [2].

For creators, concentrated categories imply that a few products attract most of the top-product revenue, so choosing which product to promote may matter more there. These readings are associations only.

Several alternative explanations apply. Revenue per shop partly reflects price levels: higher-ticket categories such as Furniture and Household Appliances may show higher revenue per shop for that reason alone. Average revenue per shop can also be pulled up by a few large shops. Concentration among top products also depends on how many products are tracked per category.

8. Limitations

Product figures cover the top-selling products we track in each category, not every listing, so they describe what sells best rather than everything on sale. Results are associations, not causes. Paid promotion, creator audience size and product price are not fully controlled for. Revenue, views and sales are estimates, not figures reported by TikTok. The figures are a single latest-week snapshot, so they may reflect short-term or seasonal effects. The sample covers 29 categories, but the competition table contains 25, so four categories are not described. Concentration is computed among tracked top-selling products, not all products in a category, and the Herfindahl–Hirschman index is sensitive to how many products are tracked. Average revenue per shop is a mean and can be skewed by a few large shops. No video analysis informs this paper, and nothing here establishes causation.

9. Conclusion

Crowding, revenue per shop and concentration vary substantially across TikTok Shop categories, and they do not line up neatly. Home Supplies is the most crowded category and earns little per shop, while Beauty & Personal Care and Womenswear & Underwear are crowded yet earn above $5,000 per shop on average.

Brands and creators should read shop counts alongside revenue per shop and concentration before choosing a category. Because these are single-week, descriptive figures, they should be treated as a starting point rather than a forecast.