Fashion & style Creators on Douyin: 41 Indexed

This is the pool of Fashion & style creators on Douyin within Lifestyle — 41 of them. The narrower the niche, the higher the reply rate; searching broadly for "Lifestyle creators" usually spends budget on audiences that do not overlap yours.

How this slice differs from the index as a whole

• Median following is 1,104,419, 273.0× the index-wide median of 4,045 — expect higher rate cards.
• 78% have recent content captured for judgement versus 45% overall — better-evidenced than average.
• A and B grades are 15% of this slice versus 37% overall — expect to screen through more names to find usable ones.

How to read these numbers: this covers creators indexed and validated inside Koinon Link, not every creator on the platform. Suspected bought-follower accounts are quarantined and excluded from the table above (shown separately). The authenticity score is our own anomaly detection over follower/engagement structure, not a platform-official metric. Figures update as collection continues.

Creators in this slice

• 大洪洪文化（橱窗营业中） (@4375710794001976) — Slimming outfit tips

• 六一的宝藏 (@95168625329) — ootd outfits

• 小熊出没25号5点10分入秋穿搭分享指南 (@104061298629) — autumn fashion sharing

• 小蛋黄omi（118斤 同款在橱窗） (@62288305301) — Plus-size fashion reviews

• 张红瑜（魔帝版 (@1917148434554317) — Boss babe fashion and transformations

• 广州琴姐GZQJ穿搭分享 (@1675277155442012) — Fall and winter fashion

• 小北职业穿搭 (@487804952381294) — Professional shirts and suits

• 钗然说美学穿搭 (@61121359999) — Classic style outfits

• 设计师李亦寒 (@110074662438) — Design philosophy and brand spirit

• 舞哥女装穿搭（6:00开播) (@96630985795) — Women's dress outfits

Ordered by follower tier, first 10 shown. Each link opens that creator’s public profile: what they post about, who reads it, and recent samples — no contact details.

Top 6 countries

Why this niche deserves its own view

"Fashion & style" is defined by who the audience is and what job they do, not by what the creator posts about. Two creators can both cover AI tools while their audiences barely overlap — one talks to media buyers who care about creative and bids, the other to engineers who care about APIs and deployment. Searching by topic mixes them together; searching by audience function does not.