Dropshipping Creators on Xiaohongshu (RED): 23 Indexed

This is the pool of Dropshipping creators on Xiaohongshu (RED) within E-commerce / DTC — 23 of them. The narrower the niche, the higher the reply rate; searching broadly for "E-commerce / DTC creators" usually spends budget on audiences that do not overlap yours.

How this slice differs from the index as a whole

• Median following is 9,100, 2.3× the index-wide median of 3,946 — expect higher rate cards.
• 35% have recent content captured for judgement versus 45% overall — confidence here is lower than average, so pair it with manual review.
• Average authenticity is 61/100 against 55/100 index-wide, with fewer bought-follower signals than average.

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

• 1688选品中心 (@27762635367) — 1688 curated sourcing

• OHWOW纷享好物品牌总部 (@5661133423) — 3-yuan store sourcing

• 好物分享.在商言商 (@HaoDuoBao3330) — Wholesale sourcing for 2-yuan and 3-yuan stores

• 如涵爱种草I share (@95852193817) — Recruitment for the Ai Zhong Cao platform

• 便宜二手电脑+好物分享 (@1574298271) — Turning old digital devices into cash and recycling

• 简宇Ai绘画 (@26394317105) — Setting up a no-source online store

• 1688家居选品官 (@27247356569) — 1688 Dropshipping and Inventory-Free Store SOP

• 赫hehe然 (@335508492) — Pinduoduo ruin treasure hunting

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

"Dropshipping" 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.