Rnote API Rnote API
Back to Blog

How Beauty & Skincare Brands Use RedNote Data

Rnote API Team · · 25 views · 中文
Xiaohongshu Data Beauty Industry Use Case

Beauty and skincare is one of RedNote (Xiaohongshu)'s loudest categories — reviews, ingredient deep-dives, "dupe" lists, and recommendations all happen here. For beauty brands and agencies, whoever puts this public data to work understands trends faster, picks the right creators, and manages reputation better. Here's how beauty teams use the Rnote API to turn "scrolling RedNote" into "reading data."

Questions beauty teams ask most

  • Which ingredients / categories are trending? — Is it a routine like AM vitamin C / PM retinol, or did an affordable serum suddenly blow up?
  • Which creators are worth working with? — Audience fit, real engagement, niche content.
  • What do users really think of my product? — Comments are more honest than surveys.
  • What are competitors doing? — New launches, creator partnerships, share-of-voice shifts.

Answer them with data

Goal Endpoints How
Trend discovery keyword search search/notes Search "serum", "sunscreen", "affordable makeup", sort by likes
Creator vetting creator analytics user/info user/posted Compute engagement rate, check niche fit
Reputation comment data note/comments Extract frequent praise and complaints
Products & pricing product data product/detail product/reviews Compare competitor prices and buyer reviews

A practical example (Python)

import requests

API = "https://rnote.dev/api/v2/crawler"
H = {"X-API-Key": "YOUR_API_KEY"}

# Find this week's top "affordable serum" notes and mine their comments for reputation
notes = requests.get(f"{API}/search/notes",
                     params={"keyword": "affordable serum", "sort_type": "popularity_descending"},
                     headers=H).json()

for note in iter_notes(notes):                 # iterate per response shape
    comments = requests.get(f"{API}/note/comments",
                            params={"note_id": note["note_id"]},
                            headers=H).json()
    # Aggregate comment keywords -> "what users care about most: selling points / complaints"

Tips

  • Run on a cadence — Ingredient and category heat shifts fast; collect weekly to build trend curves.
  • Only successful requests are billed — Validate your selection/vetting models on a small batch, then scale.

Get started

Upgrade beauty ops from gut feel to data. Sign up free for an API key, read the API docs and pricing, or start with the 5-minute Python tutorial.

Back to Blog