China Consumer Voice & Competitor Intelligence

Turn existing Chinese social-media and community data into evidence-linked consumer and competitor intelligence across RedNote / Xiaohongshu, Douyin, Weibo, Zhihu, Douban, WeChat, Bilibili, and other sources.

Unofficial tool. This Actor is not affiliated with, sponsored by, or endorsed by Xiaohongshu, RedNote, Douyin, ByteDance, Weibo, Zhihu, Douban, Tencent, WeChat, Bilibili, or any other source platform.

The output buyers actually need

This Actor does not collect credentials and does not require a platform Cookie. It analyzes JSON exported from any compatible scraper, internal data pipeline, survey, or research process and returns:

• recurring consumer concerns and decision questions
• underlying needs such as proof, safety, choice criteria, price, usage, and trust
• product and non-product alternatives in the customer's mind
• evidence-linked brand and product associations
• competitor mention share and platform coverage
• platform-specific discussion differences
• ranked content opportunities with transparent scores and source excerpts
• risky-claim screening and safer framing for sensitive topics

Report modes

• demo: run a five-platform Chinese health-category example
• consumerVoice: questions, concerns, needs, alternatives, and brand associations
• competitorAnalysis: compare two to ten named brands or products
• contentOpportunities: rank content opportunities from observed discussion gaps
• fullReport: return every module in one report

Accepted data

Use one flat uploadedRecords list:

{
  "mode": "fullReport",
  "topic": "敏感肌防晒",
  "knownEntities": ["客户品牌", "竞品甲", "竞品乙", "物理防晒"],
  "competitorNames": ["客户品牌", "竞品甲", "竞品乙"],
  "uploadedRecords": [
    {
      "id": "xhs-1",
      "platform": "xiaohongshu",
      "recordType": "post",
      "title": "敏感肌防晒怎么选",
      "text": "重点比较刺激风险、肤感和补涂难度。",
      "url": "https://example.com/source/1",
      "likes": 1200,
      "comments": 88,
      "saves": 430
    },
    {
      "id": "zhihu-1",
      "platform": "zhihu",
      "recordType": "comment",
      "text": "竞品甲和竞品乙哪个好？价格差异值不值？",
      "url": "https://example.com/source/2"
    }
  ]
}

Or group records by platform and optional brand:

{
  "mode": "competitorAnalysis",
  "topic": "敏感肌防晒",
  "competitorNames": ["品牌甲", "品牌乙"],
  "platformDatasets": [
    {
      "platform": "xiaohongshu",
      "brand": "品牌甲",
      "records": [
        {"noteId": "1", "title": "品牌甲实测", "description": "使用体验"}
      ]
    },
    {
      "platform": "douban",
      "comments": [
        {"commentId": "2", "content": "品牌乙是不是更适合通勤？"}
      ]
    }
  ]
}

Common aliases such as noteId, postId, awemeId, commentId, title, description, content, text, likes, likedCount, collects, shares, and nested engagement fields are recognized.

Important analytical boundaries

• Raw likes, comments, saves, and shares are not compared across platforms because platform metrics have different meanings and distributions.
• Counts describe the supplied sample, not the entire Chinese market.
• Evidence excerpts retain platform, source ID, URL, and optional brand labels.
• The analysis is deterministic and does not use an LLM.
• Fewer than 20 discussions, one-platform samples, missing comments, and unmatched competitors generate visible data-quality warnings.

Pricing

Report	Price
Consumer voice	$1.99
Competitor intelligence	$2.49
Content opportunities	$1.99
Full intelligence	$4.99

One event returns one report for up to 5,000 supplied discussions. Invalid and duplicate rows are skipped or reported before analysis.

Responsible use

Only process data you are authorized to use. Follow applicable platform terms, privacy, intellectual-property, advertising, and data-protection laws. Do not use the output for harassment, sensitive profiling, automated decisions about individuals, or unsupported medical, financial, or regulatory claims.

For regulated topics, the risky-claim screen is a research aid only. It does not provide legal, medical, advertising, or compliance approval.

Local development

$python3 -m unittest discover -s tests -v

After installing Actor dependencies:

pip install -r requirements.txt
python3 -m src