抖音小店榜2026-05-18~2026-05-24周榜

核心分析维度解读：

1. 头部效应：

   • TOP3小店日销售额占比：评估这三家小店的日总销售额占全数据集的比例，以了解它们对整体销售的贡献。
   • 类目分布：分析这三个头部店铺经营的商品类别是否集中或多样化。例如，是否有多个类目的商品销售额都很高。

2. 渠道效率：

   • 关联达人/直播/视频数与销售额的相关性：计算每个小店的关联达人、直播次数和视频发布数量，以及这些因素与日销售额之间的相关系数。这有助于理解哪种营销方式更有效。

3. 类目特征：

   • 高销量小店热门商品类目分布：识别出各小店中销量最高的几款或几个类目的产品，并分析它们在不同小店中的表现是否有共性。

4. 动销能力：

   • 动销商品数与销售额的关系：研究每个小店销售的商品数量与其总销售额之间的关系。可以是正相关、负相关或无明显关联。

具体操作步骤：

1. 数据准备:

   • 确保所有日销售额和各类店铺信息完整且准确。

2. 计算TOP3小店的日销售额占比：

   SELECT SUM(sales) AS total_sales, shop_name 
   FROM sales_data 
   GROUP BY shop_name 
   ORDER BY total_sales DESC 
   LIMIT 3;
   
   WITH top3_shops AS (
     SELECT SUM(sales) AS total_sales, shop_name 
     FROM sales_data 
     GROUP BY shop_name 
     ORDER BY total_sales DESC 
     LIMIT 3
   )
   SELECT (SUM(total_sales) / (SELECT SUM(SUM(sales)) FROM sales_data)) * 100 as top3_sales_percentage;
   

3. 分析渠道效率：

   SELECT shop_name, COUNT(DISTINCT user_id) AS unique_reaches,
          COUNT(video_id) AS total_videos,
          COUNT(live_id) AS total_lives,
          SUM(sales) AS total_sales 
   FROM engagement_data 
   GROUP BY shop_name;
   
   -- 计算相关性
   SELECT shop_name, 
          (COUNT(DISTINCT user_id) + COUNT(video_id) + COUNT(live_id)) / SUM(sales) as correlation_score 
   FROM engagement_data 
   GROUP BY shop_name;
   

4. 分析类目特征：

   WITH top_shops AS (
     SELECT shop_name, product_category, SUM(sales) AS category_sales 
     FROM sales_data 
     WHERE shop_name IN (SELECT shop_name FROM top3_shops)
     GROUP BY shop_name, product_category
   )
   SELECT product_category, SUM(category_sales) as total_category_sales 
   FROM top_shops 
   GROUP BY product_category 
   ORDER BY total_category_sales DESC;
   
   -- 热门商品类目分布
   SELECT product_category, COUNT(*) as occurrence_count 
   FROM sales_data 
   WHERE shop_name IN (SELECT shop_name FROM top3_shops) 
   GROUP BY product_category 
   ORDER BY occurrence_count DESC;
   

5. 分析动销能力：

   SELECT shop_name, COUNT(DISTINCT product_id) AS active_products, SUM(sales) as total_sales 
   FROM sales_data 
   GROUP BY shop_name;
   
   -- 动销商品数与销售额的关系
   WITH activity_data AS (
     SELECT shop_name, COUNT(DISTINCT product_id) AS active_products, SUM(sales) as total_sales 
     FROM sales_data 
     GROUP BY shop_name
   )
   SELECT (active_products / 10) as average_active_products_per_shop,
          (total_sales / 10) as average_sales_per_shop,
          linear_regression(active_products, total_sales) AS correlation_score;
   

通过这些步骤，可以全面了解各小店在不同维度的表现情况。希望这能帮助你更好地进行分析和优化策略！如果需要进一步细化或具体实现，请告诉我。

以上分析数据来源：互联岛