143 lines
3.2 KiB
Markdown
143 lines
3.2 KiB
Markdown
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# SQL 分析查询模板
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## 窗口函数
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```sql
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-- 排名
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SELECT
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user_id, amount,
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ROW_NUMBER() OVER (ORDER BY amount DESC) as rank,
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NTILE(4) OVER (ORDER BY amount DESC) as quartile
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FROM orders;
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-- 同比环比
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SELECT
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DATE_TRUNC('month', order_date) as month,
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SUM(amount) as revenue,
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LAG(SUM(amount), 1) OVER (ORDER BY DATE_TRUNC('month', order_date)) as prev_month,
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LAG(SUM(amount), 12) OVER (ORDER BY DATE_TRUNC('month', order_date)) as prev_year
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FROM orders
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GROUP BY 1;
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-- 累计
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SELECT
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order_date,
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daily_revenue,
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SUM(daily_revenue) OVER (ORDER BY order_date) as cumulative
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FROM daily_stats;
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```
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## 留存分析
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```sql
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WITH cohort AS (
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SELECT user_id, DATE_TRUNC('week', MIN(order_date)) as cohort_week
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FROM orders GROUP BY 1
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),
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activity AS (
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SELECT
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o.user_id, c.cohort_week,
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DATE_TRUNC('week', o.order_date) - c.cohort_week as weeks_since
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FROM orders o JOIN cohort c ON o.user_id = c.user_id
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)
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SELECT
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cohort_week,
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weeks_since,
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COUNT(DISTINCT user_id) as users,
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COUNT(DISTINCT user_id) * 100.0 /
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FIRST_VALUE(COUNT(DISTINCT user_id)) OVER (PARTITION BY cohort_week ORDER BY weeks_since) as retention
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FROM activity
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WHERE weeks_since >= 0
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GROUP BY 1, 2;
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```
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## 漏斗分析
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```sql
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WITH funnel AS (
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SELECT
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user_id,
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MAX(CASE WHEN event='view' THEN 1 ELSE 0 END) as step1,
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MAX(CASE WHEN event='cart' THEN 1 ELSE 0 END) as step2,
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MAX(CASE WHEN event='checkout' THEN 1 ELSE 0 END) as step3,
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MAX(CASE WHEN event='pay' THEN 1 ELSE 0 END) as step4
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FROM events GROUP BY 1
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)
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SELECT
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SUM(step1) as view,
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SUM(step2) as cart,
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SUM(step3) as checkout,
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SUM(step4) as pay,
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ROUND(SUM(step2)*100.0/SUM(step1), 2) as view_to_cart,
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ROUND(SUM(step4)*100.0/SUM(step3), 2) as checkout_to_pay
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FROM funnel;
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```
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## RFM分析
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```sql
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WITH rfm AS (
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SELECT
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user_id,
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CURRENT_DATE - MAX(order_date) as recency,
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COUNT(*) as frequency,
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SUM(amount) as monetary
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FROM orders
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WHERE order_date >= CURRENT_DATE - INTERVAL '1 year'
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GROUP BY 1
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),
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scores AS (
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SELECT *,
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NTILE(5) OVER (ORDER BY recency DESC) as r,
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NTILE(5) OVER (ORDER BY frequency) as f,
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NTILE(5) OVER (ORDER BY monetary) as m
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FROM rfm
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)
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SELECT *,
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CASE
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WHEN r>=4 AND f>=4 AND m>=4 THEN '高价值'
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WHEN r>=4 AND f<3 THEN '新客户'
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WHEN r<2 AND f>=4 THEN '流失风险'
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WHEN r<2 AND f<2 THEN '已流失'
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ELSE '一般客户'
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END as segment
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FROM scores;
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```
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## 用户分群
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```sql
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-- 活跃度分群
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SELECT
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user_id,
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CASE
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WHEN last_active >= CURRENT_DATE - 7 THEN '活跃'
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WHEN last_active >= CURRENT_DATE - 30 THEN '沉默'
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WHEN last_active >= CURRENT_DATE - 90 THEN '流失风险'
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ELSE '流失'
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END as activity_segment
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FROM users;
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```
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## 常用聚合
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```sql
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-- 多维度聚合
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SELECT
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region, category,
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COUNT(*) as orders,
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COUNT(DISTINCT user_id) as users,
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SUM(amount) as revenue,
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AVG(amount) as avg_order_value
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FROM orders
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GROUP BY 1, 2
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ORDER BY revenue DESC;
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-- 百分比
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SELECT
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category,
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revenue,
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ROUND(revenue * 100.0 / SUM(revenue) OVER (), 2) as pct
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FROM category_stats;
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```
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