MySQL 一

在 MySQL 中导入 order_info_utf.csv 和 user_info_utf.csv 两个文件

orderinfo表
userinfo

1. 统计不同月份的下单人数

SELECT MONTH(paidTime),COUNT(DISTINCT userId)  # 用DISTINCT 对 userId 去重
FROM data.orderinfo  
WHERE isPaid = '已支付'  # 过滤脏数据
GROUP BY MONTH(paidTime);

不同月下单人数

2. 三月份的回购率（次月也有购买 ）和复购（当月购买次数在一次以上）率

a. 统计用户三月份的复购率

# 三月已支付的用户
SELECT * FROM data.orderinfo 
WHERE isPaid = '已支付'
AND MONTH(paidTime) = 3;

三月已支付的用户

# 不同用户 id 购买次数
SELECT userId, count(userId) FROM data.orderinfo 
WHERE isPaid = '已支付'
AND MONTH(paidTime) = 3
GROUP BY userId;

不同用户 id 购买次数

# 用户复购率
SELECT COUNT(ct), count(if(ct > 1, 1, null)), count(if(ct > 1, 1, null)) / COUNT(ct)
FROM(
    SELECT userId, count(userId) AS ct FROM data.orderinfo 
    WHERE isPaid = '已支付'
    AND MONTH(paidTime) = 3
    GROUP BY userId) t;

用户复购率

b. 用户回购率

# 每个 useId 在每月的消费情况
SELECT userId, DATE_FORMAT(paidTime, '%Y-%m-01')   # 按月统计是否有消费
FROM data.orderinfo
GROUP BY userId, DATE_FORMAT(paidTime, '%Y-%m-01');   

每个 useId 在每月的消费情况

# 回购率
SELECT t1.m, COUNT(t1.m), COUNT(t2.m) FROM(  # 以月份，t1 月购买总量，t2 月购买总量为三个字段
    SELECT userId, DATE_FORMAT(paidTime, '%Y-%m-01') m FROM data.orderinfo
    WHERE isPaid = '已支付'
    GROUP BY userId, DATE_FORMAT(paidTime, '%Y-%m-01')) t1  # t1 月购买商品的客户 id 和购买日期

LEFT JOIN(
    SELECT userId, DATE_FORMAT(paidTime, '%Y-%m-01') m FROM data.orderinfo
    WHERE isPaid = '已支付'
    GROUP BY userId, DATE_FORMAT(paidTime, '%Y-%m-01')) t2  # t2 月购买的客户 id 和购买日期

ON t1.userId = t2.userId  # 以 t1 月和 t2 月都购买商品为条件

AND t1.m = DATE_SUB(t2.m, INTERVAL 1 MONTH)  # t2 月比 t1月自增 1

GROUP BY t1.m;  # 以月份的升序排列，这里的 m 为 DATE_FORMAT(paidTime, '%Y-%m-01')
#  注：此方法可以计算任意年份的回购率

回购率

3. 统计男女用户的消费频次是否有差异

# 过滤性别为空的
SELECT * FROM DATA.userinfo
WHERE sex <> '';

过滤性别为空的

# 不同 userId 男性用户和女性用户的消费次数
SELECT o.userId, sex, count(1) 
FROM data.orderinfo o
INNER JOIN(
    SELECT * FROM DATA.userinfo
    WHERE sex <> '') t
ON o.userId = t.userId
GROUP BY userId, sex;

男性用户和女性用户的消费次数

# 男女消费平均次数
SELECT sex, AVG(ct) FROM(
    SELECT o.userId, sex, count(1) AS ct
    FROM data.orderinfo o
    INNER JOIN(
        SELECT * FROM DATA.userinfo
        WHERE sex <> '') t
    ON o.userId = t.userId
    GROUP BY userId, sex) t2
GROUP BY sex;

男女消费平均次数

4. 统计多次消费的顾客第一次和最后一次消费的间隔

# 第一次购买时间和最后一次购买时间
SELECT userId, MAX(paidTime), MIN(paidTime)
FROM  data.orderinfo
WHERE isPaid = '已支付'
GROUP BY userId 
HAVING COUNT(1);

第一次购买时间和最后一次购买时间

# 第一次购买和最后一次购买所间隔天数
SELECT userId, MAX(paidTime), MIN(paidTime), DATEDIFF(MAX(paidTime), MIN(paidTime))  # DATEDIFF() 是计算日期差值，若直接用减号则算出来的是相差的秒数
FROM  data.orderinfo
WHERE isPaid = '已支付'
GROUP BY userId 
HAVING COUNT(1);
# 注：相差为不足一天的认为值是 0 

第一次购买和最后一次购买所间隔天数

5. 统计不同年龄段，消费金额是否有差异

# 计算不同用户的年龄段
SELECT userId, CEIL((YEAR(NOW()) - YEAR(birth)) / 10)   # CEIL() 表示向上取整
FROM data.userinfo
WHERE birth > '1901-00-00';

计算不同用户的年龄段

# 购买商品用户的年龄等信息
SELECT * FROM data.orderinfo o
INNER JOIN(
    SELECT userId, CEIL((YEAR(NOW()) - YEAR(birth)) / 10) AS age  # CEIL() 表示向上取整
    FROM data.userinfo
    WHERE birth > '1901-00-00') t   # 各个奈年龄段与 orderinfo 表中的交集
ON o.userId = t.userId;

购买商品用户的年龄等信息

# 各年龄段用户消费频次
SELECT o.userId, age, COUNT(o.userId) FROM data.orderinfo o
INNER JOIN(
    SELECT userId, CEIL((YEAR(NOW()) - YEAR(birth)) / 10) AS age  # CEIL() 表示向上取整
    FROM data.userinfo
    WHERE birth > '1901-00-00') t  # 各个奈年龄段与 orderinfo 表中的交集
ON o.userId = t.userId
GROUP BY o.userId, age;

各年龄段用户消费频次

# 不同年龄段消费金额差异
SELECT age, AVG(ct)
FROM(
    SELECT o.userId, age, COUNT(o.userId) AS ct FROM data.orderinfo o
    INNER JOIN(
        SELECT userId, CEIL((YEAR(NOW()) - YEAR(birth)) / 10) AS age  # CEIL() 表示向上取整
        FROM data.userinfo
        WHERE birth > '1901-00-00') t  # 各个奈年龄段与 orderinfo 表中的交集
    ON o.userId = t.userId
    GROUP BY o.userId, age) t2
GROUP BY age;

不同年龄段消费金额差异

6. 前 20% 的用户贡献了多少额度

# 每个用户的消费总额
SELECT userId, SUM(price)
FROM data.orderinfo o
WHERE isPaid = '已支付'
GROUP BY userId;

每个用户的消费总额

# 降序排列
SELECT userId, SUM(price) AS tatal 
FROM data.orderinfo o
WHERE isPaid = '已支付'
GROUP BY userId
ORDER BY tatal DESC;

降序排列

# 统计一共有多少个用户和金额
SELECT COUNT(userId), SUM(total) 
FROM(
    SELECT userId, SUM(price) AS total
    FROM data.orderinfo o
    WHERE isPaid = '已支付'
    GROUP BY userId
    ORDER BY total DESC) as t;

统计一共有多少个用户和金额

经计算 85649 * 0.2 = 17129

# 
SELECT COUNT(userId), SUM(total) 
FROM(
    SELECT userId, SUM(price) AS total
    FROM data.orderinfo o
    WHERE isPaid = '已支付'
    GROUP BY userId
    ORDER BY total DESC
    LIMIT 17129) t;

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