Question

因此，我有一个包含新闻文章的网站，并且试图计算每月4种用户类型。用户类型为：

1。新用户：在当月注册（其第一篇文章视图）并在当月中查看文章的用户。

2。保留用户：前一个月的新用户，或上个月和当月查看过文章的用户。

3。流失用户：前一个月的新用户或保留用户，但在当月未浏览过文章；前一个月的流失用户。

4。复活用户：上个月浏览过该月文章的用户。

**User Table A - Unique User Article Views**
- Current month = 2019-04-01 00:00:00 UTC

| user_id    | viewed_at                 |
------------------------------------------
| 4          | 2019-04-01 00:00:00 UTC   |
| 3          | 2019-04-01 00:00:00 UTC   |
| 2          | 2019-04-01 00:00:00 UTC   |
| 1          | 2019-03-01 00:00:00 UTC   |
| 3          | 2019-03-01 00:00:00 UTC   |
| 2          | 2019-02-01 00:00:00 UTC   |
| 1          | 2019-02-01 00:00:00 UTC   |
| 1          | 2019-01-01 00:00:00 UTC   |


The table above outlines the following user types:

2019-01-01
* User 1: New

2019-02-01
* User 1: Retained
* User 2: New

2019-03-01
* User 1: Retained
* User 2: Churned
* User 3: New

2019-04-01
* User 1: Churned
* User 2: Resurrected
* User 3: Retained
* User 4: New

我想要的表计算每个月每种用户类型的不同user_id。

| month_viewed_at           | ut_new | ut_retained | ut_churned | ut_resurrected
------------------------------------------------------------------------------------
| 2019-04-01 00:00:00 UTC   | 1      | 1           | 1          | 1
| 2019-03-01 00:00:00 UTC   | 1      | 1           | 1          | 0
| 2019-02-01 00:00:00 UTC   | 1      | 1           | 0          | 0
| 2019-01-01 00:00:00 UTC   | 1      | 0           | 0          | 0

Answer 1

我只是不确定从哪里开始

希望您阅读了我所有的评论，并亲自尝试了一些操作，但是由于我看不到任何更新，我想您仍然停留在这里-所以我们开始...

以下是用于BigQuery标准SQL的信息，应为您提供指导

#standardSQL
WITH temp1 AS (
  SELECT user_id,
    FORMAT_DATE('%Y-%m', DATE(viewed_at)) month_viewed_at, 
    DATE_DIFF(DATE(viewed_at), '2000-01-01', MONTH) pos,
    DATE_DIFF(DATE(MIN(viewed_at) OVER(PARTITION BY user_id)), '2000-01-01', MONTH) first_pos
  FROM `project.dataset.table`
), temp2 AS (
  SELECT *, pos = first_pos AS new_user
  FROM temp1
), temp3 AS (
  SELECT *, LAST_VALUE(new_user) OVER(win) OR pos - 1 = LAST_VALUE(pos) OVER(win) AS retained_user
  FROM temp2
  WINDOW win AS (PARTITION BY user_id ORDER BY pos RANGE BETWEEN 1 PRECEDING AND 1 PRECEDING)
)
SELECT month_viewed_at,
  COUNTIF(new_user) AS new_users,
  COUNTIF(retained_user) AS retained_users
FROM temp3
GROUP BY month_viewed_at
-- ORDER BY month_viewed_at DESC

如果要应用于您的样本数据-结果为

Row month_viewed_at new_users   retained_users   
1   2019-04         1           1    
2   2019-03         1           1    
3   2019-02         1           1    
4   2019-01         1           0

在temp1中，我们通过将viewed_at格式化为所需格式以准备在输出广告中展示来准备数据，并且由于某些抽象数据（2000-02-02），我们正在将其转换为连续的月份数，因此我们可以使用分析使用RANGE而不是ROWS
在temp2中，我们仅识别新用户，而在temp3中-保留的用户

我认为，这可能是一个好的开始，所以我把剩下的留给您

BIGQUERY：如何查询滚动的每月活跃用户/用户流失

1 个答案: