如何从事务数据创建流失表？答案

【问题标题】：How to create churn table from transactional data?如何从事务数据创建流失表？
【发布时间】：2020-04-25 21:03:03
【问题描述】：

目前我的交易表有每个月的客户交易数据。 Account_ID 标识客户的 ID。 Order_ID 是客户已下订单的数量。 Reporting_week_start_date 是从星期一开始的每笔交易发生的那一周（Date_Purchased）。

如何在每笔交易完成后创建一个新表来识别 customer_status？请注意，尽管没有进行任何交易，但新表的 Reporting_week_start_date 直到当前日期。

客户状态

- New : customers who made their first paid subscription
- Recurring :  customers with continuous payment
- Churned :  when customers' subscriptions had expired and there's no renewal within the next month/same month
- Reactivated : customers who had churned and then returned to re-subscribe

事务表

Account_ID  | Order_ID |  Reporting_week_start_date| Date_Purchased | Data_Expired
    001     | 1001     |       31 Dec 2018         |   01 Jan 2019  |    08 Jan 2019
    001     | 1001     |       07 Jan 2019         |   08 Jan 2019  |    15 Jan 2019
    001     | 1001     |       14 Jan 2019         |   15 Jan 2019  |    22 Jan 2019    #Transaction 1
    001     | 1001     |       21 Jan 2019         |   22 Jan 2019  |    29 Jan 2019
    001     | 1001     |       28 Jan 2019         |   29 Jan 2019  |    31 Jan 2019

    001     | 1002     |       28 Jan 2019         |   01 Feb 2019  |    08 Feb 2019
    001     | 1002     |       04 Feb 2019         |   08 Feb 2019  |    15 Feb 2019    #Transaction 2
    001     | 1002     |       11 Feb 2019         |   15 Feb 2019  |    22 Feb 2019
    001     | 1002     |       18 Feb 2019         |   22 Feb 2019  |    28 Feb 2019

    001     | 1003     |       25 Feb 2019         |   01 Mar 2019  |    08 Mar 2019
    001     | 1003     |       04 Mar 2019         |   08 Mar 2019  |    15 Mar 2019
    001     | 1003     |       11 Mar 2019         |   15 Mar 2019  |    22 Mar 2019    #Transaction 3
    001     | 1003     |       18 Mar 2019         |   22 Mar 2019  |    29 Mar 2019
    001     | 1003     |       25 Mar 2019         |   29 Mar 2019  |    31 Mar 2019

    001     | 1004     |       27 May 2019         |   01 Jun 2019  |    08 Jun 2019
    001     | 1004     |       03 Jun 2019         |   08 Jun 2019  |    15 Jun 2019    #Transaction 4
    001     | 1004     |       10 Jun 2019         |   15 Jun 2019  |    22 Jun 2019
    001     | 1004     |       17 Jun 2019         |   22 Jun 2019  |    29 Jun 2019
    001     | 1004     |       24 Jun 2019         |   29 Jun 2019  |    30 Jun 2019

预期输出

Account_ID  | Order_ID |  Reporting_week_start_date| Customer_status
    001     | 1001     |       31 Dec 2018         |   New  
    001     | 1001     |       07 Jan 2019         |   New               #Transaction 1
    001     | 1001     |       14 Jan 2019         |   New
    001     | 1001     |       21 Jan 2019         |   New  
    001     | 1001     |       28 Jan 2019         |   New  

    001     | 1002     |       28 Jan 2019         |   Recurring        
    001     | 1002     |       04 Feb 2019         |   Recurring         #Transaction 2
    001     | 1002     |       11 Feb 2019         |   Recurring  
    001     | 1002     |       18 Feb 2019         |   Recurring  

    001     | 1003     |       25 Feb 2019         |   Churned  
    001     | 1003     |       04 Mar 2019         |   Churned           #Transaction 3
    001     | 1003     |       11 Mar 2019         |   Churned      
    001     | 1003     |       18 Mar 2019         |   Churned    
    001     | 1003     |       25 Mar 2019         |   Churned    

    001     |    -     |       1 Apr 2019          |   Churned  
    001     |    -     |       08 Apr 2019         |   Churned    
    001     |    -     |       15 Apr 2019         |   Churned      
    001     |    -     |       22 Apr 2019         |   Churned    
    001     |    -     |       29 Apr 2019         |   Churned

    001     |    -     |       29 Apr 2019         |   Churned  
    001     |    -     |       06 May 2019         |   Churned    
    001     |    -     |       13 May 2019         |   Churned      
    001     |    -     |       20 May 2019         |   Churned    
    001     |    -     |       27 May 2019         |   Churned

    001     | 1004     |       27 May 2019         |   Reactivated  
    001     | 1004     |       03 Jun 2019         |   Reactivated       #Transaction 4
    001     | 1004     |       10 Jun 2019         |   Reactivated  
    001     | 1004     |       17 Jun 2019         |   Reactivated  
    001     | 1004     |       24 Jun 2019         |   Reactivated'
    ...
    ...
    ...
    current date

【问题讨论】：

这是家庭作业/学习作业吗？到目前为止，您尝试过什么？
您的数据中有三个日期。完全不清楚数据与您指定的规则有什么关系。
客户状态（Lead & Lag）实际上与 date_purchased 进行比较，而不是 Reporting_week_start_date。 Reporting_week_start_date 用于在新表中创建记录 - 从 data_purchased 的第一个 Reporting_week_start_date 到当前日期的最后一个 Reporting_week_start_date。像队列分析。还有另一条规则 - 每个 order_id 必须具有相同的客户状态。 order_id 改变时客户状态改变（下一笔交易）

标签： sql google-bigquery lag lead churn

【解决方案1】：

我认为您只需要窗口函数和case 逻辑。假设您指的日期是Reporting_week_start_date，那么逻辑如下所示：

select t.*,
       (case when Reporting_week_start_date = min(Reporting_week_start_date) over (partition by account_id)
             then 'New'
             when Reporting_week_start_date < dateadd(lag(Reporting_week_start_date) over (partition by account_id order by Reporting_week_start_date), interval 1 month)
             then 'Recurring'
             when Reporting_week_start_date < dateadd(lead(Reporting_week_start_date) over (partition by account_id order by Reporting_week_start_date), interval -1 month)
             then 'Churned'
             else 'Reactivated'
        end) as status
from transactions t;

这些不是完全您指定的规则。但它们似乎对您想要做的事情非常合理的解释。

【讨论】：