SQL 滚动 LTV（生命周期价值）

Question

我正在尝试滚动计算客户生命周期价值。我使用的基本公式是'SUM(revenue) / COUNT(DISTINCT CUSTOMERS)'，但是当试图从它向后移动的任何一天获取这些数字时遇到了问题。我下面的代码不正确，但也尝试了同样不起作用的 PARTITION 代码。

    CREATE TEMP TABLE customer_revenue AS 
    (
    SELECT TRUNC(timestamp) AS "order_date", COUNT(DISTINCT customer_email) AS "customers", 
      SUM(revenue)-SUM(discount)-SUM(shipping)-SUM(tax) AS "revenue"
    FROM public.fact_shopify_orders
    GROUP BY TRUNC(timestamp)
    );

    SELECT TRUNC(SO.timestamp) AS "date", SUM(CR.revenue) / COUNT(customers) AS "LTV"
    FROM customer_revenue CR
    LEFT JOIN public.fact_shopify_orders SO ON CR.order_date = SO.timestamp 
    WHERE CR.order_date <= SO.timestamp
    GROUP BY TRUNC(SO.timestamp)
    ORDER BY TRUNC(SO.timestamp) DESC

Answer 1

我想你想要滚动和count(distinct)。后者有点棘手，但您可以使用基于第一次看到客户的标志轻松模拟它：

SELECT date,
       ( SUM(SUM(net_revenue)) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) /
         SUM(SUM( (seqnum = 1)::int )) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
        ) as LTV
FROM (SELECT so.*, TRUNC(SO.timestamp) as date,
             (revenue - discount - shipping - tax) as net_revenue,
             ROW_NUMBER() OVER (PARTITION BY customer_email ORDER BY timestamp) as seqnum
      FROM public.fact_shopify_orders so
     ) so
GROUP BY date;

编辑：

我认为 Redshift 支持带有聚合的窗口函数。 . .但是有一些数据库没有。你可以试试这个：

SELECT date,
       ( SUM(net_revenue) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) /
         SUM(num_firsts) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
        ) as LTV
FROM (SELECT date, SUM(net_revenue) as net_revenue,
             SUM( (seqnum = 1)::int ) as num_firsts
      FROM (SELECT so.*, TRUNC(SO.timestamp) as date,
                   (revenue - discount - shipping - tax) as net_revenue,
                   ROW_NUMBER() OVER (PARTITION BY customer_email ORDER BY timestamp) as seqnum
            FROM public.fact_shopify_orders so
           ) so
      GROUP BY date
     ) so;

Here 是在 Postgres 中运行的类似版本。