背景介绍

业务介绍：用户是通过低价引流进来，然后通过复购购买高价商品，可以多次购买。低价商品和高价商品均可以退款，高价商品由于各种复杂的场景，可能会有多次退款。低价商品如果退款是全退，不存在多次退款。
业务需求：计算低价引流的流量后续的产出，复购的期限是在 30天内，退款的期限也是按 30天（退款时间-复购时间），最终交付一个按引流订单维度。

架构设计

由于引流订单和复购订单的性质不同，在数仓贴源层处理的过程中，已经将二者分离独立成表，此处是在贴源层的基础上加以处理。
将引流订单记录、复购订单记录和退款订单记录以引流订单为主表整合到一起，输出不加业务逻辑的事实表：订单链路数据表。在此基础上，再根据不同的业务逻辑处理为不同聚合表单。

数据开发

说明：为方便测试，以下使用的是 MySQL 语法，实际开发使用的是阿里云的 MaxCompute SQL，本代码的差异点在于日期函数date_add()，在 MaxCompute SQL 中，语法有一定差异，后者是date_add(<date_col_name>, 30)，如果时间字段包含日期和时间，则要使用dateadd(<datetime_col_name>,30,'dd')。

订单链路数据表好处理，把三个表根据用户信息进行 JOIN 即可，伪代码参考如下：

select *
from <引流订单记录>
left join <复购订单记录> on <用户信息>
left join <退款订单记录> on <用户信息>

有了订单链路数据表，接下来按 30天的复购和30天的退款期限聚合为引流订单产出表。
抽象出一个表单，数据记录如下，有 10 个字段

• low_price_order_id：低价订单号
• low_price_paid_time：低价订单付款时间
• low_price_paid_amount：低价订单付款金额
• low_price_refund_time：低价订单退款时间
• low_price_refund_amount：低价订单退款金额
• high_price_order_id：高价订单号
• high_price_paid_time：高价订单付款时间
• high_price_paid_amount：高价订单付款金额
• high_price_refund_time：高价订单退款时间
• high_price_refund_amount：高价订单退款金额

with user_orders as(
-- 只有低价订单
select '10001' as "low_price_order_id",'2024-01-01' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",null as "high_price_order_id",null as "high_price_paid_time",0 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union all
-- 只有低价订单，且退款
select '10002' as "low_price_order_id",'2024-01-01' as "low_price_paid_time",1.0 as "low_price_paid_amount",'2024-01-02' as "low_price_refund_time",1.0 as "low_price_refund_amount",null as "high_price_order_id",null as "high_price_paid_time",0 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union all
-- 低价订单+一个高价订单
select '10003' as "low_price_order_id",'2024-01-01' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20001' as "high_price_order_id",'2024-01-01' as "high_price_paid_time",1000 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union all
-- 低价订单+2个高价订单
select '10004' as "low_price_order_id",'2024-01-01' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20002' as "high_price_order_id",'2024-01-02' as "high_price_paid_time",2000 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union allselect '10004' as "low_price_order_id",'2024-01-02' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20004' as "high_price_order_id",'2024-01-05' as "high_price_paid_time",1000 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union all
-- 低价订单+1个30天内高价订单+1个30天外高价订单
select '10005' as "low_price_order_id",'2024-01-02' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20003' as "high_price_order_id",'2024-01-02' as "high_price_paid_time",2000 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union allselect '10005' as "low_price_order_id",'2024-01-02' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20010' as "high_price_order_id",'2024-02-04' as "high_price_paid_time",1500 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union all-- 低价订单+一个高价订单且一个退款
select '10006' as "low_price_order_id",'2024-01-02' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20005' as "high_price_order_id",'2024-01-03' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-01-05' as "high_price_refund_time",2000 as "high_price_refund_amount"
union all
-- 低价订单+一个高价订单且2个退款
select '10007' as "low_price_order_id",'2024-01-02' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20006' as "high_price_order_id",'2024-01-03' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-01-03' as "high_price_refund_time",1000 as "high_price_refund_amount"
union allselect '10007' as "low_price_order_id",'2024-01-02' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20006' as "high_price_order_id",'2024-01-03' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-01-04' as "high_price_refund_time",1000 as "high_price_refund_amount"
union all
-- 低价订单+一个高价订单且1个30天内退款1个30天外退款
select '10008' as "low_price_order_id",'2024-01-03' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20007' as "high_price_order_id",'2024-01-03' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-01-05' as "high_price_refund_time",100 as "high_price_refund_amount"
union allselect '10008' as "low_price_order_id",'2024-01-03' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20007' as "high_price_order_id",'2024-01-03' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-02-05' as "high_price_refund_time",1900 as "high_price_refund_amount"
union all
-- 低价订单+1个30天内高价订单—+1个30天外高价订单且该单有30天内退款和30天外退款
select '10009' as "low_price_order_id",'2024-01-03' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20008' as "high_price_order_id",'2024-01-03' as "high_price_paid_time",1500 as "high_price_paid_amount",null as "high_price_refund_time",0 as "high_price_refund_amount"
union allselect '10009' as "low_price_order_id",'2024-01-03' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20009' as "high_price_order_id",'2024-02-04' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-02-05' as "high_price_refund_time",100 as "high_price_refund_amount"
union allselect '10009' as "low_price_order_id",'2024-01-03' as "low_price_paid_time",1.0 as "low_price_paid_amount",null as "low_price_refund_time",0 as "low_price_refund_amount",'20009' as "high_price_order_id",'2024-02-04' as "high_price_paid_time",2000 as "high_price_paid_amount",'2024-03-06' as "high_price_refund_time",1900 as "high_price_refund_amount"
)
select *
from user_orders;

数据参考如下：

• 部分只有低价订单，可能有退款，退款可能是 30天内，可能是 30天外；
• 部分有高价订单，高价订单可能是 30天内，可能是 30天外；
• 部分有多笔高价订单，每笔可能是 30天内，可能是 30天外；
• 部分高价订单有退款，退款可能是 30天内，可能是 30天外；
• 部分高价订单有多笔退款，每笔退款可能是 30天内，可能是 30天外。

目标表字段记录如下：

• low_price_order_id：低价订单号
• low_price_paid_time：低价订单付款时间
• low_price_paid_amount：低价订单付款金额
• low_price_refund_amount：30天内低价订单退款金额
• high_price_paid_amount：30天内高价订单付款金额
• high_price_refund_amount：30天内高价订单退款金额

接下来开始聚合目标表。
保留低价订单唯一记录，那就直接按照低价订单聚合？
逻辑上没问题，直接聚合试试。

不加日期限制时，结构如下：

select uos.low_price_order_id,uos.low_price_paid_time,uos.low_price_paid_amount,sum(uos.low_price_refund_amount),sum(uos.high_price_paid_amount),sum(uos.high_price_refund_amount)
from user_orders uos
group by uos.low_price_order_id,uos.low_price_paid_time,uos.low_price_paid_amount

按 30 天周期限制，对每个聚合字段进行界限判断：

select uos.low_price_order_id,uos.low_price_paid_time,uos.low_price_paid_amount,sum(case when uos.low_price_refund_time<=date_add(uos.low_price_paid_time,interval 30 day) then uos.low_price_refund_amount else 0 end)     low_price_refund_amount,sum(case when uos.high_price_paid_time<=date_add(uos.low_price_paid_time,interval 30 day) then uos.high_price_paid_amount else 0 end)       high_price_paid_amount,sum(case when uos.high_price_paid_time<=date_add(uos.low_price_paid_time,interval 30 day) and uos.high_price_refund_time<=date_add(uos.high_price_paid_time,interval 30 day) then uos.high_price_refund_amount else 0 end)  high_price_refund_amount
from user_orders uos
group by uos.low_price_order_id,uos.low_price_paid_time,uos.low_price_paid_amount

查看结果，显然不行！当高价订单有多笔退款时，数据发散了，直接聚合时，会出现翻倍的异常。

既然高价订单发散了，为了保证唯一记录，需要分两段聚合，先按低价订单和高价订单聚合退款数据，然后再按低价订单聚合高价订单金额和退款金额，数据流转参考如下。

不加时间限制时，参考如下：

select uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,sum(uos.low_price_refund_amount)  low_price_refund_amount,sum(uos.high_price_paid_amount)   high_price_paid_amount,sum(uos.high_price_refund_amount) high_price_refund_amount
from(select uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,uos.low_price_refund_amount,uos.low_price_refund_time,uos.high_price_order_id,uos.high_price_paid_time,uos.high_price_paid_amount,sum(uos.high_price_refund_amount) high_price_refund_amountfrom user_orders uosgroup by uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,uos.low_price_refund_amount,uos.low_price_refund_time,uos.high_price_order_id,uos.high_price_paid_time,uos.high_price_paid_amount
)uos
group by uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time

接下来加上限制。
先按低价订单和高价订单聚合退款数据，把高价订单的退款时间减去高价订单的付款时间在30天内的退款金额聚合。
然后再按低价订单，把高价订单的付款时间减去低价订单的付款时间在 30天内的高价订单的付款金额和高价订单的退款金额聚合，同时把低价订单的退款时间减去低价订单的付款时间在 30天内的低价订单的付款金额聚合，得到最终的目标表。

select uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,sum(case when uos.low_price_refund_time<=date_add(uos.low_price_paid_time,interval 30 day) then uos.low_price_refund_amount else 0 end)     low_price_refund_amount,sum(case when uos.high_price_paid_time<=date_add(uos.low_price_paid_time,interval 30 day) then uos.high_price_paid_amount else 0 end)       high_price_paid_amount,sum(case when uos.high_price_paid_time<=date_add(uos.low_price_paid_time,interval 30 day) then uos.high_price_refund_amount else 0 end)     high_price_refund_amount
from(select uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,uos.low_price_refund_amount,uos.low_price_refund_time,uos.high_price_order_id,uos.high_price_paid_time,uos.high_price_paid_amount,sum(case when uos.high_price_refund_time<=date_add(uos.high_price_paid_time,interval 30 day) then uos.high_price_refund_amount else 0 end)  high_price_refund_amountfrom user_orders uosgroup by uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,uos.low_price_refund_amount,uos.low_price_refund_time,uos.high_price_order_id,uos.high_price_paid_time,uos.high_price_paid_amount
)uos
group by uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time

结果如下，符合需求的预期。

可视化

说明：实际业务表单还有很多其他的维度，此处仅抽象出时间维度。

有了目标表，便可以根据目标表按时间维度聚合每月、每周、每天的营收金额、退款率等数据指标。
由于案例数据较少，此处做一个按天查看营收金额和退款率的折线图，看看二者的关系走势。

• 营收金额：低价订单付款金额-低价订单退款金额+高价订单付款金额-高价订单付款金额
• 退款率：高价订单退款金额>0 的数量/总低价订单数，一个低价订单可以视为是一个用户，按用户数计算退款率。

select low_price_paid_time,sum(low_price_paid_amount-low_price_refund_amount+high_price_paid_amount-high_price_refund_amount) 营收金额,sum(if(high_price_refund_amount>0,1,0))/count(*) 退款率
from <引流订单产出表>
group by low_price_paid_time
order by low_price_paid_time

小结

本文介绍了怎么实现以引流的低价订单的为基本维度，按照业务 30天的间隔分别聚合低价订单退款、高价订单金额和高价订单退款金额。

采用了两层表单的设计，一层事实表，一层根据业务不同的口径进行聚合。

处理业务逻辑的时候，根据事实表的结构，采用分段聚合的逻辑先聚合【低价订单+高价订单】，然后再根据【低价订单】进行聚合，最终得到目标表。

目标表开发好之后，在该表的基础上，根据各类业务指标进行聚合并可视化，最终提交给业务方使用。

附录

附上 MaxCompute SQL 处理逻辑，此案例，将 date_add()语法修改即可。

select uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,sum(case when uos.low_price_refund_time<=date_add(uos.low_price_paid_time,30) then uos.low_price_refund_amount else 0 end)     low_price_refund_amount,sum(case when uos.high_price_paid_time<=date_add(uos.low_price_paid_time,30) then uos.high_price_paid_amount else 0 end)       high_price_paid_amount,sum(case when uos.high_price_paid_time<=date_add(uos.low_price_paid_time,30) then uos.high_price_refund_amount else 0 end)     high_price_refund_amount
from(select uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,uos.low_price_refund_amount,uos.low_price_refund_time,uos.high_price_order_id,uos.high_price_paid_time,uos.high_price_paid_amount,sum(case when uos.high_price_refund_time<=date_add(uos.high_price_paid_time,30) then uos.high_price_refund_amount else 0 end)  high_price_refund_amountfrom user_orders uosgroup by uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time,uos.low_price_refund_amount,uos.low_price_refund_time,uos.high_price_order_id,uos.high_price_paid_time,uos.high_price_paid_amount
)uos
group by uos.low_price_order_id,uos.low_price_paid_amount,uos.low_price_paid_time