BigQuery for running count of distinct values with a dynamic date-range - sql

Question

We are trying to make a query where we get the sum of unique customers on a specific year-month + the sum of unique customers on the 364 days before the specific date.
For example:
Our customer-table looks like this:
| order_date | customer_unique_id |
| -------- | -------------- |
| 2020-01-01 | tom#email.com |
| 2020-01-01 | daisy#email.com |
| 2019-05-02 | tom#email.com |
In this example we have two customers who ordered on 2020-01-01 and one of them already ordered within the 364-days timeframe.
The desired table should look like this:
| year_month | unique_customers |
| -------- | -------------- |
| 2020-01 | 2 |
We tried multiple solutions, such as partitioning and windows, but nothing seem to work correctly. The tricky part is the uniqueness. We want the look 364 days back but want to do a count distinct on the customers based on that whole period and not based on date/year/month because then we would get duplicates. For example, if you partition by date, year or month tom#email.com would be counted twice instead of once.
The goal of this query is to get insight into the order-frequency (orders divided by customers) over a time period from 12 months.
We work with Google BigQuery.
Hope someone can help us out! :)

Answer 1

Here is a way to achieve your desired results. Note that this query does year-month join in a separate query, and joins it with the rolling 364-day-interval query.
with year_month_distincts as (
select
concat(
cast(extract(year from order_date) as string),
'-',
cast(extract(month from order_date) as string)
) as year_month,
count(distinct customer_id) as ym_distincts
from customer_table
group by 1
)
select x.order_date, x.ytd_distincts, y.ym_distincts from (
select
a. order_date,
(select
count(distinct customer_id)
from customer_table b
where b.order_date between date_sub(a.order_date, interval 364 day) and a.order_date
) as ytd_distincts
from orders a
group by 1
) x
join year_month_distincts y on concat(
cast(extract(year from x.order_date) as string),
'-',
cast(extract(month from x.order_date) as string)
) = y.year_month

Answer 2

Two options using arrays that may help.
Look back 364 days as requested
In case you wish to look back 11 months (given reporting is monthly)
month_array AS (
SELECT
DATE_TRUNC(order_date,month) AS order_month,
STRING_AGG(DISTINCT customer_unique_id) AS cust_mth
FROM customer_table
GROUP BY 1
),
year_array AS (
SELECT
order_month,
STRING_AGG(cust_mth) OVER(ORDER by UNIX_DATE(order_month) RANGE BETWEEN 364 PRECEDING AND CURRENT ROW) cust_12m
-- (option 2) STRING_AGG(cust_mth) OVER (ORDER by cast(format_date('%Y%m', order_month) as int64) RANGE BETWEEN 99 PRECEDING AND CURRENT ROW) AS cust_12m
FROM month_array
)
SELECT format_date('%Y-%m',order_month) year_month,
(SELECT COUNT(DISTINCT cust_unique_id) FROM UNNEST(SPLIT(cust_12m)) AS cust_unique_id) as unique_12m
FROM year_array