Attribute sales to email campaigns based on time window - sql

Question

Hopefully someone is able to help me with the following. I have two tables, one containing events which are "clicked emails" and one containing "conversions" (=orders). I would like to write attribution logic in BigQuery SQL to match the conversions to the clicked emails based on the following logic:
the matching ID is a "person_id"
conversions that happened within a 5 day (120h) timeframe after the clicked email count to that mail
if the customer clicked on two different emails within a five day window, the email clicked most recent before the conversion gets attributed the revenue
To know: the clicks table also contains clicks which do not have a conversion, and the conversions table also contains conversions not related to emails.
Wished end result: a table containing a count of all attributed orders and a count of all clicks, on date and campaign name.
I figured I would need to do a left join getting only the conversions in that might be related to a click, on person_id. However, now I need to define the window up until which conversions are counted (the 5 days). Maybe I could include this in the where statement? Then after that, I need to check, in case the count of conversions is > 1, to only take the conversion into account where the "date diff" is smallest.
How far I got :
SELECT
c. person_id,
c.campaign_name,
c.datetime,
s.processed_at,
c.email,
s.order_number,
SUM(s.total_price) AS revenue,
COUNT(DISTINCT s.order_number) AS orders
FROM
`klaviyo_de_clicks` c
LEFT JOIN
`klaviyo_de_conversions` s
ON
c.person_id = s.person_id
GROUP BY
1,2,3,4,5,6
Thanks for your help!

Answer 1

You can get the emails just before the conversion using union all and window functions.
The idea is to put all the data into a single "result set" with the clicks and conversions separated. Then use window functions to get the previous click information for each conversion.
Your sample code as a bunch of columns not alluded to in your question. But this is the structure of the code:
with cc as (
select person_id, date_time, conversion_id, null as click_id
from conversions c
union all
select person_id, date_time, null, click_id
from clicks c
)
select cc.*,
(click_date_time >= datetime_add(datetime, interval -5 day)) as is_click_conversion
from (select cc.*,
max(case when click_id is not null then date_time end) over (partition by person_id order by date_time) as click_date_time,
last_value(click_id ignore nulls) over (partition by person_id order by date_time) as prev_click_id
from cc
) cc
where conversion_id is not null;
If you need additional columns, you can use join to bring them in.