Join unique UserIDs with previous month' first time UserIDs - firebase

Question

I want to identify users that had a "first_open" event in month a (here: january) and came back to our in month b (here: february) with an "user_engagement" event.
My idea:
1. Create a table with all users who had a "first_open" event
2. Create a table with all users who had a "user_engagement" event
3. Join both tables on userID
4. Count Users who both had a "first_open" event in month a and month b and count all users from january with the "first_open" event
With the following query I am currently overcounting both the users in month a and b, because I am not counting all unqiue users for both event types.
With
users_first_open as (select
user_pseudo_id,
EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) AS install_month,
event_name as firstopen
FROM
`table.events_*`
where _TABLE_SUFFIX BETWEEN '20190101'
AND '20190108' and event_name = "first_open" and
EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) = 1
),
user_enagement_next_month as (select
user_pseudo_id,
EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) AS engagement_month,
event_name as engagament_next_month
FROM
`table.events_*`
where _TABLE_SUFFIX BETWEEN '20190109'
AND '20190116' and event_name = "user_engagement"
and EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) = 1),
cohort_raw as(
select
user_pseudo_id,
install_month,
engagement_month,
case when firstopen = "first_open" then 1 else 0 end as cohort_count_first_open,
case when engagament_next_month = "user_engagement" then 1 else 0 end as cohort_count_engagement
from
user_enagement_next_month
full join
users_first_open using (user_pseudo_id))--,
select
sum(case when cohort_count_first_open is not null then 1 else 0 end) as users_first_open,
(select sum(case when cohort_count_engagement is not null then 1 else 0 end) as u_engagement_open from cohort_raw where cohort_count_first_open = 1) as users_engagement_open
from cohort_raw
What I tried next was the following: group in table 2 "user_enagement_next_month" by userID, etc.
and create a sum of "first_open" case when and "engagement" case when results. With the later I then included the query to only count users whose count of these two was = 2
-
With
users_first_open as (select
user_pseudo_id,
EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) AS install_month,
event_name as firstopen
FROM
`table.events_*`
where _TABLE_SUFFIX BETWEEN '20190101'
AND '20190131' and event_name = "first_open" and
EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) = 1
),
user_enagement_next_month as (select
user_pseudo_id,
EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) AS engagement_month,
event_name as engagament_next_month
FROM
`table.events_*`
where _TABLE_SUFFIX BETWEEN '20190201'
AND '20190228' and event_name = "session_start"
and EXTRACT (Month FROM(DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)))) = 2
group by 1,2,3)--,
--cohort_raw as(
select
user_pseudo_id,
install_month,
engagement_month,
case when firstopen = "first_open" then 1 else 0 end as cohort_count_first_open,
case when engagament_next_month = "session_start" then 1 else 0 end as cohort_count_engagement
--case when user_pseudo_id is not null then 1 else 0 end as cohort_count_engagement
from
user_enagement_next_month
full join
users_first_open using (user_pseudo_id)),
cohort_agg as (
select *, cohort_count_first_open+cohort_count_engagement as cohort_sum
from cohort_raw
group by 1,2,3,4,5
order by 6 desc)
select
(select count(*) from users_first_open) as cohort_jan,
(select Sum(cohort_sum) from cohort_agg where cohort_sum = 2) as ret,
sum(case when cohort_count_first_open is not null then 1 else 0 end) as users_first_open,
(select sum(case when cohort_count_engagement is not null then 1 else 0 end) as u_engagement_open from cohort_raw where cohort_count_first_open = 1) as users_engagement_open
from cohort_agg
I expect a return rate of around 20%. My output at the moment is 54%, because in my query I am either overcounting or counting to little, because I assume my join does not work.

Answer 1

Maybe I don't clearly understand what you want, but try this one
with
users_first_open as (
select distinct -- is there duplicates for one user_id?
user_pseudo_id,
extract(
month from
timestamp_micros(user_first_touch_timestamp)
) as install_month
from
`table.events_201901*` -- longer prefixes generally perform better
where
_table_suffix between '01' and '31'
and event_name = 'first_open'
and extract(
month from
timestamp_micros(user_first_touch_timestamp)
) = 1
),
user_enagement_next_month as (
select distinct
user_pseudo_id,
extract(
month from
timestamp_micros(user_first_touch_timestamp)
) as engagement_month
from
`table.events_201902*` -- longer prefixes generally perform better
where
_table_suffix between '01' and '28'
and event_name = 'user_engagement'
and extract(
month from
timestamp_micros(user_first_touch_timestamp)
) = 2
)
select
ufo.install_month,
uenm.engagement_month,
count(*) as first_open_event_users_cnt,
count(uenm.user_pseudo_id) as user_engagement_event_users_cnt
from
users_first_open as ufo
left join user_enagement_next_month as uenm
on ufo.user_pseudo_id = uenm.user_pseudo_id
group by
1, 2