SQL Retention Rates - sql

Question

I am trying to construct a rolling retention measure but am having troubles figuring how to do it in redshift.
I have defined retention as the intersection between two sets of users. The first a cohort of distinct user ids who 90 days from todays date had been active at least once 30 days from that date (between 90 and 120 days from today). The second is the number of those users who were active in the last 30 days from today.
Retention = Todays 30 day active users who were in original cohort 90 days ago / 30 day active suers 90 days ago
My sessions table looks like this:
id
created_date
1
2021-03-04
1
2021-01-01
1
2020-12-15
2
2021-02-17
The only way I can seem to do this is as follows:
Create a temple table and insert into for todays date.
with t1 as (
select distinct customer_id id
from sessions
and created_date >= dateadd('day', -29, current_date)
)
, t2 as (
select distinct customer_id id
from sessions
and created_date <= dateadd('day', -89, current_date)
and created_date >= dateadd('day', -119, current_date)
)
select current_date,
count(t1.id) as original,
count(t2.id) as current,
round(cast(count(t2.id) as float) / cast(count(t1.id) as float), 2) as ratio
into temp table temp1
from t1
left join t2
on t1.id = t2.id
Run an insert statement into the temp table multiple times subtracting one day from current date in each query
insert into temp1
with t1 as (
select distinct customer_id id
from sessions
and created_date >= dateadd('day', -29, current_date-1)
)
, t2 as (
select distinct customer_id id
from sessions
and created_date <= dateadd('day', -89, current_date-1)
and created_date >= dateadd('day', -119, current_date-1)
)
select current_date-1,
count(t1.id) as original,
count(t2.id) as current,
round(cast(count(t2.id) as float) / cast(count(t1.id) as float), 2) as ratio
from t1
left join t2
on t1.id = t2.id
Obtain this table with a daily retention rate for all days so far in 2021
The column original is the user cohort of 30 day active users 90 days ago from the reference date.
The current column is the number of users from the cohort in the original column that are 30 day active users at the reference date.
Step 1 returns only the first row 2021-03-05 and step 2 gives me the other row.
date
original
current
ratio
2021-03-05
100
70
0.7
2021-03-04
100
60
0.6
This process obviously is very inefficient and I am trying to figure out whether there is faster, easier way to do it? The issue is I need to compare a distinct user cohort from 3 months ago and then see today how many of those users from the cohort are still active.
All hep will be greatly appreciated!

Answer 1

If you want to get the number of users for 30 days today and 90 days ago for each date, the query is:
with t1 as (
select
s2.created_date,
count(distinct customer_id id) as cnt30
from sessions s1 inner join
(select distinct created_date from sessions) s2
on dateadd('day', -29, s2.created_date)<=s1.created_date
and s1.created_date<=s2.created_date
group by s2.created_date
)
select a1.current_date,
a1.cnt30 as original,
a2.cnt32 as current,
round(cast(a2.cnt30) as float) / cast(count(a1.cnt30) as float), 2) as ratio
from t1 as a1 inner join t1 as a2
on dateadd('day', -89, a1.created_date)=a2.created_date
order by 1
Using the subquery in the select-list, the query is:
with t1 as (
select
s2.created_date,
(select count(distinct s1.customer_id) from sessions s1
where dateadd('day', -29, s2.created_date)<=s1.created_date
and s1.created_date<=s2.created_date) as cnt30
from
(select distinct created_date from sessions) s2
)
select a1.current_date,
a1.cnt30 as original,
a2.cnt32 as current,
round(cast(a2.cnt30) as float) / cast(count(a1.cnt30) as float), 2) as ratio
from t1 as a1 inner join t1 as a2
on dateadd('day', -89, a1.created_date)=a2.created_date
order by 1
First, use joins and subqueries to calculate the number of unique IDs for the last 30 days on each date.
Next, join the same tables and output the number of unique IDs on the current day and 90 days ago.
Note taht I've never used redshift, so I'll write this based on your query and common SQL syntax. I hope my answer helps you.