Looking to do some cohort analysis on a userbase. We have 2 tables "users" and "sessions", where users and sessions both have a "created_at" field. I'm looking to formulate a query that yields a 7 by 7 table of numbers (with some blanks) that shows me: a count of users who were created on a particular day who also have a session created y = (0..6 days ago), indicating that he returned on that day.
created_at d2 d3 d4
today * * *
today-1 49 * *
today-2 45 30 *
today-3 47 48 18
...
In this case, 47 users who were created on today-3 returned on today-2.
Can I perform this in a single MySQL query? I can perform the queries individually like so, but it'd be really nice to have it all in one query.
SELECT `users`.* FROM `users` INNER JOIN `sessions` ON `sessions`.`user_id` = `users`.`id` WHERE `users`.`os` = 'ios' AND (`sessions`.`updated_at` BETWEEN '2013-01-16 08:00:00' AND '2013-01-17 08:00:00')
This seems a complex problem. Regardless of whether it also seems to you a difficult one or not, it is never a bad idea to start working it up from a smaller problem.
You could start, for instance, with a query returning all the users (just the users) that have been registered within the last week, i.e. starting from the day six days from now, as per your requirement:
SELECT *
FROM users
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
The next step could be grouping the results by dates and counting rows in every group:
SELECT
created_at,
COUNT(*) AS user_count
FROM users
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
GROUP BY created_at
If created_at
is a datetime
or timestamp
, use DATE(created_at)
as the grouping criterion:
SELECT
DATE(created_at) AS created_at,
COUNT(*) AS user_count
FROM users
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
GROUP BY DATE(created_at)
However, you don't seem to want absolute dates in the output, but only relative ones, like today
, today - 1 day
etc. In that case, you could use the DATEDIFF()
function, which returns the number of days between two dates, to produce (numeric) offsets from today and group by those values:
SELECT
DATEDIFF(CURDATE(), created_at) AS created_at,
COUNT(*) AS user_count
FROM users
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
GROUP BY DATE(created_at)
Your created_at
column would contain "dates" like 0
, 1
and so on till 6
. Converting them into today
, today-1
etc. is trivial and you will see that in the final query. So far, however, we've reached the point at which we need to take one step back (or, perhaps, it's rather a half step to the right), because we don't really need to count the users but rather their returns. So, the actual working dataset from users
that is needed at the moment will be this:
SELECT
id,
DATEDIFF(CURDATE(), created_at) AS day_offset
FROM users
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
We need user IDs to join this rowset to (the one that will be derived from) sessions
and we need day_offset
as the grouping criterion.
Moving on, a similar transformation will need to be performed on the sessions
table, and I won't go into details on that. Suffice it to say that the resulting query will be very identical to the last one, with just two exception:
id
gets replaced with user_id
;
DISTINCT is applied to the entire subset.
The reason for DISTINCT is to return no more than one row per user & day: it is my understanding that however many sessions a user might have on a particular day, you want to count them as one return. So, here's what gets derived from sessions
:
SELECT DISTINCT
user_id,
DATEDIFF(CURDATE(), created_at) AS day_offset
FROM sessions
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
Now it only remains to join the two derived tables, apply grouping and use conditional aggregation to get the required results:
SELECT
CONCAT('today', IFNULL(CONCAT('-', NULLIF(u.DayOffset, 0)), '')) AS created_at,
SUM(s.DayOffset = 0) AS d0,
SUM(s.DayOffset = 1) AS d1,
SUM(s.DayOffset = 2) AS d2,
SUM(s.DayOffset = 3) AS d3,
SUM(s.DayOffset = 4) AS d4,
SUM(s.DayOffset = 5) AS d5,
SUM(s.DayOffset = 6) AS d6
FROM (
SELECT
id,
DATEDIFF(CURDATE(), created_at) AS DayOffset
FROM users
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
) u
LEFT JOIN (
SELECT DISTINCT
user_id,
DATEDIFF(CURDATE(), created_at) AS DayOffset
FROM sessions
WHERE created_at >= CURDATE() - INTERVAL 6 DAY
) s
ON u.id = s.user_id
GROUP BY u.DayOffset
;
I must admit that I haven't tested/debugged this, but, if this be needed, I'll be happy to work with the data samples you will have provided, once you have provided them. :)
Example Of Month Wise Cohort:
First Let's Create Table Individual User Activity Flow (MONTH WISE):
SELECT
mu.created_timestamp AS cohort
, mu.id AS user_id
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 1 AND l.user_id = mu.id) AS m1
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 2 AND l.user_id = mu.id) AS m2
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 3 AND l.user_id = mu.id) AS m3
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 4 AND l.user_id = mu.id) AS m4
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 5 AND l.user_id = mu.id) AS m5
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 6 AND l.user_id = mu.id) AS m6
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 7 AND l.user_id = mu.id) AS m7
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 8 AND l.user_id = mu.id) AS m8
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 9 AND l.user_id = mu.id) AS m9
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 10 AND l.user_id = mu.id) AS m10
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 11 AND l.user_id = mu.id) AS m11
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 12 AND l.user_id = mu.id) AS m12
FROM user mu
WHERE mu.created_timestamp BETWEEN '2018-01-01 00:00:00' AND '2019-12-31 23:59:59'
Then After This Table Calculate the individual activity-sum of the user:
SELECT MONTH(c.cohort) AS cohort
,COUNT(c.user_id) AS signups
,SUM(c.m1) AS m1
,SUM(c.m2) AS m2
,SUM(c.m3) AS m3
,SUM(c.m4) AS m4
,SUM(c.m5) AS m5
,SUM(c.m6) AS m6
,SUM(c.m7) AS m7
,SUM(c.m8) AS m8
,SUM(c.m9) AS m9
,SUM(c.m10) AS m10
,SUM(c.m11) AS m11
,SUM(c.m12) AS m12
FROM (SELECT
mu.created_timestamp AS cohort
, mu.id AS user_id
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 1 AND l.user_id = mu.id) AS m1
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 2 AND l.user_id = mu.id) AS m2
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 3 AND l.user_id = mu.id) AS m3
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 4 AND l.user_id = mu.id) AS m4
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 5 AND l.user_id = mu.id) AS m5
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 6 AND l.user_id = mu.id) AS m6
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 7 AND l.user_id = mu.id) AS m7
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 8 AND l.user_id = mu.id) AS m8
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 9 AND l.user_id = mu.id) AS m9
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 10 AND l.user_id = mu.id) AS m10
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 11 AND l.user_id = mu.id) AS m11
,(SELECT IF(COUNT(l.order_date) = 0 , 0, 1) FROM order l WHERE MONTH(l.order_date) = 12 AND l.user_id = mu.id) AS m12
FROM user mu
WHERE mu.created_timestamp BETWEEN '2018-01-01 00:00:00' AND '2019-12-31 23:59:59') AS c GROUP BY MONTH(cohort)
In replacement of months you can use days, other wise cohort analysis mostly use in month cases
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With