Google Analytics: Conversion rate in time intervals - google-analytics

Question

I've got Google Analytics running on a website and am now trying to determine the conversion rate in certain time intervals. I therefore have a table that contains
interval_id
i.interval_start_time_utc
i.interval_stop_time_utc
Sadly, the following BigQuery query that would assign each order to an interval will not work:
SELECT
totals.transactions,
totals.visits,
i.interval_id
FROM [123456.ga_sessions_20160609]
INNER JOIN intervals i ON i.interval_start_time_utc < visitStartTime AND visitStartTime < i.interval_end_time_utc
This throws the error
ON clause must be AND of = comparisons of one field name from each table [...]
so I gather that BigQuery simply doesn't do range joins. Is there another way to do this short of doing a full join and then paring down? Are there entirely different, better approaches for this sort of thing?

Answer 1

BigQuery Standard SQL doesn't have this limitation - see Enabling Standard SQL
If yo want to make with BigQuery Legacy SQL - try something like below
SELECT
totals.transactions,
totals.visits,
i.interval_id
FROM [123456.ga_sessions_20160609]
CROSS JOIN intervals i
WHERE i.interval_start_time_utc < visitStartTime
AND visitStartTime < i.interval_end_time_utc

Answer 2

For the sake of presenting idea - let’s simplify example
And let’s remember - we do want to make it with BigQuery Legacy SQL - not with Standard SqL where it is trivial!
Challenge
Assume we have visits table:
SELECT visit_time FROM
(SELECT 2 AS visit_time),
(SELECT 12 AS visit_time),
(SELECT 22 AS visit_time),
(SELECT 32 AS visit_time)
and intervals table:
SELECT before, after, event FROM
(SELECT 1 AS before, 5 AS after, 3 AS event),
(SELECT 6 AS before, 10 AS after, 8 AS event),
(SELECT 21 AS before, 25 AS after, 23 AS event),
(SELECT 33 AS before, 37 AS after, 35 AS event)
We want to extract all visits which are within event’s before and after values
This can be simply done with use of CROSS JOIN like below:
SELECT
visit_time, event, before, after
FROM (
SELECT visit_time FROM
(SELECT 2 AS visit_time),
(SELECT 12 AS visit_time),
(SELECT 22 AS visit_time),
(SELECT 32 AS visit_time),
) AS visits
CROSS JOIN (
SELECT before, after, event FROM
(SELECT 1 AS before, 5 AS after, 3 AS event),
(SELECT 6 AS before, 10 AS after, 8 AS event),
(SELECT 21 AS before, 25 AS after, 23 AS event),
(SELECT 33 AS before, 37 AS after, 35 AS event)
) AS intervals
WHERE visit_time BETWEEN before AND after
With result as:
visit_time event before after
2 3 1 5
22 23 21 25
Potential Issue
When both tables are big enough – this cross join becomes quite expensive!
Hint
It happened that (from user’s comments) - The intervals are always the x units to the left and right of event.
Solution
Below is proposed solution/option that uses hint/fact and makes use of JOIN instead of CROSS JOIN between two big tables
The key here is to generate (on fly) new table that will hold all possible interval’s values based on event and x
SELECT event, event + delta AS point
FROM (
SELECT event FROM
(SELECT 1 AS before, 5 AS after, 3 AS event),
(SELECT 6 AS before, 10 AS after, 8 AS event),
(SELECT 21 AS before, 25 AS after, 23 AS event),
(SELECT 33 AS before, 37 AS after, 35 AS event)
) AS events
CROSS JOIN (
SELECT pos - 1 - 2 AS delta FROM (
SELECT ROW_NUMBER() OVER() AS pos, * FROM (FLATTEN((
SELECT SPLIT(RPAD('', 1 + 2 * 2, '.'),'') AS h FROM (SELECT NULL)),h
)))
) AS deltas
In above code x = 2 – but you can change it in two places, for example if x = 5 you should have
SELECT pos - 1 - 5 AS delta FROM (
SELECT ROW_NUMBER() OVER() AS pos, * FROM (FLATTEN((
SELECT SPLIT(RPAD('', 1 + 2 * 5, '.'),'') AS h FROM (SELECT NULL)),h
)))
CROSS JOIN in above code is inexpensive because deltas table is quite small
So, finally now, you can have your result with below:
SELECT
visit_time, event
FROM (
SELECT visit_time FROM
(SELECT 2 AS visit_time),
(SELECT 12 AS visit_time),
(SELECT 22 AS visit_time),
(SELECT 32 AS visit_time),
) AS visits
JOIN (
SELECT event, event + delta AS point
FROM (
SELECT event FROM
(SELECT 1 AS before, 5 AS after, 3 AS event),
(SELECT 6 AS before, 10 AS after, 8 AS event),
(SELECT 21 AS before, 25 AS after, 23 AS event),
(SELECT 33 AS before, 37 AS after, 35 AS event)
) AS events
CROSS JOIN (
SELECT pos - 1 - 2 AS delta FROM (
SELECT ROW_NUMBER() OVER() AS pos, * FROM (FLATTEN((
SELECT SPLIT(RPAD('', 1 + 2 * 2, '.'),'') AS h FROM (SELECT NULL)),h
)))
) AS deltas
) AS points
ON points.point = visits.visit_time
With expected result
visit_time event
2 3
22 23
I think above approach can work for you – but you sure need to adopt it to your particular case
I think this can be done relatively easy if you will round all your involved times up to respective minutes
Hope this will help
Share result with us if you will get this work :o)