I have a calculated member which represents an active customer. That would be the following;
WITH MEMBER [Measures].[Active Customers] AS
Count ( nonempty( Filter (
( [Customer].[Customer Key].Members, [Measures].[Turnover] ),
[Measures].[Turnover] > 0
) ) )
This works great, when I want to get active customers in the current period and previous ones, as I get my time dimension, and use the CurrentMember, CurrentMember.PrevMember and CurrentMember with the Lag function in order to get customers who were active in previous periods.
My problem is when I want to get the count of customers, who are common in different members. Say I want to get customers who are active in the current period, and NOT in the previous period. Or another case, active in current, and active in previous. Because of this, I would need to use the INTERSECT function, and my customer dimension has 4 million records. This is already a subset of 9 million records.
So when checking for a customer who is active in 2 consecutive periods, I do this (The Active Previous Period, and Active Current Period is basically the calculated member above, however with CurrentMember and CurrentMember.PrevMember) :
set [Previous Active Customers Set] AS
Filter (
( [Customer].[Customer Key].Members, [Measures].[Active Previous Period] ),
[Measures].[Active Previous Period] > 0
)
set [Current Active Customers Set] AS
Filter (
( [Customer].[Customer Key].Members, [Measures].[Active Current Period] ),
[Measures].[Active Current Period] > 0
)
member [Measures].[Active 2 consecutive periods] as
count(INTERSECT([Current Active Customers Set],[Previous Active Customers Set]) )
This takes forever. Is there anyway to improve, or go around this performance problem of using the INTERSECT with large sets? Or maybe optimizations on the MDX query? I tried always using a subset of my customers dimension, but this only reduced the number of records to less than 4 million - so it's still large. Any help would be appreciated!