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Consider the following table:
Customer ID | Wave | Use Profile |
101 | 1 | Light |
101 | 2 | Light |
101 | 3 | Medium |
102 | 1 | Heavy |
102 | 2 | Heavy |
102 | 3 | Heavy |
103 | 1 | Medium |
103 | 2 | Light |
103 | 3 | Medium |
104 | 1 | Light |
104 | 2 | Medium |
104 | 3 | Heavy |
I would like to create a chart to look at how the [Use Profile] changes for customers over time.
For example: I would like to be able look at just the customers who were a "Light" user for Wave 1 to see what their distribution looks like over subsequent waves. The desired expression should be able to produce something similar to the below pivot table for the sample data above :
Wave | Use Profile | Customer Count |
1 | Light | 2 |
Medium | 0 | |
Heavy | 0 | |
2 | Light | 1 |
Medium | 1 | |
Heavy | 0 | |
3 | Light | 0 |
Medium | 1 | |
Heavy | 1 |
I have a few ideas of how to implement this in script, but I would prefer to use set analysis for this if possible. Any Ideas?
Thanks,
Clark
Maybe create a chart with dimension Wave and Use Profile, and as expression
=count({<[Customer ID] = p(), [Use Profile]= >}DISTINCT [Customer ID])
then create a listbox for User Profile and select e.g. 'Light'.
Maybe create a chart with dimension Wave and Use Profile, and as expression
=count({<[Customer ID] = p(), [Use Profile]= >}DISTINCT [Customer ID])
then create a listbox for User Profile and select e.g. 'Light'.
Thanks! Had to tweak it a bit, but using p() did the trick.
I had never used that expression before - great to know about it.
Thanks,
Clark