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I have the following 2 tables:
| Table 1 | |||
| Date | Brand | Segment | Daily Click User |
| 1/1/2020 | A | Segment1 | 100 |
| 1/1/2020 | A | Segment2 | 200 |
| 1/1/2020 | A | Segment3 | 454 |
| 1/1/2020 | A | Segment4 | 534 |
| 1/2/2020 | A | Segment1 | 546 |
| 1/2/2020 | A | Segment2 | 675 |
| 1/2/2020 | A | Segment3 | 876 |
| 1/2/2020 | A | Segment4 | 876 |
| 1/1/2020 | B | Segment1 | 987 |
| 1/1/2020 | B | Segment2 | 54345 |
| 1/1/2020 | B | Segment3 | 45425 |
| 1/1/2020 | B | Segment4 | 43566 |
| 1/2/2020 | B | Segment1 | 4786545 |
| 1/2/2020 | B | Segment2 | 453543 |
| 1/2/2020 | B | Segment3 | 6545645 |
| 1/2/2020 | B | Segment4 | 654654 |
| Table 2 | ||
| Date | Brand | Total Daily Brand Population |
| 1/1/2020 | A | 10000000 |
| 1/2/2020 | A | 20000000 |
| 1/1/2020 | B | 3000000000 |
| 1/2/2020 | B | 3100000000 |
The target metric calculation logic is in below:
| Date | Brand | Segment | Daily Click User | Brand Population | Brand Penetration% |
| 1/1/2020 | A | Segment1 | 100 | 10000000 | ? |
| 1/1/2020 | A | Segment2 | 200 | 10000000 | ? |
| 1/1/2020 | A | Segment3 | 454 | 10000000 | ? |
| 1/1/2020 | A | Segment4 | 534 | 10000000 | ? |
| 1/2/2020 | A | Segment1 | 546 | 20000000 | ? |
| 1/2/2020 | A | Segment2 | 675 | 20000000 | ? |
| 1/2/2020 | A | Segment3 | 876 | 20000000 | ? |
| 1/2/2020 | A | Segment4 | 876 | 20000000 | ? |
| 1/1/2020 | B | Segment1 | 987 | 3000000000 | ? |
| 1/1/2020 | B | Segment2 | 54345 | 3000000000 | ? |
| 1/1/2020 | B | Segment3 | 45425 | 3000000000 | ? |
| 1/1/2020 | B | Segment4 | 43566 | 3000000000 | ? |
| 1/2/2020 | B | Segment1 | 4786545 | 3100000000 | ? |
| 1/2/2020 | B | Segment2 | 453543 | 3100000000 | ? |
| 1/2/2020 | B | Segment3 | 6545645 | 3100000000 | ? |
| 1/2/2020 | B | Segment4 | 654654 | 3100000000 | ? |
The Brand Penetration Rate is calculated by Clicker Users/Total Brand Population.
It is tempting to use the dax below:
Total Click = SUM(table1[Daily Click User]) Total Brand = SUM(table2[Total Daily Brand Population]) Brand Penetration = [Total Click]/[Total Brand]\
However, I am worried that it will result in duplicated values when aggregating the daily click user by segments, the total brand population will also be duplicated.
My hope is to flexibly know the Brand Penetration Rate by segments slicer or overall.
So finally, we can see the trend visualization by either any segments or All.
Thank you.
Solved! Go to Solution.
Hi, @zakkyang ;
You could merge two tables in power query first ,and then create a measure.
1.merge two tables by two columns.
2.create a measure.
Brand Penetration =
VAR _sumclick =
CALCULATE (
SUM ( [Daily Click User] ),
ALLEXCEPT ( Table1, Table1[Brand], Table1[Date] ))
RETURN
DIVIDE(MAX ( [Table2.Total Daily Brand Population] ) ,_sumclick)
The final output is shown below:
Best Regards,
Community Support Team_ Yalan Wu
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi, @zakkyang ;
You could merge two tables in power query first ,and then create a measure.
1.merge two tables by two columns.
2.create a measure.
Brand Penetration =
VAR _sumclick =
CALCULATE (
SUM ( [Daily Click User] ),
ALLEXCEPT ( Table1, Table1[Brand], Table1[Date] ))
RETURN
DIVIDE(MAX ( [Table2.Total Daily Brand Population] ) ,_sumclick)
The final output is shown below:
Best Regards,
Community Support Team_ Yalan Wu
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
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