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Hi All,
I want to calculate win rate from two tables - 1) Sales table and 2) Pipeline table.
The win rate calculation formula= Sales amount / Original Pipeline.
In each table I have Dynamic measure to classify whether an account is large deal or small deal - the classification will change based on the product selected.
Pipeline Table
| Account | Dynamic Pipe NNACV | Dynamic Pipe SD/LD |
| City | 180,363 | Small deal |
| Coia | 3,017,522 | Large Deal |
| iref | 1,960,052 | Large Deal |
Sales Table
| Account | Dynamic Sales NNACV | Dynamic Sales SD/LD |
| Coia | 1,100,316 | Large Deal |
| iref | 1,138,494 | Large Deal |
| City | 103,765 | Small deal |
Now the question is how to calculate large deal/ small deal win rate in DAX measure?
In above example,
Large deal win rate should be= (1.1m+1.13m)/ (3.01m+1.96m)=~44.9%
Run Rate win rate should be=103k/180k=~57%
The win rate should also change when product filter applied.
Here is sample file. It might be easier to see sample data. Thanks for your advice in advance.
https://drive.google.com/file/d/1kO4TZokzEp37pvyHGoJEk7Y79SbeFT3t/view?usp=sharing
Irene
Solved! Go to Solution.
@irenelitw629 , You need to have common dimensions like Account, Dynamic Pipe SD/LD, and date and analyze data with those
Power BI- DAX: When I asked you to create common tables: https://youtu.be/a2CrqCA9geM
https://medium.com/@amitchandak/power-bi-when-i-asked-you-to-create-common-tables-a-quick-dax-soluti...
Power BI- Power Query: When I asked you to create common tables: https://youtu.be/PqfGW6pl1Sw
Thank you Amit! You are the best!
@irenelitw629 , You need to have common dimensions like Account, Dynamic Pipe SD/LD, and date and analyze data with those
Power BI- DAX: When I asked you to create common tables: https://youtu.be/a2CrqCA9geM
https://medium.com/@amitchandak/power-bi-when-i-asked-you-to-create-common-tables-a-quick-dax-soluti...
Power BI- Power Query: When I asked you to create common tables: https://youtu.be/PqfGW6pl1Sw
Share feedback directly with Fabric product managers, participate in targeted research studies and influence the Fabric roadmap.
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